modelId
string | author
string | last_modified
timestamp[us, tz=UTC] | downloads
int64 | likes
int64 | library_name
string | tags
sequence | pipeline_tag
string | createdAt
timestamp[us, tz=UTC] | card
string |
|---|---|---|---|---|---|---|---|---|---|
sultan/BioM-ALBERT-xxlarge-SQuAD2 | sultan | 2021-08-10T21:59:59Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"albert",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.
# Model Description
This model is fine-tuned on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ALBERT-xxlarge. This model (TensorFlow version ) took the lead in the BioASQ9b-Factoid challenge under the name of (UDEL-LAB1).
If you want to try our Tensor Flow example and how to fine-tune ALBERT on SQuAD and BioASQ follow this link :
https://github.com/salrowili/BioM-Transformers/blob/main/examples/Example_of_SQuAD2_0_and_BioASQ7B_tasks_with_BioM_ALBERT_xxlarge_on_TPU.ipynb
To see the full details of BioASQ9B results, please check this link http://participants-area.bioasq.org/results/9b/phaseB/ ( you need to register).
Huggingface library doesn't implement the Layer-Wise decay feature, which affects the performance on the SQuAD task. The reported result of BioM-ALBERT-xxlarge-SQuAD in our paper is 87.00 (F1) since we use ALBERT open-source code with TF checkpoint, which uses Layer-Wise decay.
Result with PyTorch and V100 GPU
```
***** eval metrics *****
HasAns_exact = 77.6484
HasAns_f1 = 85.0136
HasAns_total = 5928
NoAns_exact = 86.577
NoAns_f1 = 86.577
NoAns_total = 5945
best_exact = 82.1191
best_exact_thresh = 0.0
best_f1 = 85.7964
best_f1_thresh = 0.0
eval_samples = 12551
exact = 82.1191
f1 = 85.7964
total = 11873
```
To reproduce results in Google Colab:
- Make sure you have GPU enabled.
- Clone and install required libraries through this code
!git clone https://github.com/huggingface/transformers
!pip3 install -e transformers
!pip3 install sentencepiece
!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
- Run this python code:
```python
python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path BioM-ALBERT-xxlarge-SQuAD2 \
--do_eval \
--version_2_with_negative \
--per_device_eval_batch_size 8 \
--dataset_name squad_v2 \
--overwrite_output_dir \
--fp16 \
--output_dir out
```
You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
# Acknowledgment
We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
# Citation
```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
``` |
huggingtweets/benioff | huggingtweets | 2021-08-10T21:45:46Z | 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: 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/1421907606105329672/ypXqcYtY_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">Marc Benioff</div>
<div style="text-align: center; font-size: 14px;">@benioff</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 Marc Benioff.
| Data | Marc Benioff |
| --- | --- |
| Tweets downloaded | 3242 |
| Retweets | 2645 |
| Short tweets | 67 |
| Tweets kept | 530 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1jmodnqz/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 @benioff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/alpvpdqh) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/alpvpdqh/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/benioff')
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)
|
ricardo-filho/sbertimbau-base-allnli-mnrl | ricardo-filho | 2021-08-10T21:09:32Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
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, max 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 8066 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 806,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 807,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, '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 --> |
huggingtweets/frobenis | huggingtweets | 2021-08-10T17:36:52Z | 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: https://www.huggingtweets.com/frobenis/1628616938616/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/1424095619061141504/0FhWxHzI_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">frobenis</div>
<div style="text-align: center; font-size: 14px;">@frobenis</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 frobenis.
| Data | frobenis |
| --- | --- |
| Tweets downloaded | 245 |
| Retweets | 1 |
| Short tweets | 62 |
| Tweets kept | 182 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1c5hws47/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 @frobenis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3ee5bpsa) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3ee5bpsa/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/frobenis')
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)
|
huggingartists/placebo | huggingartists | 2021-08-10T17:26:47Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/placebo",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/placebo
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c7e467de49cab7cdcc1d52c9c95ccd47.931x931x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Placebo</div>
<a href="https://genius.com/artists/placebo">
<div style="text-align: center; font-size: 14px;">@placebo</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Placebo.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/placebo).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/placebo")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3jfcdfc1/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 Placebo's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/jx3r5x9o/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='huggingartists/placebo')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/placebo")
model = AutoModelWithLMHead.from_pretrained("huggingartists/placebo")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingtweets/thierrybaudet | huggingtweets | 2021-08-10T13:47:08Z | 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: https://www.huggingtweets.com/thierrybaudet/1628603223747/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/1414937194817626115/AxKSPREq_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">Thierry Baudet</div>
<div style="text-align: center; font-size: 14px;">@thierrybaudet</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 Thierry Baudet.
