modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-02 18:52:31
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 533
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-09-02 18:52:05
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
lixiangchun/imagenet-swav-resnet50w2
|
lixiangchun
| 2022-10-28T04:13:37Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"onnx",
"region:us"
] | null | 2022-10-20T04:06:01Z |
```python
import trace_layer2 as models
import torch
x=torch.randn(1, 3, 224, 224)
state_dict = torch.load('swav_imagenet_layer2.pt', map_location='cpu')
model = models.resnet50w2()
model.load_state_dict(state_dict)
model.eval()
feature = model(x)
traced_model = torch.jit.load('traced_swav_imagenet_layer2.pt', map_location='cpu')
traced_model.eval()
feature = traced_model(x)
```
|
agungbesti/house
|
agungbesti
| 2022-10-28T02:59:23Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-10-28T02:53:02Z |
---
title: Protas
emoji: 🏃
colorFrom: yellow
colorTo: pink
sdk: gradio
app_file: app.py
pinned: false
license: apache-2.0
---
# Configuration
`title`: _string_
Display title for the Space
`emoji`: _string_
Space emoji (emoji-only character allowed)
`colorFrom`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`colorTo`: _string_
Color for Thumbnail gradient (red, yellow, green, blue, indigo, purple, pink, gray)
`sdk`: _string_
Can be either `gradio` or `streamlit`
`sdk_version` : _string_
Only applicable for `streamlit` SDK.
See [doc](https://hf.co/docs/hub/spaces) for more info on supported versions.
`app_file`: _string_
Path to your main application file (which contains either `gradio` or `streamlit` Python code).
Path is relative to the root of the repository.
`pinned`: _boolean_
Whether the Space stays on top of your list.
|
helloway/simple
|
helloway
| 2022-10-28T02:00:19Z | 0 | 0 | null |
[
"audio-classification",
"license:apache-2.0",
"region:us"
] |
audio-classification
| 2022-10-28T01:51:37Z |
---
license: apache-2.0
tags:
- audio-classification
---
|
Kolgrima/Luna
|
Kolgrima
| 2022-10-28T01:39:20Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2022-10-27T23:48:49Z |
---
license: openrail
---
## Model of Evanna Lynch as Luna Lovegood
If you've ever tried to create an image of Luna Lovegood from the movies, you'll have noticed Stable Diffusion is not good at this! That's where this model comes in.
This has been trained on 38 images of Evanna Lynch as Luna Lovegood.
## Usage
Simply use the keyword "**Luna**" anywhere in your prompt.
### Output Examples
Each image has embedded data that can be read from the PNG info tab in Stable diffusion Web UI.
|
skang/distilbert-base-uncased-finetuned-imdb
|
skang
| 2022-10-28T01:38:56Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T01:30:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.76 | 1.0 | 157 | 0.6640 |
| 0.688 | 2.0 | 314 | 0.6581 |
| 0.6768 | 3.0 | 471 | 0.6604 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
ByungjunKim/distilbert-base-uncased-finetuned-imdb
|
ByungjunKim
| 2022-10-28T01:36:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T01:27:52Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6627
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.76 | 1.0 | 157 | 0.6640 |
| 0.688 | 2.0 | 314 | 0.6581 |
| 0.6768 | 3.0 | 471 | 0.6604 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/revmaxxing
|
huggingtweets
| 2022-10-28T01:23:51Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T23:49:45Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1578729528695963649/mmiLKGp1_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rev 🇷🇺 🌾 🛞</div>
<div style="text-align: center; font-size: 14px;">@revmaxxing</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Rev 🇷🇺 🌾 🛞.
| Data | Rev 🇷🇺 🌾 🛞 |
| --- | --- |
| Tweets downloaded | 3097 |
| Retweets | 241 |
| Short tweets | 416 |
| Tweets kept | 2440 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nfmh3no/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @revmaxxing's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/zust2rmi) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/zust2rmi/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/revmaxxing')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Rogerooo/bordaloii
|
Rogerooo
| 2022-10-28T00:57:28Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-28T00:49:17Z |
---
license: creativeml-openrail-m
---
|
OpenMatch/cocodr-large-msmarco-idro-only
|
OpenMatch
| 2022-10-28T00:45:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T00:42:33Z |
---
license: mit
---
This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
caffsean/bert-base-cased-deep-ritmo
|
caffsean
| 2022-10-28T00:17:00Z | 161 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T03:19:50Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-deep-ritmo
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-cased-deep-ritmo
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5837
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.0463 | 1.0 | 1875 | 3.7428 |
| 3.3393 | 2.0 | 3750 | 3.0259 |
| 2.7435 | 3.0 | 5625 | 2.5837 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
allenai/scirepeval_adapters_rgn
|
allenai
| 2022-10-28T00:05:08Z | 6 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:scirepeval/regression",
"bert",
"dataset:allenai/scirepeval",
"region:us"
] | null | 2022-10-28T00:04:59Z |
---
tags:
- adapterhub:scirepeval/regression
- adapter-transformers
- bert
datasets:
- allenai/scirepeval
---
# Adapter `allenai/scirepeval_adapters_rgn` for malteos/scincl
An [adapter](https://adapterhub.ml) for the `malteos/scincl` model that was trained on the [scirepeval/regression](https://adapterhub.ml/explore/scirepeval/regression/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("malteos/scincl")
adapter_name = model.load_adapter("allenai/scirepeval_adapters_rgn", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
OpenMatch/condenser-large
|
OpenMatch
| 2022-10-28T00:04:23Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T23:44:05Z |
---
license: mit
---
This model has been pretrained on BookCorpus and English Wikipedia following the approach described in the paper **Condenser: a Pre-training Architecture for Dense Retrieval**. The model can be used to reproduce the experimental results within the GitHub repository https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
OpenMatch/co-condenser-large
|
OpenMatch
| 2022-10-28T00:03:42Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T23:56:37Z |
---
license: mit
---
This model has been pretrained on MS MARCO following the approach described in the paper **Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**. The model can be used to reproduce the experimental results within the GitHub repository https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
allenai/scirepeval_adapters_clf
|
allenai
| 2022-10-28T00:03:35Z | 14 | 0 |
adapter-transformers
|
[
"adapter-transformers",
"adapterhub:scirepeval/classification",
"bert",
"dataset:allenai/scirepeval",
"region:us"
] | null | 2022-10-28T00:03:26Z |
---
tags:
- adapterhub:scirepeval/classification
- adapter-transformers
- bert
datasets:
- allenai/scirepeval
---
# Adapter `allenai/scirepeval_adapters_clf` for malteos/scincl
An [adapter](https://adapterhub.ml) for the `malteos/scincl` model that was trained on the [scirepeval/classification](https://adapterhub.ml/explore/scirepeval/classification/) dataset.
This adapter was created for usage with the **[adapter-transformers](https://github.com/Adapter-Hub/adapter-transformers)** library.
## Usage
First, install `adapter-transformers`:
```
pip install -U adapter-transformers
```
_Note: adapter-transformers is a fork of transformers that acts as a drop-in replacement with adapter support. [More](https://docs.adapterhub.ml/installation.html)_
Now, the adapter can be loaded and activated like this:
```python
from transformers import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("malteos/scincl")
adapter_name = model.load_adapter("allenai/scirepeval_adapters_clf", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here -->
|
rajistics/setfit-model
|
rajistics
| 2022-10-27T23:47:04Z | 2 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-27T23:46:48Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
ViktorDo/SciBERT-POWO_Climber_Finetuned
|
ViktorDo
| 2022-10-27T22:39:38Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T21:19:57Z |
---
tags:
- generated_from_trainer
model-index:
- name: SciBERT-POWO_Climber_Finetuned
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. -->
# SciBERT-POWO_Climber_Finetuned
This model is a fine-tuned version of [allenai/scibert_scivocab_uncased](https://huggingface.co/allenai/scibert_scivocab_uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1086
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1033 | 1.0 | 2133 | 0.1151 |
| 0.0853 | 2.0 | 4266 | 0.1058 |
| 0.0792 | 3.0 | 6399 | 0.1086 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
JamesH/Translation_en_to_fr_project
|
JamesH
| 2022-10-27T21:52:09Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"translation",
"en",
"fr",
"dataset:JamesH/autotrain-data-second-project-en2fr",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-10-27T19:57:24Z |
---
tags:
- autotrain
- translation
language:
- en
- fr
datasets:
- JamesH/autotrain-data-second-project-en2fr
co2_eq_emissions:
emissions: 0.6863820434350988
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 1907464829
- CO2 Emissions (in grams): 0.6864
## Validation Metrics
- Loss: 1.117
- SacreBLEU: 16.546
- Gen len: 14.511
|
wavymulder/zelda-diffusion-HN
|
wavymulder
| 2022-10-27T21:32:27Z | 0 | 18 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-10-25T01:06:42Z |
---
license: creativeml-openrail-m
---
**Zelda Diffusion - Hypernet**
[*DOWNLOAD LINK*](https://huggingface.co/wavymulder/zelda-diffusion-HN/resolve/main/zeldaBOTW.pt) - This is a hypernet trained on screenshots of Princess Zelda from BOTW
Here's a random batch of 9 images to show the hypernet uncherrypicked. The prompt is "anime princess zelda volumetric lighting" and the negative prompt is "cel render 3d animation"
and [a link to more](https://i.imgur.com/NixQGid.jpg)
---
Tips:
You'll want to adjust the hypernetwork strength depending on what style you're trying to put Zelda into. I usually keep it at 80% strength and go from there.
This hypernetwork helps make Zelda look more like the BOTW Zelda. You still have to prompt for what you want. Extra weight might sometimes need to be applied to get her to wear costumes. You may also have luck putting her name closer to the end of the prompt than you normally would.
Since the hypernetwork is trained on screenshots from the videogame, it imparts a heavy Cel Shading effect [(Example here)](https://huggingface.co/wavymulder/zelda-diffusion-HN/resolve/main/00108-920950.png). You can minimize this by negative prompting "cel". I believe every example posted here uses this.
The hypernet can be used either with very simple prompting, as shown above, or a prompt of your favourite artists.
You can put this hypernet on top of different models to create some really cool Zeldas, such as this one made with [Nitrosocke](https://huggingface.co/nitrosocke)'s [Modern Disney Model](https://huggingface.co/nitrosocke/modern-disney-diffusion).
|
RUCAIBox/elmer
|
RUCAIBox
| 2022-10-27T21:30:13Z | 4 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"text-generation",
"non-autoregressive-generation",
"early-exit",
"en",
"arxiv:2210.13304",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T21:14:19Z |
---
license: apache-2.0
language:
- en
tags:
- text-generation
- non-autoregressive-generation
- early-exit
---
# ELMER
The ELMER model was proposed in [**ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation**](https://arxiv.org/abs/2210.13304) by Junyi Li, Tianyi Tang, Wayne Xin Zhao, Jian-Yun Nie and Ji-Rong Wen.
The detailed information and instructions can be found [https://github.com/RUCAIBox/ELMER](https://github.com/RUCAIBox/ELMER).
## Model Description
ELMER is an efficient and effective PLM for NAR text generation, which generates tokens at different layers by leveraging the early exit technique.
The architecture of ELMER is a variant of the standard Transformer encoder-decoder and poses three technical contributions:
1. For decoder, we replace the original masked multi-head attention with bi-directional multi-head attention akin to the encoder. Therefore, ELMER dynamically adjusts the output length by emitting an end token "[EOS]" at any position.
2. Leveraging early exit, ELMER injects "off-ramps" at each decoder layer, which make predictions with intermediate hidden states. If ELMER exits at the $l$-th layer, we copy the $l$-th hidden states to the subsequent layers.
3. ELMER utilizes a novel pre-training objective, layer permutation language modeling (LPLM), to pre-train on the large-scale corpus. LPLM permutes the exit layer for each token from 1 to the maximum layer $L$.