| Data | Thierry Baudet |
| --- | --- |
| Tweets downloaded | 3195 |
| Retweets | 2181 |
| Short tweets | 163 |
| Tweets kept | 851 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1xjn87z9/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 @thierrybaudet's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/wgh8o2kc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/wgh8o2kc/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/thierrybaudet')
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)
|
huggingtweets/gerardjoling | huggingtweets | 2021-08-10T13:38:50Z | 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: https://www.huggingtweets.com/gerardjoling/1628602714633/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/1362683032017244162/vjtrYSK1_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">Gerard Joling</div>
<div style="text-align: center; font-size: 14px;">@gerardjoling</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 Gerard Joling.
| Data | Gerard Joling |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 102 |
| Short tweets | 33 |
| Tweets kept | 3115 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/nnhwkwwc/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 @gerardjoling's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hq3zjug) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hq3zjug/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/gerardjoling')
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)
|
Dean/summarsiation | Dean | 2021-08-10T10:51:11Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:04Z | ---
Summarisation model summarsiation |
huggingartists/ghostemane | huggingartists | 2021-08-10T10:49:23Z | 7 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/ghostemane",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/ghostemane
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/c4407bb331c50916c1dfdc7f875f73a9.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Ghostemane</div>
<a href="https://genius.com/artists/ghostemane">
<div style="text-align: center; font-size: 14px;">@ghostemane</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Ghostemane.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/ghostemane).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/ghostemane")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/1ou29taa/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 Ghostemane's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/futdflju) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/futdflju/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='huggingartists/ghostemane')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/ghostemane")
model = AutoModelWithLMHead.from_pretrained("huggingartists/ghostemane")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
eugenesiow/pan-bam | eugenesiow | 2021-08-10T08:56:54Z | 6,461 | 1 | transformers | [
"transformers",
"PAN",
"super-image",
"image-super-resolution",
"dataset:eugenesiow/Div2k",
"dataset:eugenesiow/Set5",
"dataset:eugenesiow/Set14",
"dataset:eugenesiow/BSD100",
"dataset:eugenesiow/Urban100",
"arxiv:2010.01073",
"arxiv:2104.07566",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- super-image
- image-super-resolution
datasets:
- eugenesiow/Div2k
- eugenesiow/Set5
- eugenesiow/Set14
- eugenesiow/BSD100
- eugenesiow/Urban100
metrics:
- pnsr
- ssim
---
# Pixel Attention Network (PAN)
PAN model pre-trained on DIV2K (800 images training, augmented to 4000 images, 100 images validation) for 2x, 3x and 4x image super resolution. It was introduced in the paper [Efficient Image Super-Resolution Using Pixel Attention](https://arxiv.org/abs/2010.01073) by Zhao et al. (2020) and first released in [this repository](https://github.com/zhaohengyuan1/PAN).
The goal of image super resolution is to restore a high resolution (HR) image from a single low resolution (LR) image. The image below shows the ground truth (HR), the bicubic upscaling and model upscaling.
## Model description
The PAN model proposes a a lightweight convolutional neural network for image super resolution. Pixel attention (PA) is similar to channel attention and spatial attention in formulation. PA however produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results.
This model also applies the balanced attention (BAM) method invented by [Wang et al. (2021)](https://arxiv.org/abs/2104.07566) to further improve the results.
The model is very lightweight with the model being just 260k to 270k parameters (~1mb).
## Intended uses & limitations
You can use the pre-trained models for upscaling your images 2x, 3x and 4x. You can also use the trainer to train a model on your own dataset.
### How to use
The model can be used with the [super_image](https://github.com/eugenesiow/super-image) library:
```bash
pip install super-image
```
Here is how to use a pre-trained model to upscale your image:
```python
from super_image import PanModel, ImageLoader
from PIL import Image
import requests
url = 'https://paperswithcode.com/media/datasets/Set5-0000002728-07a9793f_zA3bDjj.jpg'
image = Image.open(requests.get(url, stream=True).raw)
model = PanModel.from_pretrained('eugenesiow/pan-bam', scale=2) # scale 2, 3 and 4 models available
inputs = ImageLoader.load_image(image)
preds = model(inputs)
ImageLoader.save_image(preds, './scaled_2x.png') # save the output 2x scaled image to `./scaled_2x.png`
ImageLoader.save_compare(inputs, preds, './scaled_2x_compare.png') # save an output comparing the super-image with a bicubic scaling
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Upscale_Images_with_Pretrained_super_image_Models.ipynb "Open in Colab")
## Training data
The models for 2x, 3x and 4x image super resolution were pretrained on [DIV2K](https://huggingface.co/datasets/eugenesiow/Div2k), a dataset of 800 high-quality (2K resolution) images for training, augmented to 4000 images and uses a dev set of 100 validation images (images numbered 801 to 900).
## Training procedure
### Preprocessing
We follow the pre-processing and training method of [Wang et al.](https://arxiv.org/abs/2104.07566).
Low Resolution (LR) images are created by using bicubic interpolation as the resizing method to reduce the size of the High Resolution (HR) images by x2, x3 and x4 times.
During training, RGB patches with size of 64×64 from the LR input are used together with their corresponding HR patches.
Data augmentation is applied to the training set in the pre-processing stage where five images are created from the four corners and center of the original image.
We need the huggingface [datasets](https://huggingface.co/datasets?filter=task_ids:other-other-image-super-resolution) library to download the data:
```bash
pip install datasets
```
The following code gets the data and preprocesses/augments the data.
```python
from datasets import load_dataset
from super_image.data import EvalDataset, TrainDataset, augment_five_crop
augmented_dataset = load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='train')\
.map(augment_five_crop, batched=True, desc="Augmenting Dataset") # download and augment the data with the five_crop method
train_dataset = TrainDataset(augmented_dataset) # prepare the train dataset for loading PyTorch DataLoader
eval_dataset = EvalDataset(load_dataset('eugenesiow/Div2k', 'bicubic_x4', split='validation')) # prepare the eval dataset for the PyTorch DataLoader
```
### Pretraining
The model was trained on GPU. The training code is provided below:
```python
from super_image import Trainer, TrainingArguments, PanModel, PanConfig
training_args = TrainingArguments(
output_dir='./results', # output directory
num_train_epochs=1000, # total number of training epochs
)
config = PanConfig(
scale=4, # train a model to upscale 4x
bam=True, # apply balanced attention to the network
)
model = PanModel(config)
trainer = Trainer(
model=model, # the instantiated model to be trained
args=training_args, # training arguments, defined above
train_dataset=train_dataset, # training dataset
eval_dataset=eval_dataset # evaluation dataset
)
trainer.train()
```
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Train_super_image_Models.ipynb "Open in Colab")
## Evaluation results
The evaluation metrics include [PSNR](https://en.wikipedia.org/wiki/Peak_signal-to-noise_ratio#Quality_estimation_with_PSNR) and [SSIM](https://en.wikipedia.org/wiki/Structural_similarity#Algorithm).
Evaluation datasets include:
- Set5 - [Bevilacqua et al. (2012)](https://huggingface.co/datasets/eugenesiow/Set5)
- Set14 - [Zeyde et al. (2010)](https://huggingface.co/datasets/eugenesiow/Set14)
- BSD100 - [Martin et al. (2001)](https://huggingface.co/datasets/eugenesiow/BSD100)
- Urban100 - [Huang et al. (2015)](https://huggingface.co/datasets/eugenesiow/Urban100)
The results columns below are represented below as `PSNR/SSIM`. They are compared against a Bicubic baseline.
|Dataset |Scale |Bicubic |pan-bam |
|--- |--- |--- |--- |
|Set5 |2x |33.64/0.9292 |**37.7/0.9596** |
|Set5 |3x |30.39/0.8678 |**34.62/0.9371** |
|Set5 |4x |28.42/0.8101 |**31.9/0.8911** |
|Set14 |2x |30.22/0.8683 |**33.4/0.9161** |
|Set14 |3x |27.53/0.7737 |**30.83/0.8545** |
|Set14 |4x |25.99/0.7023 |**28.54/0.7795** |
|BSD100 |2x |29.55/0.8425 |**33.6/0.9234** |
|BSD100 |3x |27.20/0.7382 |**29.47/0.8153** |
|BSD100 |4x |25.96/0.6672 |**28.32/0.7591** |
|Urban100 |2x |26.66/0.8408 |**31.35/0.92** |
|Urban100 |3x | |**28.64/0.861** |
|Urban100 |4x |23.14/0.6573 |**25.6/0.7691** |
You can find a notebook to easily run evaluation on pretrained models below:
[](https://colab.research.google.com/github/eugenesiow/super-image-notebooks/blob/master/notebooks/Evaluate_Pretrained_super_image_Models.ipynb "Open in Colab")
## BibTeX entry and citation info
```bibtex
@misc{wang2021bam,
title={BAM: A Lightweight and Efficient Balanced Attention Mechanism for Single Image Super Resolution},
author={Fanyi Wang and Haotian Hu and Cheng Shen},
year={2021},
eprint={2104.07566},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
```
```bibtex
@misc{zhao2020efficient,
title={Efficient Image Super-Resolution Using Pixel Attention},
author={Hengyuan Zhao and Xiangtao Kong and Jingwen He and Yu Qiao and Chao Dong},
year={2020},
eprint={2010.01073},
archivePrefix={arXiv},
primaryClass={eess.IV}
}
``` |
ayameRushia/gpt2-small-indonesia-fine-tuning-poem | ayameRushia | 2021-08-10T06:50:20Z | 6 | 5 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: id
widget:
- text: "Wahai rembulan yang tertutup awan hujan"
---
# Indonesian GPT-2 finetuned on Indonesian poems
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to Indonesian poems. The dataset can be found in [here](https://huggingface.co/datasets/id_puisi) All training was done on Google Colab Jupyter Notebook (soon).