## Examples
To fine-tune ELMER on non-autoregressive text generation:
```python
>>> from transformers import BartTokenizer as ElmerTokenizer
>>> from transformers import BartForConditionalGeneration as ElmerForConditionalGeneration
>>> tokenizer = ElmerTokenizer.from_pretrained("RUCAIBox/elmer")
>>> model = ElmerForConditionalGeneration.from_pretrained("RUCAIBox/elmer")
```
## Citation
```bibtex
@article{lijunyi2022elmer,
title={ELMER: A Non-Autoregressive Pre-trained Language Model for Efficient and Effective Text Generation},
author={Li, Junyi and Tang, Tianyi and Zhao, Wayne Xin and Nie, Jian-Yun and Wen, Ji-Rong},
booktitle={EMNLP 2022},
year={2022}
}
```
|
OpenMatch/cocodr-base-msmarco-idro-only
|
OpenMatch
| 2022-10-27T21:26:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:mit",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-10-27T21:21:56Z |
---
license: mit
---
This model has been pretrained on MS MARCO corpus and then finetuned on MS MARCO training data with implicit distributionally robust optimization (iDRO), following the approach described in the paper **COCO-DR: Combating Distribution Shifts in Zero-Shot Dense Retrieval with Contrastive and Distributionally Robust Learning**. The associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-base as the backbone with 110M hyperparameters.
|
Phantasion/phaninc
|
Phantasion
| 2022-10-27T21:03:33Z | 0 | 1 | null |
[
"region:us"
] | null | 2022-10-27T20:18:49Z |
Phaninc is a model based on my cyberpunk tumblr blog phantasyinc. One thing that has frustrated me with AI art is the generic quality of prompting for cyberpunk imagery, so I went through my blog and curated a dataset for 25 new keywords to get the results I desire. I have been heavily inspired by the work of nousr on robodiffusion whose model gave me a lot of results I love.
I have utilised the new FAST dreambooth method, and run it at 20000 steps on 684 images (around 800 steps per concept). At the time of writing the model is still training but I thought I would use my training time to summarise my intent with each keyword. I expect there to be problems and some of my experiments to not pan out so well, but I thought I would share.
*Post training update: the entire model is contaminated, most prompts are gonna churn out cyberpunk work, but the keywords are still good against one another and work as desired, and the base model has had some interesting lessons taught to it.*
**phanborg**
This set was the first to be tested, it is a combination of portraits of cyborgs much like phancyborg and phandroid. The difference between the three is that phanborg uses a combination of images with the face covered and uncovered by machinery, while phancyborg uses only uncovered cyborgs and phandroid only covered cyborgs. The images used in all three are entirely different so that I can play with a diversity of trained features with my keywords.
**phanbrutal**
Images I consider a combination of cyberpunk and brutalism.
**phanbw**
This one is one of my more experimental keywords, utilising monochrome cyberpunk images I find quite striking in black and white. However apart from sticking to a cyberpunk theme, there is no consistent subject matter and may just end up being a generic monochrome keyword.
**phancircle**
another experimental keyword, this keyword utilises a selection of architectural, textural and 3d design images with circles and spheres as a recurring motif. My hope is this keyword will help provide a cyberpunk texture to other prompts with a circular motif.
**phancity**
Bleak futuristic cityscapes, but like phanbw this experiment may fail due to being too varied subject matter.
**phanconcrete**
concrete, images of architecture with mostly concrete finishes, might be overkill with phanbrutal above, but I like that there will still be nuanced differences to play with.
**phanconsole**
A command centre needs buttons to beep and switches to boop, this keyword is all about screens and buttons.
**phancorridor**
images of spaceship corridors and facilities to provide a more futuristic interior design.
**phancyborg**
phancyborg is an image selection of cyborgs with some or all of a human face uncovered.
**phandraw**
a selection focused on drawn cyberpunk artwork with bright neon colors and defined linework
**phandroid**
this is where I pay most homage to nousrs robodiffusion, using only cyborgs with their faces concealed or just plain humanoid robots
**phandustrial**
futuristic ndustrial imagery of pipes wires and messes of cables.
**phanfashion**
trying to get that urbanwear hoodie look but with some variations.
**phanfem**
a series of cyberpunk women
**phanglitch**
Glitch art I had reblogged on the blog with a cyberpunk feel. Quite colorful.
**phangrunge**
Dilapidated dens for the scum of the city. Hopefully will add a good dose of urban decay to your prompt.
**phanlogo**
Sleek graphic design, typography and logos.
**phanmachine**
Built with unclear subject matter, phanmachine focuses on the details of futuristic shiny machinery in hopes of it coming out as a style or texture that can be applied in prompts.
**phanmecha**
The three cyborg keywords are sleek and humanoid, phanmecha focuses more on creating unique robot bodytypes.
**phanmilitary**
Future soldiers, man and machine. Likely to attach a gun to your prompt's character.
**phanneon**
Bright neon lights taking over the scene, this feature is what annoyed me with a lot of cyberpunk prompts in ai models. Overall I have it pretty isolated to this keyword, if you want those futuristic glowies.
**phanrooms**
Totally seperate to the rest of the theming, phanrooms is trained on backrooms and liminal space imagery. Which like cyberpunk is of high visual interest to me, and something the base model can sometimes struggle with.
**phansterile**
This is like cyberpunk cleancore, lots of white, very clean, clinical theming.
**phantex**
I don't know why latex outfits are cyberpunk but they just are, these images were selected for the accessorising on top of just the latex outfits.
**phanture**
Abstract textures that were cyberpunk enough for me to put on my blog.
|
motmono/ppo-LunarLander-v2
|
motmono
| 2022-10-27T20:39:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T20:39:09Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 272.74 +/- 15.00
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
...
```
|
andrewzhang505/sf2-lunar-lander
|
andrewzhang505
| 2022-10-27T19:51:07Z | 2 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T19:50:47Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- metrics:
- type: mean_reward
value: 126.58 +/- 137.36
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLanderContinuous-v2
type: LunarLanderContinuous-v2
---
A(n) **APPO** model trained on the **LunarLanderContinuous-v2** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
Aitor/testpyramidsrnd
|
Aitor
| 2022-10-27T19:45:32Z | 1 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-10-27T19:45:24Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: Aitor/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
PraveenKishore/Reinforce-CartPole-v1
|
PraveenKishore
| 2022-10-27T18:59:10Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T18:50:31Z |
---
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: 94.10 +/- 36.62
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 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Eleusinian/haladas
|
Eleusinian
| 2022-10-27T18:07:35Z | 0 | 0 | null |
[
"license:unknown",
"region:us"
] | null | 2022-10-27T18:00:03Z |
---
license: unknown
---
<div style='display: flex; flex-wrap: wrap; column-gap: 0.75rem;'>
<img src='https://s3.amazonaws.com/moonup/production/uploads/1666893412370-noauth.jpeg' width='400' height='400'>
<img src='https://s3.amazonaws.com/moonup/production/uploads/1666893411703-noauth.jpeg' width='400' height='400'>
<img src='https://s3.amazonaws.com/moonup/production/uploads/1666893411826-noauth.jpeg' width='400' height='400'>
<img src='https://s3.amazonaws.com/moonup/production/uploads/1666893411866-noauth.jpeg' width='400' height='400'>
</div>
|
vict0rsch/climateGAN
|
vict0rsch
| 2022-10-27T17:49:52Z | 0 | 2 | null |
[
"Climate Change",
"GAN",
"Domain Adaptation",
"en",
"license:gpl-3.0",
"region:us"
] | null | 2022-10-24T13:17:28Z |
---
language:
- en
tags:
- Climate Change
- GAN
- Domain Adaptation
license: gpl-3.0
title: ClimateGAN
emoji: 🌎
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 3.6
app_file: app.py
inference: true
pinned: true
---
# ClimateGAN: Raising Awareness about Climate Change by Generating Images of Floods
This repository contains the code used to train the model presented in our **[paper](https://openreview.net/forum?id=EZNOb_uNpJk)**.
It is not simply a presentation repository but the code we have used over the past 30 months to come to our final architecture. As such, you will find many scripts, classes, blocks and options which we actively use for our own development purposes but are not directly relevant to reproduce results or use pretrained weights.
If you use this code, data or pre-trained weights, please cite our ICLR 2022 paper:
```
@inproceedings{schmidt2022climategan,
title = {Climate{GAN}: Raising Climate Change Awareness by Generating Images of Floods},
author = {Victor Schmidt and Alexandra Luccioni and M{\'e}lisande Teng and Tianyu Zhang and Alexia Reynaud and Sunand Raghupathi and Gautier Cosne and Adrien Juraver and Vahe Vardanyan and Alex Hern{\'a}ndez-Garc{\'\i}a and Yoshua Bengio},
booktitle = {International Conference on Learning Representations},
year = {2022},
url = {https://openreview.net/forum?id=EZNOb_uNpJk}
}
```
## Using pre-trained weights from this Huggingface Space and Stable Diffusion In-painting
<p align="center">
<strong>Huggingface ClimateGAN Space:</strong>
<a href="https://huggingface.co/spaces/vict0rsch/climateGAN" target="_blank">
<img src="https://huggingface.co/vict0rsch/climateGAN/resolve/main/images/hf-cg.png">
</a>
</p>
1. Download code and model
```bash
git lfs install
git clone https://huggingface.co/vict0rsch/climateGAN
git lfs pull # optional if you don't have the weights
```
2. Install requirements
```
pip install requirements.txt
```
3. **Enable Stable Diffusion Inpainting** by visiting the model's card: https://huggingface.co/runwayml/stable-diffusion-inpainting **and** running `$ huggingface-cli login`
4. Run `$ python climategan_wrapper.py help` for usage instructions on how to infer on a folder's images.
5. Run `$ python app.py` to see the Gradio app.
1. To use Google Street View you'll need an API key and set the `GMAPS_API_KEY` environment variable.
2. To use Stable Diffusion if you can't run `$ huggingface-cli login` (on a Huggingface Space for instance) set the `HF_AUTH_TOKEN` env variable to a [Huggingface authorization token](https://huggingface.co/settings/tokens)
3. To change the UI without model overhead, set the `CG_DEV_MODE` environment variable to `true`.
For a more fine-grained control on ClimateGAN's inferences, refer to `apply_events.py` (does not support Stable Diffusion painter)
**Note:** you don't have control on the prompt by design because I disabled the safety checker. Fork this space/repo and do it yourself if you really need to change the prompt. At least [open a discussion](https://huggingface.co/spaces/vict0rsch/climateGAN/discussions).
## Using pre-trained weights from source
In the paper, we present ClimateGAN as a solution to produce images of floods. It can actually do **more**:
* reusing the segmentation map, we are able to isolate the sky, turn it red and in a few more steps create an image resembling the consequences of a wildfire on a neighboring area, similarly to the [California wildfires](https://www.google.com/search?q=california+wildfires+red+sky&source=lnms&tbm=isch&sa=X&ved=2ahUKEwisws-hx7zxAhXxyYUKHQyKBUwQ_AUoAXoECAEQBA&biw=1680&bih=917&dpr=2).
* reusing the depth map, we can simulate the consequences of a smog event on an image, scaling the intensity of the filter by the distance of an object to the camera, as per [HazeRD](http://www2.ece.rochester.edu/~gsharma/papers/Zhang_ICIP2017_HazeRD.pdf)
In this section we'll explain how to produce the `Painted Input` along with the Smog and Wildfire outputs of a pre-trained ClimateGAN model.
### Installation
This repository and associated model have been developed using Python 3.8.2 and **Pytorch 1.7.0**.
```bash
$ git clone git@github.com:cc-ai/climategan.git
$ cd climategan
$ pip install -r requirements-3.8.2.txt # or `requirements-any.txt` for other Python versions (not tested but expected to be fine)
```
Our pipeline uses [comet.ml](https://comet.ml) to log images. You don't *have* to use their services but we recommend you do as images can be uploaded on your workspace instead of being written to disk.
If you want to use Comet, make sure you have the [appropriate configuration in place (API key and workspace at least)](https://www.comet.ml/docs/python-sdk/advanced/#non-interactive-setup)
### Inference
1. Download and unzip the weights [from this link](https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K&export=download) (checkout [`gdown`](https://github.com/wkentaro/gdown) for a commandline interface) and put them in `config/`
```
$ pip install gdown
$ mkdir config
$ cd config
$ gdown https://drive.google.com/u/0/uc?id=18OCUIy7JQ2Ow_-cC5xn_hhDn-Bp45N1K
$ unzip release-github-v1.zip
$ cd ..
```
2. Run from the repo's root:
1. With `comet`:
```bash
python apply_events.py --batch_size 4 --half --images_paths path/to/a/folder --resume_path config/model/masker --upload
```
2. Without `comet` (and shortened args compared to the previous example):
```bash
python apply_events.py -b 4 --half -i path/to/a/folder -r config/model/masker --output_path path/to/a/folder
```
The `apply_events.py` script has many options, for instance to use a different output size than the default systematic `640 x 640` pixels, look at the code or `python apply_events.py --help`.