The dataset is splitted into two subset with details belows:
| split | count (examples) | percentage |
| ---------- | ---------- | -------------- |
| train | 7,358 | 80% |
| validation | 1,890 | 20% |
### Evaluation results
The model evaluation results after 10 epochs are as follows:
| dataset | train/loss | eval/loss | eval perplexity |
| ---------- | ---------- | -------------- | ---------- |
| [id puisi](https://huggingface.co/datasets/id_puisi) | 3.324700 | 3.502665 | 33.20 |
The logs can be found in [wandb page here](https://wandb.ai/ayamerushia/gpt-2_poem/runs/36ymudz9/overview?workspace=user-ayamerushia) or tensorboard [here](https://huggingface.co/ayameRushia/gpt2-small-indonesia-fine-tuning-poem/tensorboard)
|
huggingartists/deep-purple | huggingartists | 2021-08-10T06:30:14Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/deep-purple",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/deep-purple
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/91b25ad26e90b71d04d42ccec0a46347.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Deep Purple</div>
<a href="https://genius.com/artists/deep-purple">
<div style="text-align: center; font-size: 14px;">@deep-purple</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Deep Purple.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/deep-purple).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/deep-purple")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/2sybcajo/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 Deep Purple's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/3evu15qv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/3evu15qv/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='huggingartists/deep-purple')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/deep-purple")
model = AutoModelWithLMHead.from_pretrained("huggingartists/deep-purple")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/chester-bennington | huggingartists | 2021-08-10T05:47:50Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/chester-bennington",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/chester-bennington
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/3853f38429e3cd0278c2b5b6307b9e92.752x752x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Chester Bennington</div>
<a href="https://genius.com/artists/chester-bennington">
<div style="text-align: center; font-size: 14px;">@chester-bennington</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Chester Bennington.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/chester-bennington).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/chester-bennington")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3pq3bd6d/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 Chester Bennington's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1sxpshrc) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1sxpshrc/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='huggingartists/chester-bennington')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/chester-bennington")
model = AutoModelWithLMHead.from_pretrained("huggingartists/chester-bennington")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
tupleblog/generate-thai-lyrics | tupleblog | 2021-08-09T23:06:14Z | 22 | 4 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"th",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- th
widget:
- text: "ความรัก"
- text: "อยากรู้"
- text: "ไหนว่า"
---
# Generate Thai Lyrics (แต่งเพลงไทยด้วย GPT-2)
GPT-2 for Thai lyrics generation. We use [GPT-2 base Thai](https://huggingface.co/flax-community/gpt2-base-thai) as a pre-trained model
for [Siamzone lyrics](https://www.siamzone.com/music/thailyric/)
เราเทรนโมเดล GPT-2 สำหรับใช้แต่งเนื้อเพลงไทยด้วยเนื้อเพลงจากเว็บไซต์ Siamzone
## Example use
``` py
from transformers import pipeline
from transformers import GPT2Model, GPT2TokenizerFast, AutoModelForCausalLM, AutoTokenizer
model_name = "tupleblog/generate-thai-lyrics"
model = AutoModelForCausalLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model.config.pad_token_id = model.config.eos_token_id
nlp = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer
)
text = "ความรัก"
nlp(text, max_length=100, top_k=40, temperature=0.8) # varying the temperature and top-k produce different output
```
|
sibyl/BART-commongen | sibyl | 2021-08-09T22:24:43Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:gem",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- gem
model_index:
- name: BART-commongen
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: gem
type: gem
args: common_gen
---
<!-- 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-commongen