## Training from scratch
ClimateGAN is split in two main components: the Masker producing a binary mask of where water should go and the Painter generating water within this mask given an initial image's context.
### Configuration
The code is structured to use `shared/trainer/defaults.yaml` as default configuration. There are 2 ways of overriding those for your purposes (without altering that file):
1. By providing an alternative configuration as command line argument `config=path/to/config.yaml`
1. The code will first load `shared/trainer/defaults.yaml`
2. *then* update the resulting dictionary with values read in the provided `config` argument.
3. The folder `config/` is NOT tracked by git so you would typically put them there
2. By overwriting specific arguments from the command-line like `python train.py data.loaders.batch_size=8`
### Data
#### Masker
##### Real Images
Because of copyrights issues we are not able to share the real images scrapped from the internet. You would have to do that yourself. In the `yaml` config file, the code expects a key pointing to a `json` file like `data.files.<train or val>.r: <path/to/a/json/file>`. This `json` file should be a list of dictionaries with tasks as keys and files as values. Example:
```json
[
{
"x": "path/to/a/real/image",
"s": "path/to/a/segmentation_map",
"d": "path/to/a/depth_map"
},
...
]
```
Following the [ADVENT](https://github.com/valeoai/ADVENT) procedure, only `x` should be required. We use `s` and `d` inferred from pre-trained models (DeepLab v3+ and MiDAS) to use those pseudo-labels in the first epochs of training (see `pseudo:` in the config file)
##### Simulated Images
We share snapshots of the Virtual World we created in the [Mila-Simulated-Flood dataset](). You can download and unzip one water-level and then produce json files similar to that of the real data, with an additional key `"m": "path/to/a/ground_truth_sim_mask"`. Lastly, edit the config file: `data.files.<train or val>.s: <path/to/a/json/file>`
#### Painter
The painter expects input images and binary masks to train using the [GauGAN](https://github.com/NVlabs/SPADE) training procedure. Unfortunately we cannot share openly the collected data, but similarly as for the Masker's real data you would point to the data using a `json` file as:
```json
[
{
"x": "path/to/a/real/image",
"m": "path/to/a/water_mask",
},
...
]
```
And put those files as values to `data.files.<train or val>.rf: <path/to/a/json/file>` in the configuration.
## Coding conventions
* Tasks
* `x` is an input image, in [-1, 1]
* `s` is a segmentation target with `long` classes
* `d` is a depth map target in R, may be actually `log(depth)` or `1/depth`
* `m` is a binary mask with 1s where water is/should be
* Domains
* `r` is the *real* domain for the masker. Input images are real pictures of urban/suburban/rural areas
* `s` is the *simulated* domain for the masker. Input images are taken from our Unity world
* `rf` is the *real flooded* domain for the painter. Training images are pairs `(x, m)` of flooded scenes for which the water should be reconstructed, in the validation data input images are not flooded and we provide a manually labeled mask `m`
* `kitti` is a special `s` domain to pre-train the masker on [Virtual Kitti 2](https://europe.naverlabs.com/research/computer-vision/proxy-virtual-worlds-vkitti-2/)
* it alters the `trainer.loaders` dict to select relevant data sources from `trainer.all_loaders` in `trainer.switch_data()`. The rest of the code is identical.
* Flow
* This describes the call stack for the trainers standard training procedure
* `train()`
* `run_epoch()`
* `update_G()`
* `zero_grad(G)`
* `get_G_loss()`
* `get_masker_loss()`
* `masker_m_loss()` -> masking loss
* `masker_s_loss()` -> segmentation loss
* `masker_d_loss()` -> depth estimation loss
* `get_painter_loss()` -> painter's loss
* `g_loss.backward()`
* `g_opt_step()`
* `update_D()`
* `zero_grad(D)`
* `get_D_loss()`
* painter's disc losses
* `masker_m_loss()` -> masking AdvEnt disc loss
* `masker_s_loss()` -> segmentation AdvEnt disc loss
* `d_loss.backward()`
* `d_opt_step()`
* `update_learning_rates()` -> update learning rates according to schedules defined in `opts.gen.opt` and `opts.dis.opt`
* `run_validation()`
* compute val losses
* `eval_images()` -> compute metrics
* `log_comet_images()` -> compute and upload inferences
* `save()`
|
Houryy/Houry
|
Houryy
| 2022-10-27T16:27:08Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2022-10-27T16:27:08Z |
---
license: bigscience-openrail-m
---
|
hagerty7/recyclable-materials-classification
|
hagerty7
| 2022-10-27T15:54:32Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-24T15:10:05Z |
ViT for Recyclable Material Classification
|
mgb-dx-meetup/distilbert-multilingual-finetuned-sentiment
|
mgb-dx-meetup
| 2022-10-27T15:43:10Z | 100 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"unk",
"dataset:lewtun/autotrain-data-mgb-product-reviews-mbert",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T15:34:22Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-mgb-product-reviews-mbert
co2_eq_emissions:
emissions: 5.523107849339405
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1904564767
- CO2 Emissions (in grams): 5.5231
## Validation Metrics
- Loss: 1.135
- Accuracy: 0.514
- Macro F1: 0.504
- Micro F1: 0.514
- Weighted F1: 0.505
- Macro Precision: 0.506
- Micro Precision: 0.514
- Weighted Precision: 0.507
- Macro Recall: 0.513
- Micro Recall: 0.514
- Weighted Recall: 0.514
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lewtun/autotrain-mgb-product-reviews-mbert-1904564767
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-mgb-product-reviews-mbert-1904564767", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-mgb-product-reviews-mbert-1904564767", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
mgb-dx-meetup/xlm-roberta-finetuned-sentiment
|
mgb-dx-meetup
| 2022-10-27T15:37:04Z | 102 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"unk",
"dataset:lewtun/autotrain-data-mgb-product-reviews-xlm-r",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T15:17:01Z |
---
tags:
- autotrain
- text-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- lewtun/autotrain-data-mgb-product-reviews-xlm-r
co2_eq_emissions:
emissions: 19.116414139555882
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1904264758
- CO2 Emissions (in grams): 19.1164
## Validation Metrics
- Loss: 1.021
- Accuracy: 0.563
- Macro F1: 0.555
- Micro F1: 0.563
- Weighted F1: 0.556
- Macro Precision: 0.555
- Micro Precision: 0.563
- Weighted Precision: 0.556
- Macro Recall: 0.562
- Micro Recall: 0.563
- Weighted Recall: 0.563
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("lewtun/autotrain-mgb-product-reviews-xlm-r-1904264758", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
Sennodipoi/LayoutLMv3-FUNSD-ft
|
Sennodipoi
| 2022-10-27T15:29:16Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-23T08:14:07Z |
LayoutLMv3 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained using seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|-----------|--------|----------|---------------|
| Answer | 0.90 | 0.91 | 0.90 | 3e-5 |
| Header | 0.61 | 0.66 | 0.63 | 4e-4 |
| Question | 0.88 | 0.87 | 0.88 | 1e-4 |
| Micro avg | 0.87 | 0.88 | 0.87 | 3e-5 |
| Macro avg | 0.79 | 0.82 | 0.80 | 3e-5 |
| Weighted avg | 0.87 | 0.88 | 0.87 | 3e-5 |
|
Sennodipoi/LayoutLMv1-FUNSD-ft
|
Sennodipoi
| 2022-10-27T15:27:32Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-23T08:10:54Z |
LayoutLMv1 fine-tuned on the FUNSD dataset. Code and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained using seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|--------------|-----------|--------|----------|---------------|
| ANSWER | 0.80 | 0.78 | 0.80 | 1e-4 |
| HEADER | 0.62 | 0.47 | 0.53 | 2e-4 |
| QUESTION | 0.85 | 0.71 | 0.83 | 3e-5 |
| Micro avg | 0.83 | 0.77 | 0.81 | 1e-4 |
| Macro avg | 0.77 | 0.56 | 0.72 | 3e-5 |
| Weighted avg | 0.83 | 0.78 | 0.80 | 1e-4 |
|
Sennodipoi/LayoutLMv3-kleisterNDA
|
Sennodipoi
| 2022-10-27T15:26:00Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-24T15:26:45Z |
LayoutLMv3 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm).
Results obtained with seqeval in strict mode:
| | Precision | Recall | F1-score | Variance (F1) |
|----------------|-----------|--------|----------|---------------|
| EFFECTIVE_DATE | 0.92 | 0.99 | 0.95 | 5e-5 |
| JURISDICTION | 0.87 | 0.88 | 0.88 | 8e-6 |
| PARTY | 0.92 | 0.99 | 0.95 | 5e-5 |
| TERM | 1 | 1 | 1 | 0 |
| Micro avg | 0.91 | 0.96 | 0.94 | 2e-5 |
| Macro avg | 0.92 | 0.96 | 0.94 | 3e-7 |
| Weighted avg | 0.91 | 0.96 | 0.94 | 2e-5 |
Since I used the same segmentation strategy of the original paper i.e. using the labels to create segments, the scores are not directly comparable with the other LayoutLM versions.
|
Sennodipoi/LayoutLMv1-kleisterNDA
|
Sennodipoi
| 2022-10-27T15:18:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"layoutlm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-08-24T07:33:25Z |
LayoutLMv1 fine-tuned on the Kleister-NDA dataset. Code (including pre-processing) and results are available at the official GitHub repository of my [Master Degree thesis ](https://github.com/AleRosae/thesis-layoutlm)
Results obtained with seqeval in strict mode:
| | Precision | Recall| F1-score | Variance (F1) |
|--------------------------|--------------------|-----------------|-------------------|------------------------|
| EFFECTIVE\_DATE | 0.87 | 0.51 | 0.64 | 2e-6 |
| JURISDICTION | 0.75 | 0.84 | 0.80 | 4e-7 |
| PARTY | 0.84 | 0.71 | 0.77 | 9e-6 |
| TERM | 0.69 | 0.51 | 0.58 | 1e-3 |
| Micro avg | 0.81 | 0.72 | 0.77 | 2e-6 |
| Macro avg | 0.79 | 0.65 | 0.70 | 9e-5 |
| Weighted avg | 0.82 | 0.73 | 0.76 | 3e-6 |
|
alanakbik/test-push-public
|
alanakbik
| 2022-10-27T15:10:07Z | 3 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"region:us"
] |
token-classification
| 2022-10-27T15:07:07Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
---
### Demo: How to use in Flair
Requires:
- **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("alanakbik/test-push-public")
# make example sentence
sentence = Sentence("On September 1st George won 1 dollar while watching Game of Thrones.")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
|
yubol/bert-finetuned-ner-30
|
yubol
| 2022-10-27T15:03:09Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-27T13:19:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
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-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0453
- Precision: 0.9275
- Recall: 0.9492
- F1: 0.9382
- Accuracy: 0.9934
## 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: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 407 | 0.0539 | 0.8283 | 0.8758 | 0.8514 | 0.9866 |
| 0.1524 | 2.0 | 814 | 0.0333 | 0.8931 | 0.9123 | 0.9026 | 0.9915 |
| 0.0381 | 3.0 | 1221 | 0.0345 | 0.8835 | 0.9280 | 0.9052 | 0.9906 |
| 0.0179 | 4.0 | 1628 | 0.0351 | 0.8890 | 0.9361 | 0.9119 | 0.9909 |
| 0.0089 | 5.0 | 2035 | 0.0310 | 0.9102 | 0.9372 | 0.9235 | 0.9924 |
| 0.0089 | 6.0 | 2442 | 0.0344 | 0.9198 | 0.9383 | 0.9289 | 0.9922 |
| 0.0057 | 7.0 | 2849 | 0.0331 | 0.9144 | 0.9448 | 0.9294 | 0.9931 |
| 0.0039 | 8.0 | 3256 | 0.0340 | 0.9144 | 0.9481 | 0.9309 | 0.9928 |
| 0.0027 | 9.0 | 3663 | 0.0423 | 0.9032 | 0.9481 | 0.9251 | 0.9921 |
| 0.0018 | 10.0 | 4070 | 0.0373 | 0.9047 | 0.9507 | 0.9271 | 0.9923 |
| 0.0018 | 11.0 | 4477 | 0.0448 | 0.8932 | 0.9474 | 0.9195 | 0.9910 |
| 0.0014 | 12.0 | 4884 | 0.0380 | 0.9079 | 0.9474 | 0.9272 | 0.9928 |
| 0.0015 | 13.0 | 5291 | 0.0360 | 0.9231 | 0.9474 | 0.9351 | 0.9936 |
| 0.0013 | 14.0 | 5698 | 0.0378 | 0.9243 | 0.9456 | 0.9348 | 0.9935 |
| 0.0013 | 15.0 | 6105 | 0.0414 | 0.9197 | 0.9496 | 0.9344 | 0.9930 |
| 0.0009 | 16.0 | 6512 | 0.0405 | 0.9202 | 0.9478 | 0.9338 | 0.9929 |
| 0.0009 | 17.0 | 6919 | 0.0385 | 0.9305 | 0.9441 | 0.9373 | 0.9933 |
| 0.0006 | 18.0 | 7326 | 0.0407 | 0.9285 | 0.9437 | 0.9360 | 0.9934 |
| 0.0009 | 19.0 | 7733 | 0.0428 | 0.9203 | 0.9488 | 0.9343 | 0.9929 |
| 0.0006 | 20.0 | 8140 | 0.0455 | 0.9232 | 0.9536 | 0.9382 | 0.9928 |
| 0.0004 | 21.0 | 8547 | 0.0462 | 0.9261 | 0.9529 | 0.9393 | 0.9930 |
| 0.0004 | 22.0 | 8954 | 0.0423 | 0.9359 | 0.9492 | 0.9425 | 0.9940 |
| 0.0005 | 23.0 | 9361 | 0.0446 | 0.9180 | 0.9529 | 0.9351 | 0.9931 |
| 0.0005 | 24.0 | 9768 | 0.0430 | 0.9361 | 0.9467 | 0.9413 | 0.9938 |
| 0.0002 | 25.0 | 10175 | 0.0436 | 0.9322 | 0.9496 | 0.9408 | 0.9935 |
| 0.0002 | 26.0 | 10582 | 0.0440 | 0.9275 | 0.9492 | 0.9382 | 0.9935 |
| 0.0002 | 27.0 | 10989 | 0.0450 | 0.9272 | 0.9488 | 0.9379 | 0.9932 |
| 0.0002 | 28.0 | 11396 | 0.0445 | 0.9304 | 0.9470 | 0.9386 | 0.9935 |
| 0.0003 | 29.0 | 11803 | 0.0449 | 0.9278 | 0.9481 | 0.9378 | 0.9934 |
| 0.0001 | 30.0 | 12210 | 0.0453 | 0.9275 | 0.9492 | 0.9382 | 0.9934 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pig4431/sst2_bert_3epoch
|
pig4431
| 2022-10-27T15:01:53Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-27T14:55:30Z |
---
tags:
- generated_from_trainer
model-index:
- name: sst2_bert_3epoch
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sst2_bert_3epoch
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Shri3/q-FrozenLake-v1-4x4-noSlippery
|
Shri3
| 2022-10-27T14:33:14Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-27T14:07:26Z |
---
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 playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Shri3/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"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
huggingtweets/tykesinties
|
huggingtweets
| 2022-10-27T14:31:37Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-25T19:33:52Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tykesinties/1666881093237/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/917201427583438848/X-zHDjYL_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">RegressCo H.R.</div>
<div style="text-align: center; font-size: 14px;">@tykesinties</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from RegressCo H.R..
| Data | RegressCo H.R. |
| --- | --- |
| Tweets downloaded | 1844 |
| Retweets | 215 |
| Short tweets | 27 |
| Tweets kept | 1602 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2pqqtat7/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tykesinties's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1vqh1gov) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1vqh1gov/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/tykesinties')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
yeahrmek/arxiv-math-lean
|
yeahrmek
| 2022-10-27T14:05:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-10-27T12:23:41Z |
This is a BPE tokenizer based on "Salesforce/codegen-350M-mono".
The tokenizer has been trained to treat spaces like parts of the tokens (a bit like sentencepiece)
so a word will be encoded differently whether it is at the beginning of the sentence (without space) or not.
We used ArXiv subset of The Pile dataset and proof steps from [lean-step-public](https://github.com/jesse-michael-han/lean-step-public) datasets to train the tokenizer.
|
OWG/imagegpt-small
|
OWG
| 2022-10-27T13:10:17Z | 0 | 0 | null |
[
"onnx",
"vision",
"dataset:imagenet-21k",
"license:apache-2.0",
"region:us"
] | null | 2022-10-27T11:52:39Z |
---
license: apache-2.0
tags:
- vision
datasets:
- imagenet-21k
---
# ImageGPT (small-sized model)
ImageGPT (iGPT) model pre-trained on ImageNet ILSVRC 2012 (14 million images, 21,843 classes) at resolution 32x32. It was introduced in the paper [Generative Pretraining from Pixels](https://cdn.openai.com/papers/Generative_Pretraining_from_Pixels_V2.pdf) by Chen et al. and first released in [this repository](https://github.com/openai/image-gpt). See also the official [blog post](https://openai.com/blog/image-gpt/).
## Model description
The ImageGPT (iGPT) is a transformer decoder model (GPT-like) pretrained on a large collection of images in a self-supervised fashion, namely ImageNet-21k, at a resolution of 32x32 pixels.
The goal for the model is simply to predict the next pixel value, given the previous ones.
By pre-training the model, it learns an inner representation of images that can then be used to:
- extract features useful for downstream tasks: one can either use ImageGPT to produce fixed image features, in order to train a linear model (like a sklearn logistic regression model or SVM). This is also referred to as "linear probing".
- perform (un)conditional image generation.
## Intended uses & limitations
You can use the raw model for either feature extractor or (un) conditional image generation.
### How to use
Here is how to use this model as feature extractor:
```python
from transformers import AutoFeatureExtractor
from onnxruntime import InferenceSession
from datasets import load_dataset
# load image
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained("openai/imagegpt-small")
session = InferenceSession("model/model.onnx")
# ONNX Runtime expects NumPy arrays as input
inputs = feature_extractor(image, return_tensors="np")
outputs = session.run(output_names=["last_hidden_state"], input_feed=dict(inputs))
```
Or you can use the model with classification head that returns logits
```python
from transformers import AutoFeatureExtractor
from onnxruntime import InferenceSession
from datasets import load_dataset
# load image
dataset = load_dataset("huggingface/cats-image")
image = dataset["test"]["image"][0]
# load model
feature_extractor = AutoFeatureExtractor.from_pretrained("openai/imagegpt-small")
session = InferenceSession("model/model_classification.onnx")
# ONNX Runtime expects NumPy arrays as input
inputs = feature_extractor(image, return_tensors="np")
outputs = session.run(output_names=["logits"], input_feed=dict(inputs))
```
## Original implementation
Follow [this link](https://huggingface.co/openai/imagegpt-small) to see the original implementation.
## Training data
The ImageGPT model was pretrained on [ImageNet-21k](http://www.image-net.org/), a dataset consisting of 14 million images and 21k classes.
## Training procedure
### Preprocessing
Images are first resized/rescaled to the same resolution (32x32) and normalized across the RGB channels. Next, color-clustering is performed. This means that every pixel is turned into one of 512 possible cluster values. This way, one ends up with a sequence of 32x32 = 1024 pixel values, rather than 32x32x3 = 3072, which is prohibitively large for Transformer-based models.
### Pretraining
Training details can be found in section 3.4 of v2 of the paper.
## Evaluation results
For evaluation results on several image classification benchmarks, we refer to the original paper.
### BibTeX entry and citation info
```bibtex
@InProceedings{pmlr-v119-chen20s,
title = {Generative Pretraining From Pixels},
author = {Chen, Mark and Radford, Alec and Child, Rewon and Wu, Jeffrey and Jun, Heewoo and Luan, David and Sutskever, Ilya},
booktitle = {Proceedings of the 37th International Conference on Machine Learning},
pages = {1691--1703},
year = {2020},
editor = {III, Hal Daumé and Singh, Aarti},
volume = {119},
series = {Proceedings of Machine Learning Research},
month = {13--18 Jul},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v119/chen20s/chen20s.pdf},
url = {https://proceedings.mlr.press/v119/chen20s.html
}
```
```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}
}
```
|
kevinbror/bertbaseuncasedny
|
kevinbror
| 2022-10-27T12:13:45Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-27T12:13:00Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: bertbaseuncasedny
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. -->
# bertbaseuncasedny
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: 0.3901
- Train End Logits Accuracy: 0.8823
- Train Start Logits Accuracy: 0.8513
- Validation Loss: 1.2123
- Validation End Logits Accuracy: 0.7291
- Validation Start Logits Accuracy: 0.6977
- Epoch: 3
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 29508, '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}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2597 | 0.6683 | 0.6277 | 1.0151 | 0.7214 | 0.6860 | 0 |
| 0.7699 | 0.7820 | 0.7427 | 1.0062 | 0.7342 | 0.6996 | 1 |
| 0.5343 | 0.8425 | 0.8064 | 1.1162 | 0.7321 | 0.7010 | 2 |
| 0.3901 | 0.8823 | 0.8513 | 1.2123 | 0.7291 | 0.6977 | 3 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
kosec39/distilbert-base-uncased-finetuned-imdb
|
kosec39
| 2022-10-27T12:00:24Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-27T11:31:31Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
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-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4898 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Rijgersberg/whisper-small-fy-NL
|
Rijgersberg
| 2022-10-27T08:50:21Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-10-25T22:17:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: whisper-small-fy-NL
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. -->
# whisper-small-fy-NL
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the [CommonVoice 11 `fy-NL` (West-Frisian)](https://huggingface.co/datasets/mozilla-foundation/common_voice_11_0/viewer/fy-NL/train) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5276
- Wer: 0.2919
The Wer before finetuning was 1.0622.
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| | 0 | 0 | | 1.0622|
| 0.9177 | 1.0 | 211 | 0.8145 | 0.3450 |
| 0.5807 | 2.0 | 422 | 0.7113 | 0.3640 |
| 0.2884 | 3.0 | 633 | 0.5276 | 0.2919 |
### Framework versions
- Transformers 4.24.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
teacookies/autotrain-27102022-cert-1899564594
|
teacookies
| 2022-10-27T07:34:21Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-27102022-cert",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-27T07:21:17Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-27102022-cert
co2_eq_emissions:
emissions: 22.03607609264655
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1899564594
- CO2 Emissions (in grams): 22.0361
## Validation Metrics
- Loss: 0.003
- Accuracy: 0.999
- Precision: 0.981
- Recall: 0.982
- F1: 0.981
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-27102022-cert-1899564594
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-27102022-cert-1899564594", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-27102022-cert-1899564594", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
pouxie/LaBSE-en-ru-bviolet
|
pouxie
| 2022-10-27T07:21:10Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-27T04:29:25Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 4095 with parameters:
```
{'batch_size': 8}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 3,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1228,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
teacookies/autotrain-27102022-cert1-1899464570
|
teacookies
| 2022-10-27T06:29:42Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"token-classification",
"unk",
"dataset:teacookies/autotrain-data-27102022-cert1",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-27T06:19:22Z |
---
tags:
- autotrain
- token-classification
language:
- unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- teacookies/autotrain-data-27102022-cert1
co2_eq_emissions:
emissions: 16.254745105263574
---
# Model Trained Using AutoTrain
- Problem type: Entity Extraction
- Model ID: 1899464570
- CO2 Emissions (in grams): 16.2547
## Validation Metrics
- Loss: 0.004
- Accuracy: 0.999
- Precision: 0.972
- Recall: 0.979
- F1: 0.975
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/teacookies/autotrain-27102022-cert1-1899464570
```
Or Python API:
```
from transformers import AutoModelForTokenClassification, AutoTokenizer
model = AutoModelForTokenClassification.from_pretrained("teacookies/autotrain-27102022-cert1-1899464570", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("teacookies/autotrain-27102022-cert1-1899464570", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/daymoded-menthalovely-scolopendridaes
|
huggingtweets
| 2022-10-27T05:43:26Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-27T05:26:45Z |
---
language: en
thumbnail: http://www.huggingtweets.com/daymoded-menthalovely-scolopendridaes/1666849354903/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1541285406531956736/T36HqJWY_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1576010406446907395/cXmkdxpb_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1576595483157749760/GgLl95Ug_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">meri & Mentha & 𓆣</div>
<div style="text-align: center; font-size: 14px;">@daymoded-menthalovely-scolopendridaes</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from meri & Mentha & 𓆣.