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the gem dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1263
- Spice: 0.4178
## 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: 1000
- training_steps: 6317
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spice |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 9.0971 | 0.05 | 100 | 4.1336 | 0.3218 |
| 3.5348 | 0.09 | 200 | 1.5467 | 0.3678 |
| 1.5099 | 0.14 | 300 | 1.1280 | 0.3821 |
| 1.2395 | 0.19 | 400 | 1.1178 | 0.3917 |
| 1.1827 | 0.24 | 500 | 1.0919 | 0.4086 |
| 1.1545 | 0.28 | 600 | 1.1028 | 0.4035 |
| 1.1363 | 0.33 | 700 | 1.1021 | 0.4187 |
| 1.1156 | 0.38 | 800 | 1.1231 | 0.4103 |
| 1.1077 | 0.43 | 900 | 1.1221 | 0.4117 |
| 1.0964 | 0.47 | 1000 | 1.1169 | 0.4088 |
| 1.0704 | 0.52 | 1100 | 1.1143 | 0.4133 |
| 1.0483 | 0.57 | 1200 | 1.1085 | 0.4058 |
| 1.0556 | 0.62 | 1300 | 1.1059 | 0.4249 |
| 1.0343 | 0.66 | 1400 | 1.0992 | 0.4102 |
| 1.0123 | 0.71 | 1500 | 1.1126 | 0.4104 |
| 1.0108 | 0.76 | 1600 | 1.1140 | 0.4177 |
| 1.005 | 0.81 | 1700 | 1.1264 | 0.4078 |
| 0.9822 | 0.85 | 1800 | 1.1256 | 0.4158 |
| 0.9918 | 0.9 | 1900 | 1.1345 | 0.4118 |
| 0.9664 | 0.95 | 2000 | 1.1087 | 0.4073 |
| 0.9532 | 1.0 | 2100 | 1.1217 | 0.4063 |
| 0.8799 | 1.04 | 2200 | 1.1229 | 0.4115 |
| 0.8665 | 1.09 | 2300 | 1.1263 | 0.4178 |
### Framework versions
- Transformers 4.9.2
- Pytorch 1.9.0+cu102
- Datasets 1.11.1.dev0
- Tokenizers 0.10.3
|
ricardo-filho/bertimbau_base_snli_mnrl | ricardo-filho | 2021-08-09T21:01:02Z | 10 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
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, max 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 4059 with parameters:
```
{'batch_size': 64}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 405,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 406,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 75, '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 --> |
rsedlr/RickBotExample | rsedlr | 2021-08-09T15:51:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- conversational
---
# RickBot built for [Chai](https://chai.ml/)
Make your own [here](https://colab.research.google.com/drive/1o5LxBspm-C28HQvXN-PRQavapDbm5WjG?usp=sharing)
|
huggingtweets/honeytech | huggingtweets | 2021-08-09T15:38:21Z | 5 | 1 | 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: https://www.huggingtweets.com/honeytech/1628523497653/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/1385156301194366977/bNgzDBDI_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">Honey Singh</div>
<div style="text-align: center; font-size: 14px;">@honeytech</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 Honey Singh.
| Data | Honey Singh |
| --- | --- |
| Tweets downloaded | 3247 |
| Retweets | 473 |
| Short tweets | 422 |
| Tweets kept | 2352 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/i4rpk84l/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 @honeytech's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1r4kueus) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1r4kueus/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/honeytech')
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)
|
huggingartists/kehlani | huggingartists | 2021-08-09T11:15:09Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/kehlani",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/kehlani
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/a77a2cb56da25c8f9e895bc1df12252b.750x750x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Kehlani</div>
<a href="https://genius.com/artists/kehlani">
<div style="text-align: center; font-size: 14px;">@kehlani</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Kehlani.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/kehlani).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/kehlani")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/3t2b2m5y/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 Kehlani's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/35pweb11/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='huggingartists/kehlani')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/kehlani")
model = AutoModelWithLMHead.from_pretrained("huggingartists/kehlani")
```
## 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
eli4s/Bert-L12-h384-A6 | eli4s | 2021-08-09T10:59:08Z | 15 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 384 (half the hidden size of BERT) and 6 attention heads (hence the same head size of BERT).
The knowledge distillation was performed using multiple loss functions.
The weights of the model were initialized from scratch.
PS : the tokenizer is the same as the one of the model bert-base-uncased.