| Data | meri | Mentha | 𓆣 |
| --- | --- | --- | --- |
| Tweets downloaded | 3208 | 3203 | 646 |
| Retweets | 595 | 1723 | 407 |
| Short tweets | 560 | 449 | 131 |
| Tweets kept | 2053 | 1031 | 108 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2ervd3sj/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @daymoded-menthalovely-scolopendridaes's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28d01du3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28d01du3/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/daymoded-menthalovely-scolopendridaes')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
Negs/ddpm-butterflies-128
|
Negs
| 2022-10-27T04:07:05Z | 4 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-27T02:51:00Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/Negs/ddpm-butterflies-128/tensorboard?#scalars)
|
PKR/swin-tiny-patch4-window7-224-finetuned-eurosat
|
PKR
| 2022-10-27T03:21:42Z | 61 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-27T02:53:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9814814814814815
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0593
- Accuracy: 0.9815
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2731 | 1.0 | 190 | 0.1128 | 0.9637 |
| 0.1862 | 2.0 | 380 | 0.0759 | 0.9759 |
| 0.1409 | 3.0 | 570 | 0.0593 | 0.9815 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Alex-VisTas/swin-tiny-patch4-window7-224-finetuned-woody_130epochs
|
Alex-VisTas
| 2022-10-27T03:11:10Z | 49 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-26T14:13:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-woody_130epochs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8921212121212121
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-woody_130epochs
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4550
- Accuracy: 0.8921
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 130
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6694 | 1.0 | 58 | 0.6370 | 0.6594 |
| 0.6072 | 2.0 | 116 | 0.5813 | 0.7030 |
| 0.6048 | 3.0 | 174 | 0.5646 | 0.7030 |
| 0.5849 | 4.0 | 232 | 0.5778 | 0.6970 |
| 0.5671 | 5.0 | 290 | 0.5394 | 0.7236 |
| 0.5575 | 6.0 | 348 | 0.5212 | 0.7382 |
| 0.568 | 7.0 | 406 | 0.5218 | 0.7358 |
| 0.5607 | 8.0 | 464 | 0.5183 | 0.7527 |
| 0.5351 | 9.0 | 522 | 0.5138 | 0.7467 |
| 0.5459 | 10.0 | 580 | 0.5290 | 0.7394 |
| 0.5454 | 11.0 | 638 | 0.5212 | 0.7345 |
| 0.5291 | 12.0 | 696 | 0.5130 | 0.7576 |
| 0.5378 | 13.0 | 754 | 0.5372 | 0.7503 |
| 0.5264 | 14.0 | 812 | 0.6089 | 0.6861 |
| 0.4909 | 15.0 | 870 | 0.4852 | 0.7636 |
| 0.5591 | 16.0 | 928 | 0.4817 | 0.76 |
| 0.4966 | 17.0 | 986 | 0.5673 | 0.6933 |
| 0.4988 | 18.0 | 1044 | 0.5131 | 0.7418 |
| 0.5339 | 19.0 | 1102 | 0.4998 | 0.7394 |
| 0.4804 | 20.0 | 1160 | 0.4655 | 0.7733 |
| 0.503 | 21.0 | 1218 | 0.4554 | 0.7685 |
| 0.4859 | 22.0 | 1276 | 0.4713 | 0.7770 |
| 0.504 | 23.0 | 1334 | 0.4545 | 0.7721 |
| 0.478 | 24.0 | 1392 | 0.4658 | 0.7830 |
| 0.4759 | 25.0 | 1450 | 0.4365 | 0.8012 |
| 0.4686 | 26.0 | 1508 | 0.4452 | 0.7855 |
| 0.4668 | 27.0 | 1566 | 0.4427 | 0.7879 |
| 0.4615 | 28.0 | 1624 | 0.4439 | 0.7685 |
| 0.4588 | 29.0 | 1682 | 0.4378 | 0.7830 |
| 0.4588 | 30.0 | 1740 | 0.4229 | 0.7988 |
| 0.4296 | 31.0 | 1798 | 0.4188 | 0.7976 |
| 0.4208 | 32.0 | 1856 | 0.4316 | 0.7891 |
| 0.4481 | 33.0 | 1914 | 0.4331 | 0.7891 |
| 0.4253 | 34.0 | 1972 | 0.4524 | 0.7879 |
| 0.4117 | 35.0 | 2030 | 0.4570 | 0.7952 |
| 0.4405 | 36.0 | 2088 | 0.4307 | 0.7927 |
| 0.4154 | 37.0 | 2146 | 0.4257 | 0.8024 |
| 0.3962 | 38.0 | 2204 | 0.5077 | 0.7818 |
| 0.414 | 39.0 | 2262 | 0.4602 | 0.8012 |
| 0.3937 | 40.0 | 2320 | 0.4741 | 0.7770 |
| 0.4186 | 41.0 | 2378 | 0.4250 | 0.8 |
| 0.4076 | 42.0 | 2436 | 0.4353 | 0.7988 |
| 0.3777 | 43.0 | 2494 | 0.4442 | 0.7879 |
| 0.3968 | 44.0 | 2552 | 0.4525 | 0.7879 |
| 0.377 | 45.0 | 2610 | 0.4198 | 0.7988 |
| 0.378 | 46.0 | 2668 | 0.4297 | 0.8097 |
| 0.3675 | 47.0 | 2726 | 0.4435 | 0.8085 |
| 0.3562 | 48.0 | 2784 | 0.4477 | 0.7952 |
| 0.381 | 49.0 | 2842 | 0.4206 | 0.8255 |
| 0.3603 | 50.0 | 2900 | 0.4136 | 0.8109 |
| 0.3331 | 51.0 | 2958 | 0.4141 | 0.8230 |
| 0.3471 | 52.0 | 3016 | 0.4253 | 0.8109 |
| 0.346 | 53.0 | 3074 | 0.5203 | 0.8048 |
| 0.3481 | 54.0 | 3132 | 0.4288 | 0.8242 |
| 0.3411 | 55.0 | 3190 | 0.4416 | 0.8194 |
| 0.3275 | 56.0 | 3248 | 0.4149 | 0.8291 |
| 0.3067 | 57.0 | 3306 | 0.4623 | 0.8218 |
| 0.3166 | 58.0 | 3364 | 0.4432 | 0.8255 |
| 0.3294 | 59.0 | 3422 | 0.4599 | 0.8267 |
| 0.3146 | 60.0 | 3480 | 0.4266 | 0.8291 |
| 0.3091 | 61.0 | 3538 | 0.4318 | 0.8315 |
| 0.3277 | 62.0 | 3596 | 0.4252 | 0.8242 |
| 0.296 | 63.0 | 3654 | 0.4332 | 0.8436 |
| 0.3241 | 64.0 | 3712 | 0.4729 | 0.8194 |
| 0.3104 | 65.0 | 3770 | 0.4228 | 0.8448 |
| 0.2878 | 66.0 | 3828 | 0.4173 | 0.8388 |
| 0.265 | 67.0 | 3886 | 0.4210 | 0.8497 |
| 0.3011 | 68.0 | 3944 | 0.4276 | 0.8436 |
| 0.2861 | 69.0 | 4002 | 0.4923 | 0.8315 |
| 0.2994 | 70.0 | 4060 | 0.4472 | 0.8182 |
| 0.276 | 71.0 | 4118 | 0.4541 | 0.8315 |
| 0.2796 | 72.0 | 4176 | 0.4218 | 0.8521 |
| 0.2727 | 73.0 | 4234 | 0.4053 | 0.8448 |
| 0.255 | 74.0 | 4292 | 0.4356 | 0.8376 |
| 0.276 | 75.0 | 4350 | 0.4193 | 0.8436 |
| 0.261 | 76.0 | 4408 | 0.4484 | 0.8533 |
| 0.2416 | 77.0 | 4466 | 0.4722 | 0.8194 |
| 0.2602 | 78.0 | 4524 | 0.4431 | 0.8533 |
| 0.2591 | 79.0 | 4582 | 0.4269 | 0.8606 |
| 0.2613 | 80.0 | 4640 | 0.4335 | 0.8485 |
| 0.2555 | 81.0 | 4698 | 0.4269 | 0.8594 |
| 0.2832 | 82.0 | 4756 | 0.3968 | 0.8715 |
| 0.264 | 83.0 | 4814 | 0.4173 | 0.8703 |
| 0.2462 | 84.0 | 4872 | 0.4150 | 0.8606 |
| 0.2424 | 85.0 | 4930 | 0.4377 | 0.8630 |
| 0.2574 | 86.0 | 4988 | 0.4120 | 0.8679 |
| 0.2273 | 87.0 | 5046 | 0.4393 | 0.8533 |
| 0.2334 | 88.0 | 5104 | 0.4366 | 0.8630 |
| 0.2258 | 89.0 | 5162 | 0.4189 | 0.8630 |
| 0.2153 | 90.0 | 5220 | 0.4474 | 0.8630 |
| 0.2462 | 91.0 | 5278 | 0.4362 | 0.8642 |
| 0.2356 | 92.0 | 5336 | 0.4454 | 0.8715 |
| 0.2019 | 93.0 | 5394 | 0.4413 | 0.88 |
| 0.209 | 94.0 | 5452 | 0.4410 | 0.8703 |
| 0.2201 | 95.0 | 5510 | 0.4323 | 0.8691 |
| 0.2245 | 96.0 | 5568 | 0.4999 | 0.8618 |
| 0.2178 | 97.0 | 5626 | 0.4612 | 0.8655 |
| 0.2163 | 98.0 | 5684 | 0.4340 | 0.8703 |
| 0.2228 | 99.0 | 5742 | 0.4504 | 0.8788 |
| 0.2151 | 100.0 | 5800 | 0.4602 | 0.8703 |
| 0.1988 | 101.0 | 5858 | 0.4414 | 0.8812 |
| 0.2227 | 102.0 | 5916 | 0.4392 | 0.8824 |
| 0.1772 | 103.0 | 5974 | 0.5069 | 0.8630 |
| 0.2199 | 104.0 | 6032 | 0.4648 | 0.8667 |
| 0.1936 | 105.0 | 6090 | 0.4806 | 0.8691 |
| 0.199 | 106.0 | 6148 | 0.4569 | 0.8764 |
| 0.2149 | 107.0 | 6206 | 0.4445 | 0.8739 |
| 0.1917 | 108.0 | 6264 | 0.4444 | 0.8727 |
| 0.201 | 109.0 | 6322 | 0.4594 | 0.8727 |
| 0.1938 | 110.0 | 6380 | 0.4564 | 0.8764 |
| 0.1977 | 111.0 | 6438 | 0.4398 | 0.8739 |
| 0.1776 | 112.0 | 6496 | 0.4356 | 0.88 |
| 0.1939 | 113.0 | 6554 | 0.4412 | 0.8848 |
| 0.178 | 114.0 | 6612 | 0.4373 | 0.88 |
| 0.1926 | 115.0 | 6670 | 0.4508 | 0.8812 |
| 0.1979 | 116.0 | 6728 | 0.4477 | 0.8848 |
| 0.1958 | 117.0 | 6786 | 0.4488 | 0.8897 |
| 0.189 | 118.0 | 6844 | 0.4553 | 0.8836 |
| 0.1838 | 119.0 | 6902 | 0.4605 | 0.8848 |
| 0.1755 | 120.0 | 6960 | 0.4463 | 0.8836 |
| 0.1958 | 121.0 | 7018 | 0.4474 | 0.8861 |
| 0.1857 | 122.0 | 7076 | 0.4550 | 0.8921 |
| 0.1466 | 123.0 | 7134 | 0.4494 | 0.8885 |
| 0.1751 | 124.0 | 7192 | 0.4560 | 0.8873 |
| 0.175 | 125.0 | 7250 | 0.4383 | 0.8897 |
| 0.207 | 126.0 | 7308 | 0.4601 | 0.8873 |
| 0.1756 | 127.0 | 7366 | 0.4425 | 0.8897 |
| 0.1695 | 128.0 | 7424 | 0.4533 | 0.8909 |
| 0.1873 | 129.0 | 7482 | 0.4510 | 0.8897 |
| 0.1726 | 130.0 | 7540 | 0.4463 | 0.8909 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/msg
|
sd-concepts-library
| 2022-10-27T00:39:23Z | 0 | 1 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-27T00:39:18Z |
---
license: mit
---
### MSG on Stable Diffusion
This is the `<MSG69>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
|
huggingtweets/_a_bat
|
huggingtweets
| 2022-10-26T23:12:22Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T23:08:36Z |
---
language: en
thumbnail: http://www.huggingtweets.com/_a_bat/1666825888934/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/2415729722/9rhiyt5scbbzagfdxrx2_400x400.gif')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Taw - version2.bat</div>
<div style="text-align: center; font-size: 14px;">@_a_bat</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Taw - version2.bat.
| Data | Taw - version2.bat |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 336 |
| Short tweets | 258 |
| Tweets kept | 2653 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2fdjcy6g/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @_a_bat's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/n2exl5h2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/n2exl5h2/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/_a_bat')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
JoAmps/littledatasets
|
JoAmps
| 2022-10-26T22:20:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-26T22:05:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: littledatasets
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. -->
# littledatasets
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 85 | 0.0053 |
| No log | 2.0 | 170 | 0.0002 |
| No log | 3.0 | 255 | 0.0001 |
| No log | 4.0 | 340 | 0.0001 |
| No log | 5.0 | 425 | 0.0001 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
PraveenKishore/MLAgents-Pyramids
|
PraveenKishore
| 2022-10-26T21:59:04Z | 7 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-10-26T21:32:30Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: PraveenKishore/MLAgents-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
JoAmps/littledataset
|
JoAmps
| 2022-10-26T21:53:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-26T21:39:48Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: littledataset
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. -->
# littledataset
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0000
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 169 | 0.0001 |
| No log | 2.0 | 338 | 0.0000 |
| 0.0036 | 3.0 | 507 | 0.0001 |
| 0.0036 | 4.0 | 676 | 0.0000 |
| 0.0036 | 5.0 | 845 | 0.0000 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.1
- Datasets 2.5.1
- Tokenizers 0.12.1
|
huggingtweets/big___oven-schizo_freq
|
huggingtweets
| 2022-10-26T21:50:36Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T17:42:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/big___oven-schizo_freq/1666821031327/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1571653458972794884/eaxhUsib_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1582126821025382400/PZjx83du_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">oskcar & Lukas (computer)</div>
<div style="text-align: center; font-size: 14px;">@big___oven-schizo_freq</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from oskcar & Lukas (computer).
| Data | oskcar | Lukas (computer) |
| --- | --- | --- |
| Tweets downloaded | 2642 | 3234 |
| Retweets | 605 | 480 |
| Short tweets | 325 | 326 |
| Tweets kept | 1712 | 2428 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/t7nn481m/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @big___oven-schizo_freq's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ljhfklh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ljhfklh/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/big___oven-schizo_freq')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
doodlevelyn/xlm-roberta-large-finetuned-conll03-english
|
doodlevelyn
| 2022-10-26T21:38:20Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T03:45:25Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-conll03-english
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. -->
# xlm-roberta-large-finetuned-conll03-english
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6008
- Precision: 0.4263
- Recall: 0.1404
- F1: 0.2112
- Accuracy: 0.9559
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0001 | 1.0 | 29460 | 0.6008 | 0.4263 | 0.1404 | 0.2112 | 0.9559 |
| 0.0 | 2.0 | 58920 | 0.6008 | 0.4263 | 0.1404 | 0.2112 | 0.9559 |
| 0.0001 | 3.0 | 88380 | 0.6008 | 0.4263 | 0.1404 | 0.2112 | 0.9559 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/nearcyan
|
huggingtweets
| 2022-10-26T21:10:01Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T21:08:44Z |
---
language: en
thumbnail: http://www.huggingtweets.com/nearcyan/1666818597137/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1446575702439043077/kNKnkoyI_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">nearcyan</div>
<div style="text-align: center; font-size: 14px;">@nearcyan</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from nearcyan.
| Data | nearcyan |
| --- | --- |
| Tweets downloaded | 3246 |
| Retweets | 132 |
| Short tweets | 136 |
| Tweets kept | 2978 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/ilun9vdk/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @nearcyan's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/16w8mubo) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/16w8mubo/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/nearcyan')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
andrewzhang505/doom_test
|
andrewzhang505
| 2022-10-26T20:56:17Z | 1 | 0 |
sample-factory
|
[
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"region:us"
] |
reinforcement-learning
| 2022-10-26T20:54:41Z |
---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
---
A(n) **APPO** model trained on the **doom_deathmatch_bots** environment.
This model was trained using Sample Factory 2.0: https://github.com/alex-petrenko/sample-factory
|
huggingtweets/big___oven-naamitee
|
huggingtweets
| 2022-10-26T20:15:40Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T20:12:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/big___oven-naamitee/1666815335749/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1548322756059545605/ndrcvhSk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1571653458972794884/eaxhUsib_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">bymyamym & oskcar</div>
<div style="text-align: center; font-size: 14px;">@big___oven-naamitee</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from bymyamym & oskcar.
| Data | bymyamym | oskcar |
| --- | --- | --- |
| Tweets downloaded | 168 | 2628 |
| Retweets | 45 | 605 |
| Short tweets | 41 | 325 |
| Tweets kept | 82 | 1698 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/drhgr3vu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @big___oven-naamitee's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/vrwpswox) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/vrwpswox/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/big___oven-naamitee')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
sd-concepts-library/cute-game-style
|
sd-concepts-library
| 2022-10-26T19:06:50Z | 0 | 23 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-26T18:31:32Z |
---
license: mit
---
### Cute Game Style on Stable Diffusion
This is the `<cute-game-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
Here are images generated with this style:
|
kevinbror/whynotwork
|
kevinbror
| 2022-10-26T19:02:37Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-26T19:02:10Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: whynotwork
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. -->
# whynotwork
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: 1.2892
- Train End Logits Accuracy: 0.6617
- Train Start Logits Accuracy: 0.6190
- Validation Loss: 1.0393
- Validation End Logits Accuracy: 0.7213
- Validation Start Logits Accuracy: 0.6877
- 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', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 7377, '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}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2892 | 0.6617 | 0.6190 | 1.0393 | 0.7213 | 0.6877 | 0 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/snobrights
|
huggingtweets
| 2022-10-26T18:18:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T18:17:24Z |
---
language: en
thumbnail: http://www.huggingtweets.com/snobrights/1666808315124/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1562231899925397504/PZnUZWaV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">vote4ana</div>
<div style="text-align: center; font-size: 14px;">@snobrights</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from vote4ana.
| Data | vote4ana |
| --- | --- |
| Tweets downloaded | 1947 |
| Retweets | 510 |
| Short tweets | 353 |
| Tweets kept | 1084 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/163lcflh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @snobrights's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6bnd5aob) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6bnd5aob/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/snobrights')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
PraveenKishore/dqn-SpaceInvadersNoFrameskip-v4
|
PraveenKishore
| 2022-10-26T18:07:45Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-10-26T18:07:09Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 626.50 +/- 127.69
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PraveenKishore -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga PraveenKishore -f logs/
rl_zoo3 enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga PraveenKishore
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
huggingtweets/gretathotburg-snobrights
|
huggingtweets
| 2022-10-26T17:59:14Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T16:41:44Z |
---
language: en
thumbnail: http://www.huggingtweets.com/gretathotburg-snobrights/1666807149420/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1551255816992350210/yjE--1UN_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1562231899925397504/PZnUZWaV_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">cathy & vote4ana</div>
<div style="text-align: center; font-size: 14px;">@gretathotburg-snobrights</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from cathy & vote4ana.
| Data | cathy | vote4ana |
| --- | --- | --- |
| Tweets downloaded | 1107 | 1948 |
| Retweets | 254 | 511 |
| Short tweets | 362 | 353 |
| Tweets kept | 491 | 1084 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2129jbxh/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @gretathotburg-snobrights's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3dq4zw12) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3dq4zw12/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/gretathotburg-snobrights')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
asparius/big-balanced-combined-bert
|
asparius
| 2022-10-26T17:56:54Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-24T19:41:04Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: big-balanced-combined-bert
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. -->
# big-balanced-combined-bert
This model is a fine-tuned version of [dbmdz/bert-base-turkish-128k-uncased](https://huggingface.co/dbmdz/bert-base-turkish-128k-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2872
- Accuracy: 0.9055
- F1: 0.9061
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu102
- Datasets 2.4.0
- Tokenizers 0.12.1
|
NeelNanda/SoLU_2L_v10_old
|
NeelNanda
| 2022-10-26T17:13:59Z | 71 | 0 |
transformers
|
[
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2022-10-12T08:57:58Z |
A 2L, width 736 SoLU model trained on 15B tokens of the Pile. Bugs: the layernorm just before the unembed is an RMS norm, and the width is not a multiple of 64, so d_head=64 and n_heads=11, and n_heads * d_head != d_model :(
|
rjac/setfit-ST-ICD10-L3
|
rjac
| 2022-10-26T16:28:30Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-26T16:28:17Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 1349 with parameters:
```
{'batch_size': 450, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1349,
"warmup_steps": 135,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Jsjjdnwjskxij6/Ffg
|
Jsjjdnwjskxij6
| 2022-10-26T15:24:13Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-10-26T15:24:13Z |
---
license: bigscience-bloom-rail-1.0
---
|
pig4431/rtm_ALBERT_5E
|
pig4431
| 2022-10-26T15:04:14Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T15:03:22Z |
---
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
model-index:
- name: model_output_dir
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. -->
# model_output_dir
This model was trained from scratch on the rotten_tomatoes 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
YumaSaito/distilbert-base-uncased-finetuned-emotion
|
YumaSaito
| 2022-10-26T15:03:55Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-23T14:15:34Z |
---
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: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.926
- name: F1
type: f1
value: 0.9261092845869646
---
<!-- 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: 0.2181
- Accuracy: 0.926
- F1: 0.9261
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8618 | 1.0 | 250 | 0.3206 | 0.903 | 0.8990 |
| 0.2549 | 2.0 | 500 | 0.2181 | 0.926 | 0.9261 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
yyyyifan/mlkiadapter
|
yyyyifan
| 2022-10-26T14:47:27Z | 0 | 0 | null |
[
"arxiv:2210.13617",
"region:us"
] | null | 2022-10-26T14:45:18Z |
Pretrained adapters for multilingual knowledge graph enhancement (https://arxiv.org/abs/2210.13617).