To load the model \& tokenizer :
````python
from transformers import AutoModelForMaskedLM, BertTokenizer
model_name = "eli4s/Bert-L12-h384-A6"
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
````
To use it on a sentence :
````python
import torch
sentence = "Let's have a [MASK]."
model.eval()
inputs = tokenizer([sentence], padding='longest', return_tensors='pt')
output = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
mask_index = inputs['input_ids'].tolist()[0].index(103)
masked_token = output['logits'][0][mask_index].argmax(axis=-1)
predicted_token = tokenizer.decode(masked_token)
print(predicted_token)
````
Or we can also predict the n most relevant predictions :
````python
top_n = 5
vocab_size = model.config.vocab_size
logits = output['logits'][0][mask_index].tolist()
top_tokens = sorted(list(range(vocab_size)), key=lambda i:logits[i], reverse=True)[:top_n]
tokenizer.decode(top_tokens)
````
|
huggingtweets/pixelatedboat-theonion | huggingtweets | 2021-08-09T06:08:58Z | 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: https://www.huggingtweets.com/pixelatedboat-theonion/1628489334285/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/875392068125769732/yrN-1k0Y_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/875489341291675649/hc8K1aT0_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">The Onion & pixelatedboat aka “mr tweets”</div>
<div style="text-align: center; font-size: 14px;">@pixelatedboat-theonion</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 The Onion & pixelatedboat aka “mr tweets”.
| Data | The Onion | pixelatedboat aka “mr tweets” |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3232 |
| Retweets | 7 | 568 |
| Short tweets | 12 | 452 |
| Tweets kept | 3231 | 2212 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2fjz8nxl/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 @pixelatedboat-theonion's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1demzwz8) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1demzwz8/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/pixelatedboat-theonion')
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)
|
huggingtweets/jslez | huggingtweets | 2021-08-09T05:52:09Z | 5 | 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: https://www.huggingtweets.com/jslez/1628488325525/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/1031385370166878208/b9LXsWIs_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">James Slezak</div>
<div style="text-align: center; font-size: 14px;">@jslez</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 James Slezak.
| Data | James Slezak |
| --- | --- |
| Tweets downloaded | 3237 |
| Retweets | 982 |
| Short tweets | 463 |
| Tweets kept | 1792 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/24ymhrpt/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 @jslez's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3kp7mpcw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3kp7mpcw/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/jslez')
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)
|
Davlan/byt5-base-yor-eng-mt | Davlan | 2021-08-08T21:58:46Z | 4 | 2 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2103.08647",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:04Z | Hugging Face's logo
---
language:
- yo
- en
datasets:
- JW300 + [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
---
# byt5-base-yor-eng-mt
## Model description
**byt5-base-yor-eng-mt** is a **machine translation** model from Yorùbá language to English language based on a fine-tuned byt5-base model. It establishes a **strong baseline** for automatically translating texts from Yorùbá to English.
Specifically, this model is a *byt5-base* model that was fine-tuned on JW300 Yorùbá corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt)
#### Limitations and bias
This model is limited by its training dataset. This may not generalize well for all use cases in different domains.
## Training data
This model was fine-tuned on on JW300 corpus and [Menyo-20k](https://huggingface.co/datasets/menyo20k_mt) dataset
## Training procedure
This model was trained on NVIDIA V100 GPU
## Eval results on Test set (BLEU score)
Fine-tuning byt5-base achieves 14.05 BLEU on [Menyo-20k test set](https://arxiv.org/abs/2103.08647) while mt5-base achieves 15.57
### BibTeX entry and citation info
By David Adelani
```
```
|
KamSut/distilbert-base-uncased-finetuned-ner | KamSut | 2021-08-08T16:51:51Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9836370279759162
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0604
- Precision: 0.9271
- Recall: 0.9381
- F1: 0.9326
- Accuracy: 0.9836
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2324 | 1.0 | 878 | 0.0688 | 0.9146 | 0.9264 | 0.9205 | 0.9816 |
| 0.0517 | 2.0 | 1756 | 0.0620 | 0.9207 | 0.9329 | 0.9268 | 0.9829 |
| 0.0301 | 3.0 | 2634 | 0.0604 | 0.9271 | 0.9381 | 0.9326 | 0.9836 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
kamalkraj/deberta-base | kamalkraj | 2021-08-08T09:12:57Z | 264 | 0 | transformers | [
"transformers",
"tf",
"deberta",
"feature-extraction",
"deberta-v1",
"en",
"arxiv:2006.03654",
"license:mit",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
language: en
tags: deberta-v1
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
license: mit
---
## DeBERTa: Decoding-enhanced BERT with Disentangled Attention
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. It outperforms BERT and RoBERTa on majority of NLU tasks with 80GB training data.
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more details and updates.