---
license: mit
---
|
gstqtfr/ddpm-butterflies-128
|
gstqtfr
| 2022-10-26T13:57:03Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-10-25T17:02:11Z |
---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/gstqtfr/ddpm-butterflies-128/tensorboard?#scalars)
|
pig4431/rtm_BERT_5E
|
pig4431
| 2022-10-26T13:44:44Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:rotten_tomatoes",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T13:38:36Z |
---
tags:
- generated_from_trainer
datasets:
- rotten_tomatoes
model-index:
- name: rtm_bert_5E
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. -->
# rtm_bert_5E
This model was trained from scratch on the rotten_tomatoes 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: 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
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
mrm8488/codebert-base-finetuned-code-ner-15e
|
mrm8488
| 2022-10-26T13:42:00Z | 24 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T11:57:15Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: codebert-base-finetuned-code-ner-15e
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. -->
# codebert-base-finetuned-code-ner-15e
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3831
- Precision: 0.6363
- Recall: 0.6494
- F1: 0.6428
- Accuracy: 0.9197
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 191 | 0.4566 | 0.5021 | 0.4220 | 0.4585 | 0.8827 |
| No log | 2.0 | 382 | 0.3756 | 0.5699 | 0.5764 | 0.5731 | 0.9043 |
| 0.5133 | 3.0 | 573 | 0.3605 | 0.6001 | 0.5767 | 0.5882 | 0.9093 |
| 0.5133 | 4.0 | 764 | 0.3500 | 0.6130 | 0.6130 | 0.6130 | 0.9153 |
| 0.5133 | 5.0 | 955 | 0.3501 | 0.6337 | 0.6172 | 0.6254 | 0.9178 |
| 0.2203 | 6.0 | 1146 | 0.3645 | 0.6250 | 0.6352 | 0.6300 | 0.9163 |
| 0.2203 | 7.0 | 1337 | 0.3488 | 0.6263 | 0.6422 | 0.6341 | 0.9189 |
| 0.1457 | 8.0 | 1528 | 0.3575 | 0.6372 | 0.6397 | 0.6384 | 0.9194 |
| 0.1457 | 9.0 | 1719 | 0.3662 | 0.6406 | 0.6343 | 0.6375 | 0.9189 |
| 0.1457 | 10.0 | 1910 | 0.3613 | 0.6374 | 0.6473 | 0.6423 | 0.9201 |
| 0.107 | 11.0 | 2101 | 0.3716 | 0.6329 | 0.6544 | 0.6435 | 0.9197 |
| 0.107 | 12.0 | 2292 | 0.3754 | 0.6328 | 0.6487 | 0.6406 | 0.9193 |
| 0.107 | 13.0 | 2483 | 0.3826 | 0.6395 | 0.6490 | 0.6443 | 0.9204 |
| 0.0863 | 14.0 | 2674 | 0.3821 | 0.6368 | 0.6535 | 0.6451 | 0.9200 |
| 0.0863 | 15.0 | 2865 | 0.3831 | 0.6363 | 0.6494 | 0.6428 | 0.9197 |
### Evaluation results
| | Algorithm | Application | Class | Code_Block | Data_Structure | Data_Type | Device | Error_Name | File_Name | File_Type | Function | HTML_XML_Tag | Keyboard_IP | Language | Library | Operating_System | Output_Block | User_Interface_Element | User_Name | Value | Variable | Version | Website | overall_precision | overall_recall | overall_f1 | overall_accuracy |
|:----------|------------:|--------------:|------------:|-------------:|-----------------:|------------:|----------:|-------------:|------------:|------------:|-----------:|---------------:|--------------:|-----------:|-----------:|-------------------:|---------------:|-------------------------:|------------:|-----------:|-----------:|-----------:|----------:|--------------------:|-----------------:|-------------:|-------------------:|
| precision | 0 | 0.619835 | 0.680851 | 0.455629 | 0.813187 | 0.592593 | 0.395062 | 0.181818 | 0.800505 | 0.775956 | 0.757664 | 0.585366 | 0.333333 | 0.689769 | 0.61807 | 0.769231 | 0.0212766 | 0.542214 | 0.4375 | 0.370236 | 0.560479 | 0.883721 | 0.382353 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| recall | 0 | 0.677711 | 0.696864 | 0.494253 | 0.840909 | 0.8 | 0.533333 | 0.333333 | 0.794486 | 0.628319 | 0.631387 | 0.470588 | 0.0169492 | 0.81323 | 0.546279 | 0.843373 | 0.04 | 0.653846 | 0.518519 | 0.52987 | 0.54482 | 0.914089 | 0.270833 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| f1 | 0 | 0.647482 | 0.688765 | 0.474156 | 0.826816 | 0.680851 | 0.453901 | 0.235294 | 0.797484 | 0.694377 | 0.688786 | 0.521739 | 0.0322581 | 0.746429 | 0.579961 | 0.804598 | 0.0277778 | 0.592821 | 0.474576 | 0.435897 | 0.552538 | 0.898649 | 0.317073 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
| number | 31 | 664 | 1148 | 696 | 264 | 120 | 60 | 30 | 798 | 226 | 822 | 102 | 59 | 257 | 551 | 83 | 25 | 442 | 54 | 385 | 859 | 291 | 48 | 0.626308 | 0.642171 | 0.63414 | 0.918927 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Karelito00/swin-tiny-patch4-window7-224-finetuned-eurosat
|
Karelito00
| 2022-10-26T13:40:05Z | 59 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-10-26T13:15:14Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9822222222222222
---
<!-- 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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0501
- Accuracy: 0.9822
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3259 | 1.0 | 379 | 0.0760 | 0.9763 |
| 0.1882 | 2.0 | 758 | 0.0694 | 0.9778 |
| 0.1563 | 3.0 | 1137 | 0.0501 | 0.9822 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
lafayettecreditrepair/Credit-Repair-Services-Lafayette
|
lafayettecreditrepair
| 2022-10-26T13:08:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-10-26T13:07:58Z |
We are a family-owned and operated Credit Repair company, founded in 2013. Our goal is to help you achieve financial success and reach your credit goals.
We’re not your average credit repair firm, we truly care, so we only charge for the items we pursue on your report. Not only does this make us one of the FASTEST credit restoration companies, but we’re also one of the most affordable.
Follow this [link](https://lafayette.asapcreditrepairusa.com/)
|
KGsteven/distilbert-base-uncased-finetuned-cola
|
KGsteven
| 2022-10-26T12:36:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-19T11:25:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- matthews_correlation
model-index:
- name: distilbert-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. -->
# distilbert-base-uncased-finetuned-cola
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: 0.3038
- Matthews Correlation: 0.9198
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 1.2169 | 1.0 | 626 | 0.6782 | 0.8605 |
| 0.5513 | 2.0 | 1252 | 0.4085 | 0.8998 |
| 0.343 | 3.0 | 1878 | 0.3346 | 0.9122 |
| 0.1642 | 4.0 | 2504 | 0.3106 | 0.9165 |
| 0.1216 | 5.0 | 3130 | 0.3038 | 0.9198 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Tokenizers 0.13.1
|
huggingtweets/femoidfurry
|
huggingtweets
| 2022-10-26T11:56:36Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/femoidfurry/1666785376927/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1569453578493763590/MerXNdrF_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">shitbrain dyke upside down era</div>
<div style="text-align: center; font-size: 14px;">@femoidfurry</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from shitbrain dyke upside down era.
| Data | shitbrain dyke upside down era |
| --- | --- |
| Tweets downloaded | 3211 |
| Retweets | 1977 |
| Short tweets | 106 |
| Tweets kept | 1128 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34ui7fp9/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @femoidfurry's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/177yzikv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/177yzikv/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/femoidfurry')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
chintagunta85/biobert-base-cased-v1.2-bc2gm-ner
|
chintagunta85
| 2022-10-26T11:38:53Z | 30 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:bc2gm_corpus",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T10:46:44Z |
---
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-bc2gm-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc2gm_corpus
type: bc2gm_corpus
config: bc2gm_corpus
split: train
args: bc2gm_corpus
metrics:
- name: Precision
type: precision
value: 0.7988356059445381
- name: Recall
type: recall
value: 0.8243478260869566
- name: F1
type: f1
value: 0.8113912231559292
- name: Accuracy
type: accuracy
value: 0.9772069842818806
---
<!-- 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. -->
# biobert-base-cased-v1.2-bc2gm-ner
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1528
- Precision: 0.7988
- Recall: 0.8243
- F1: 0.8114
- Accuracy: 0.9772
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.057 | 1.0 | 782 | 0.0670 | 0.7446 | 0.8051 | 0.7736 | 0.9738 |
| 0.0586 | 2.0 | 1564 | 0.0689 | 0.7689 | 0.8106 | 0.7892 | 0.9755 |
| 0.0123 | 3.0 | 2346 | 0.0715 | 0.7846 | 0.8076 | 0.7959 | 0.9750 |
| 0.0002 | 4.0 | 3128 | 0.0896 | 0.7942 | 0.8199 | 0.8068 | 0.9767 |
| 0.0004 | 5.0 | 3910 | 0.1119 | 0.7971 | 0.8201 | 0.8084 | 0.9765 |
| 0.0004 | 6.0 | 4692 | 0.1192 | 0.7966 | 0.8337 | 0.8147 | 0.9768 |
| 0.013 | 7.0 | 5474 | 0.1274 | 0.7932 | 0.8266 | 0.8095 | 0.9773 |
| 0.0236 | 8.0 | 6256 | 0.1419 | 0.7976 | 0.8213 | 0.8093 | 0.9771 |
| 0.0004 | 9.0 | 7038 | 0.1519 | 0.8004 | 0.8261 | 0.8130 | 0.9772 |
| 0.0 | 10.0 | 7820 | 0.1528 | 0.7988 | 0.8243 | 0.8114 | 0.9772 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
israel/byt5_en_am
|
israel
| 2022-10-26T10:10:40Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"am",
"dataset:sample",
"license:cc-by-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-25T09:31:06Z |
---
language:
- am
datasets:
- sample
license: cc-by-4.0
---
|
NchuNLP/Legal-Document-Question-Answering
|
NchuNLP
| 2022-10-26T09:45:48Z | 178 | 5 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"zh",
"dataset:LegalDocumentDataset",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-17T08:21:34Z |
---
language: zh
datasets:
- LegalDocumentDataset
---
# bert-base-chinese for QA
This is the [bert-base-chinese](https://huggingface.co/bert-base-chinese) model, fine-tuned using the Legal Document Dataset. It's been trained on question-answer pairs for the task of Question Answering.
## Usage
### In Transformers
```python
from transformers import BertTokenizerFast, BertForQuestionAnswering, pipeline
model_name = "NchuNLP/Legal-Document-Question-Answering"
tokenizer = BertTokenizerFast.from_pretrained(model_name)
model = BertForQuestionAnswering.from_pretrained(model_name)
# a) Get predictions
nlp = pipeline('question-answering', model=model, tokenizer=tokenizer)
QA_input = {
'question': '被告人偽造了什麼文書?',
'context': '犯罪事實一、韓金虎在采豐開發有限公司（址設臺北市○○區○○路0段000巷00○0號，下稱采豐公司）擔任臨時派遣員工，詎其竟意圖為自己不法之所有，基於行使偽造私文書、詐欺取財等犯意，於民國110年9月2日下午5時20分前某時許，在不詳地點，在采豐公司所使用之空白工作確認單中主任簽名欄上偽簽謝宏奇之簽名，佯裝其有於110年9月1日到班工作，並經工地主任確認之意，提出與采豐公司主任曾子昕而行使之，曾子昕因見該份工作確認單上有謝奇宏之簽名，因陷於錯誤而信韓金虎確實有於110年9月1日到班工作，准發薪資新臺幣（下同）2,000元給韓金虎，足生損害於采豐公司。嗣曾子昕於110年9月3日上午11時20分許，發現工作確認單點交數量有異，遂報警處理，始悉上情。二、案經曾子昕訴由臺北市政府警察局萬華分局報告偵辦。'
}
res = nlp(QA_input)
```
## Authors
**Kei Yu Heish:** iove22@hotmail.com
**Yao-Chung Fan:** yfan@nchu.edu.tw
## About us
[中興大學自然語言處理實驗室](https://nlpnchu.org/)研究方向圍繞於深度學習技術在文字資料探勘 (Text Mining) 與自然語言處理 (Natural Language Processing) 方面之研究，目前實驗室成員的研究主題著重於機器閱讀理解 (Machine Reading Comprehension) 以及自然語言生成 (Natural Language Generation) 兩面向。
## More Information
<p>For more info about Nchu NLP Lab, visit our <strong><a href="https://demo.nlpnchu.org/">Lab Online Demo</a></strong> repo and <strong><a href="https://github.com/NCHU-NLP-Lab">GitHub</a></strong>.
|
biu-nlp/lingmess-coref
|
biu-nlp
| 2022-10-26T08:55:32Z | 3,558 | 10 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"coreference-resolution",
"en",
"dataset:ontonotes",
"arxiv:2205.12644",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2022-06-09T19:05:32Z |
---
language:
- en
tags:
- coreference-resolution
license: mit
datasets:
- ontonotes
metrics:
- CoNLL
task_categories:
- coreference-resolution
model-index:
- name: biu-nlp/lingmess-coref
results:
- task:
type: coreference-resolution
name: coreference-resolution
dataset:
name: ontonotes
type: coreference
metrics:
- name: Avg. F1
type: CoNLL
value: 81.4
---
## LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution
[LingMess](https://arxiv.org/abs/2205.12644) is a linguistically motivated categorization of mention-pairs into 6 types of coreference decisions and learn a dedicated trainable scoring function for each category. This significantly improves the accuracy of the pairwise scorer as well as of the overall coreference performance on the English Ontonotes coreference corpus.
Please check the [official repository](https://github.com/shon-otmazgin/lingmess-coref) for more details and updates.
#### Training on OntoNotes
We present the test results on OntoNotes 5.0 dataset.
| Model | Avg. F1 |
|---------------------------------|---------|
| SpanBERT-large + e2e | 79.6 |
| Longformer-large + s2e | 80.3 |
| **Longformer-large + LingMess** | 81.4 |
### Citation
If you find LingMess useful for your work, please cite the following paper:
``` latex
@misc{https://doi.org/10.48550/arxiv.2205.12644,
doi = {10.48550/ARXIV.2205.12644},
url = {https://arxiv.org/abs/2205.12644},
author = {Otmazgin, Shon and Cattan, Arie and Goldberg, Yoav},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {LingMess: Linguistically Informed Multi Expert Scorers for Coreference Resolution},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
|
philadelphiacredit/Credit-Repair-Philadelphia
|
philadelphiacredit
| 2022-10-26T08:34:03Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-10-26T08:32:38Z |
We’re not your average credit repair firm, we truly care, so we only charge for the items we pursue on your report. Not only does this make us one of the FASTEST credit restoration companies, but we’re also one of the most affordable.
We offer FREE consultations, evaluations, and credit education. Our process only takes 30-60 days and we offer a 100% MONEY-BACK GUARANTEE on almost all our services.
Follow this [link](https://philadelphia.asapcreditrepairusa.com/)
|
TaoH/st-norms2
|
TaoH
| 2022-10-26T08:22:56Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-10-26T08:13:56Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 765 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 765,
"warmup_steps": 77,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
GV05/distilbert-base-uncased-finetuned-emotion
|
GV05
| 2022-10-26T07:56:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T07:18:51Z |
---
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: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9244695413548749
---
<!-- 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: 0.2144
- Accuracy: 0.9245
- F1: 0.9245
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8227 | 1.0 | 250 | 0.3150 | 0.902 | 0.8992 |
| 0.246 | 2.0 | 500 | 0.2144 | 0.9245 | 0.9245 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
sd-concepts-library/alicebeta
|
sd-concepts-library
| 2022-10-26T07:44:34Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-10-26T07:44:30Z |
---
license: mit
---
### AliceBeta on Stable Diffusion
This is the `<Alice-style>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
doodlevelyn/bert-base-cased
|
doodlevelyn
| 2022-10-26T07:29:26Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-26T02:32:02Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-cased
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-cased
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4145
- Precision: 0.4029
- Recall: 0.2740
- F1: 0.3262
- Accuracy: 0.9602
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0002 | 1.0 | 7365 | 0.3903 | 0.4151 | 0.2241 | 0.2911 | 0.9574 |
| 0.0003 | 2.0 | 14730 | 0.4288 | 0.3681 | 0.2006 | 0.2597 | 0.9580 |
| 0.0 | 3.0 | 22095 | 0.4145 | 0.4029 | 0.2740 | 0.3262 | 0.9602 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
LejlaKantar/ORGO
|
LejlaKantar
| 2022-10-26T07:21:13Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-10-26T07:21:13Z |
---
license: bigscience-bloom-rail-1.0
---
|
sania-nawaz/finetuning-sentiment-model-3000-samples
|
sania-nawaz
| 2022-10-26T06:15:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T06:04:29Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8666666666666667
- name: F1
type: f1
value: 0.8666666666666667
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3286
- Accuracy: 0.8667
- F1: 0.8667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
debbiesoon/bart_large_summarise_v2
|
debbiesoon
| 2022-10-26T05:22:32Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:multi_news",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-22T16:30:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- multi_news
metrics:
- rouge
model-index:
- name: bart_large_summarise_v2
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: multi_news
type: multi_news
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 39.305
---
<!-- 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. -->
# bart_large_summarise_v2
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on the multi_news dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2988
- Rouge1: 39.305
- Rouge2: 13.4171
- Rougel: 20.4214
- Rougelsum: 34.971
- Gen Len: 142.0
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.2.dev0
- Tokenizers 0.13.1
|
huggingtweets/kubiekit
|
huggingtweets
| 2022-10-26T05:03:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T04:57:38Z |
---
language: en
thumbnail: http://www.huggingtweets.com/kubiekit/1666760547210/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1581568862616662016/XxeL1VBT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">kubie</div>
<div style="text-align: center; font-size: 14px;">@kubiekit</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from kubie.
| Data | kubie |
| --- | --- |
| Tweets downloaded | 3136 |
| Retweets | 180 |
| Short tweets | 611 |
| Tweets kept | 2345 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2mv38hcu/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kubiekit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1uk7te5z) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1uk7te5z/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/kubiekit')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
JamesH/Movie_review_sentiment_analysis_model
|
JamesH
| 2022-10-26T01:02:13Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"text-classification",
"en",
"dataset:JamesH/autotrain-data-third-project",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-26T00:58:53Z |
---
tags:
- autotrain
- text-classification
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- JamesH/autotrain-data-third-project
co2_eq_emissions:
emissions: 6.9919208994196795
---
# Model Trained Using AutoTrain
- Problem type: Binary Classification
- Model ID: 1883864250
- CO2 Emissions (in grams): 6.9919
## Validation Metrics
- Loss: 0.175
- Accuracy: 0.950
- Precision: 0.950
- Recall: 0.950
- AUC: 0.986
- F1: 0.950
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/JamesH/autotrain-third-project-1883864250
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("JamesH/autotrain-third-project-1883864250", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("JamesH/autotrain-third-project-1883864250", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
huggingtweets/tommyinnit
|
huggingtweets
| 2022-10-26T00:11:06Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/tommyinnit/1666743061515/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1535706274049957888/4PfG6S0y_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">TommyInnit</div>
<div style="text-align: center; font-size: 14px;">@tommyinnit</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from TommyInnit.
| Data | TommyInnit |
| --- | --- |
| Tweets downloaded | 3213 |
| Retweets | 2 |
| Short tweets | 464 |
| Tweets kept | 2747 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/376p2x9n/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @tommyinnit's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2w3jxzqd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2w3jxzqd/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/tommyinnit')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
yohein/distilbert-base-uncased-finetuned-squad
|
yohein
| 2022-10-25T23:42:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-25T22:51:05Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
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-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1683
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.2264 | 1.0 | 5533 | 1.1663 |
| 0.9606 | 2.0 | 11066 | 1.1288 |
| 0.7432 | 3.0 | 16599 | 1.1683 |
### Framework versions
- Transformers 4.23.0
- Pytorch 1.12.1
- Datasets 2.5.2
- Tokenizers 0.13.1
|
redevaaa/test4
|
redevaaa
| 2022-10-25T23:20:58Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:ner",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-10-25T22:53:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- ner
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: test4
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: ner
type: ner
config: default
split: train
args: default
metrics:
- name: Precision
type: precision
value: 0.594855305466238
- name: Recall
type: recall
value: 0.6423611111111112
- name: F1
type: f1
value: 0.6176961602671119
- name: Accuracy
type: accuracy
value: 0.9579571605593911
---
<!-- 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. -->
# test4
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the ner dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3100
- Precision: 0.5949
- Recall: 0.6424
- F1: 0.6177
- Accuracy: 0.9580
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 418 | 0.2052 | 0.2415 | 0.2465 | 0.2440 | 0.9423 |
| 0.3341 | 2.0 | 836 | 0.1816 | 0.4286 | 0.4792 | 0.4525 | 0.9513 |
| 0.1296 | 3.0 | 1254 | 0.2039 | 0.4589 | 0.5035 | 0.4801 | 0.9526 |
| 0.0727 | 4.0 | 1672 | 0.2130 | 0.5237 | 0.5764 | 0.5488 | 0.9566 |
| 0.0553 | 5.0 | 2090 | 0.2290 | 0.5171 | 0.5764 | 0.5452 | 0.9551 |
| 0.0412 | 6.0 | 2508 | 0.2351 | 0.5390 | 0.5521 | 0.5455 | 0.9555 |
| 0.0412 | 7.0 | 2926 | 0.2431 | 0.5280 | 0.5903 | 0.5574 | 0.9542 |
| 0.0321 | 8.0 | 3344 | 0.2490 | 0.5825 | 0.625 | 0.6030 | 0.9570 |
| 0.0249 | 9.0 | 3762 | 0.2679 | 0.5764 | 0.5764 | 0.5764 | 0.9573 |
| 0.0192 | 10.0 | 4180 | 0.2574 | 0.5506 | 0.6042 | 0.5762 | 0.9558 |
| 0.0206 | 11.0 | 4598 | 0.2857 | 0.5498 | 0.5938 | 0.5710 | 0.9559 |
| 0.0147 | 12.0 | 5016 | 0.2638 | 0.5548 | 0.5972 | 0.5753 | 0.9550 |
| 0.0147 | 13.0 | 5434 | 0.2771 | 0.5677 | 0.5972 | 0.5821 | 0.9577 |
| 0.0129 | 14.0 | 5852 | 0.3016 | 0.5761 | 0.6181 | 0.5963 | 0.9549 |
| 0.0118 | 15.0 | 6270 | 0.3055 | 0.5587 | 0.6111 | 0.5837 | 0.9570 |
| 0.0099 | 16.0 | 6688 | 0.2937 | 0.5682 | 0.6076 | 0.5872 | 0.9564 |
| 0.0099 | 17.0 | 7106 | 0.3075 | 0.5313 | 0.6181 | 0.5714 | 0.9531 |
| 0.0085 | 18.0 | 7524 | 0.3079 | 0.6026 | 0.6424 | 0.6218 | 0.9580 |
| 0.0085 | 19.0 | 7942 | 0.3082 | 0.5833 | 0.6319 | 0.6067 | 0.9572 |
| 0.0074 | 20.0 | 8360 | 0.3100 | 0.5949 | 0.6424 | 0.6177 | 0.9580 |
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
vumichien/trillsson3-ft-keyword-spotting-14
|
vumichien
| 2022-10-25T22:36:41Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"trillsson_efficient",
"text-classification",
"audio-classification",
"generated_from_trainer",
"dataset:superb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
audio-classification
| 2022-10-25T14:40:22Z |
---
tags:
- audio-classification
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: trillsson3-ft-keyword-spotting-14
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. -->
# trillsson3-ft-keyword-spotting-14
This model is a fine-tuned version of [vumichien/nonsemantic-speech-trillsson3](https://huggingface.co/vumichien/nonsemantic-speech-trillsson3) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3015
- Accuracy: 0.9150
## 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: 64
- seed: 0
- 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_ratio: 0.1
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2824 | 1.0 | 1597 | 0.7818 | 0.6892 |
| 0.8003 | 2.0 | 3194 | 0.4443 | 0.8735 |
| 0.7232 | 3.0 | 4791 | 0.3728 | 0.8833 |
| 0.73 | 4.0 | 6388 | 0.3465 | 0.8973 |
| 0.7015 | 5.0 | 7985 | 0.3211 | 0.9109 |
| 0.6981 | 6.0 | 9582 | 0.3200 | 0.9081 |
| 0.6807 | 7.0 | 11179 | 0.3209 | 0.9059 |
| 0.6873 | 8.0 | 12776 | 0.3206 | 0.9022 |
| 0.6416 | 9.0 | 14373 | 0.3124 | 0.9057 |
| 0.6698 | 10.0 | 15970 | 0.3288 | 0.8950 |
| 0.716 | 11.0 | 17567 | 0.3147 | 0.8998 |
| 0.6514 | 12.0 | 19164 | 0.3034 | 0.9112 |
| 0.6513 | 13.0 | 20761 | 0.3091 | 0.9092 |
| 0.652 | 14.0 | 22358 | 0.3056 | 0.9100 |
| 0.7105 | 15.0 | 23955 | 0.3015 | 0.9150 |
| 0.6337 | 16.0 | 25552 | 0.3070 | 0.9091 |
| 0.63 | 17.0 | 27149 | 0.3018 | 0.9135 |
| 0.6672 | 18.0 | 28746 | 0.3084 | 0.9088 |
| 0.6479 | 19.0 | 30343 | 0.3060 | 0.9101 |
| 0.6658 | 20.0 | 31940 | 0.3072 | 0.9089 |
### Framework versions
- Transformers 4.23.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/ok_0s
|
huggingtweets
| 2022-10-25T20:20:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-25T20:18:48Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ok_0s/1666729242111/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1575869051850612737/Hz2LIceC_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">⓪𝕊 is minting Youts</div>
<div style="text-align: center; font-size: 14px;">@ok_0s</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from ⓪𝕊 is minting Youts.
| Data | ⓪𝕊 is minting Youts |
| --- | --- |
| Tweets downloaded | 1390 |
| Retweets | 132 |
| Short tweets | 287 |
| Tweets kept | 971 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/11ejsejg/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @ok_0s's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1z3prl6a) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1z3prl6a/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/ok_0s')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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