#### Fine-tuning on NLU tasks
We present the dev results on SQuAD 1.1/2.0 and MNLI tasks.
| Model | SQuAD 1.1 | SQuAD 2.0 | MNLI-m |
|-------------------|-----------|-----------|--------|
| RoBERTa-base | 91.5/84.6 | 83.7/80.5 | 87.6 |
| XLNet-Large | -/- | -/80.2 | 86.8 |
| **DeBERTa-base** | 93.1/87.2 | 86.2/83.1 | 88.8 |
### Citation
If you find DeBERTa useful for your work, please cite the following paper:
``` latex
@inproceedings{
he2021deberta,
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
booktitle={International Conference on Learning Representations},
year={2021},
url={https://openreview.net/forum?id=XPZIaotutsD}
}
```
|
huggingtweets/porns_xx | huggingtweets | 2021-08-07T13:34:18Z | 110 | 19 | 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: https://www.huggingtweets.com/porns_xx/1628343064919/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/1423389132508782593/Meo5eDzd_400x400.png')">
</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">PORN HUB 🔞</div>
<div style="text-align: center; font-size: 14px;">@porns_xx</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 PORN HUB 🔞.
| Data | PORN HUB 🔞 |
| --- | --- |
| Tweets downloaded | 1399 |
| Retweets | 0 |
| Short tweets | 7 |
| Tweets kept | 1392 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/200x5dgt/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 @porns_xx's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ha11ly3) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ha11ly3/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/porns_xx')
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)
|
gfdgdfgdg/arap_qa_bert | gfdgdfgdg | 2021-08-07T02:00:01Z | 10 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"question-answering",
"ar",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language:
- ar
widget:
- text: "أين يعيش محمد ؟"
context: "اسمي محمد وأنا أعيش في سوريا"
- text: "ما العدد الذري للهيدروجين ؟"
context: "الهيدروجين هو عنصر كيميائي عدده الذري 1 ، وهو غاز عديم الرائحة واللون وهو سريع الاشتعال"
- text: "ما خواص الهيدروجين ؟"
context: "الهيدروجين هو عنصر كيميائي عدده الذري 1 ، وهو غاز عديم الرائحة واللون وهو سريع الاشتعال"
---
|
superb/finetuned-model-upload-template | superb | 2021-08-06T23:59:32Z | 0 | 0 | null | [
"region:us"
] | null | 2022-03-02T23:29:05Z | # Fine-tuned Model Submission Template
This is a template reprository for the SUPERB benchmark for the _fine-tuned model_ category. In this category, participants are asked to fine-tuned a pretrained model in each of SUPERB's downstream tasks and then store the model weights and hyperparameters in this repo.
There are four steps involved in making a submission:
1. Fine-tune a pretrained model on a downstream task.
2. Implement the `PreTrainedModel` interface defined in each `model.py` module.
3. Store the weights and hyperparameters in the task directory
4. Push all the files to the Hugging Face Hub. |
sultan/BioM-ELECTRA-Large-SQuAD2 | sultan | 2021-08-06T22:27:10Z | 32 | 10 | transformers | [
"transformers",
"pytorch",
"electra",
"question-answering",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | # BioM-Transformers: Building Large Biomedical Language Models with BERT, ALBERT and ELECTRA
# Abstract
The impact of design choices on the performance
of biomedical language models recently
has been a subject for investigation. In
this paper, we empirically study biomedical
domain adaptation with large transformer models
using different design choices. We evaluate
the performance of our pretrained models
against other existing biomedical language
models in the literature. Our results show that
we achieve state-of-the-art results on several
biomedical domain tasks despite using similar
or less computational cost compared to other
models in the literature. Our findings highlight
the significant effect of design choices on
improving the performance of biomedical language
models.
# Model Description
We fine-tuned BioM-ELECTRA-Large, which was pre-trained on PubMed Abstracts, on the SQuAD2.0 dataset. Fine-tuning the biomedical language model on the SQuAD dataset helps improve the score on the BioASQ challenge. If you plan to work with BioASQ or biomedical QA tasks, it's better to use this model over BioM-ELECTRA-Large. This model (TensorFlow version ) took the lead in the BioASQ9b-Factoid challenge (Batch 5) under the name of (UDEL-LAB2). To see the full details of BioASQ9B results, please check this link http://participants-area.bioasq.org/results/9b/phaseB/ ( you need to register).
Huggingface library doesn't implement Layer-Wise decay feature, which affects the performance on SQuAD task. The reported result of BioM-ELECTRA-SQuAD in our paper is 88.3 (F1) since we use ELECTRA open-source code with TF checkpoint, which uses Layer-Wise decay.
Training Script
```python
run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-Discriminator \
--dataset_name squad_v2 \
--do_train \
--do_eval \
--dataloader_num_workers 20 \
--preprocessing_num_workers 20 \
--version_2_with_negative \
--num_train_epochs 2 \
--learning_rate 5e-5 \
--max_seq_length 512 \
--doc_stride 128 \
--per_device_train_batch_size 8 \
--gradient_accumulation_steps 6 \
--per_device_eval_batch_size 128
--fp16 \
--fp16_opt_level O1 \
--logging_steps 50 \
--save_steps 1000 \
--overwrite_output_dir \
--output_dir out
```
Evaluation results on SQuAD2.0 Dev Dataset
```
exact = 84.33420365535248
f1 = 87.49354241889522
total = 11873
HasAns_exact = 80.43184885290148
HasAns_f1 = 86.75958656200127
HasAns_total = 5928
NoAns_exact = 88.22539949537426
NoAns_f1 = 88.22539949537426
NoAns_total = 5945
best_exact = 84.33420365535248
best_exact_thresh = 0.0
best_f1 = 87.49354241889522
best_f1_thresh = 0.0
epoch = 2.0
```
To reproduce results in Google Colab:
- Make sure you have GPU enabled.
- Clone and install required libraries through this code
!git clone https://github.com/huggingface/transformers
!pip3 install -e transformers
!pip3 install sentencepiece
!pip3 install -r /content/transformers/examples/pytorch/question-answering/requirements.txt
- Run this python code:
```python
python /content/transformers/examples/pytorch/question-answering/run_qa.py --model_name_or_path sultan/BioM-ELECTRA-Large-SQuAD2 \
--do_eval \
--version_2_with_negative \
--per_device_eval_batch_size 8 \
--dataset_name squad_v2 \
--overwrite_output_dir \
--fp16 \
--output_dir out
```
- You don't need to download the SQuAD2 dataset. The code will download it from the HuggingFace datasets hub.
- Check our GitHub repo at https://github.com/salrowili/BioM-Transformers for TensorFlow and GluonNLP checkpoints.
- We added examples to fine-tune BioM-ELECTRA-Large on SQuAD and BioASQ7B using TensorFlow and TPU here https://github.com/salrowili/BioM-Transformers/tree/main/examples . In this example we show that we achieve 88.22 score in SQuAD2.0 since Tensor Flow code has Layer-wise decay feature.
# Acknowledgment
We would like to acknowledge the support we have from Tensorflow Research Cloud (TFRC) team to grant us access to TPUv3 units.
# Citation
```bibtex
@inproceedings{alrowili-shanker-2021-biom,
title = "{B}io{M}-Transformers: Building Large Biomedical Language Models with {BERT}, {ALBERT} and {ELECTRA}",
author = "Alrowili, Sultan and
Shanker, Vijay",
booktitle = "Proceedings of the 20th Workshop on Biomedical Language Processing",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.bionlp-1.24",
pages = "221--227",
abstract = "The impact of design choices on the performance of biomedical language models recently has been a subject for investigation. In this paper, we empirically study biomedical domain adaptation with large transformer models using different design choices. We evaluate the performance of our pretrained models against other existing biomedical language models in the literature. Our results show that we achieve state-of-the-art results on several biomedical domain tasks despite using similar or less computational cost compared to other models in the literature. Our findings highlight the significant effect of design choices on improving the performance of biomedical language models.",
}
``` |
NovelAI/genji-python-6B | NovelAI | 2021-08-06T19:15:41Z | 29 | 42 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
## Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
## Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
### How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.
For now, you need to use this fork:
[Fork](https://github.com/finetuneanon/transformers)
to install with pip:
```bash
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
```
This model takes more than 16 gigs of RAM to load. If you want more efficient and faster loading, please check our split model.
We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("NovelAI/genji-python-6B", use_auth_token=True).half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
```
When ran, this code generates:
```python
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
```
For example usage, you can see our colab notebook as well:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Eval results
TBD
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/)
and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project!
- [Aero](https://github.com/AeroScripts)
- [Finetune](https://github.com/finetuneanon)
- [Kurumuz](https://github.com/kurumuz) |
NovelAI/genji-python-6B-split | NovelAI | 2021-08-06T18:57:56Z | 0 | 3 | null | [
"pytorch",
"causal-lm",
"en",
"arxiv:2104.09864",
"license:apache-2.0",
"region:us"
] | null | 2022-03-02T23:29:04Z | ---
language:
- en
tags:
- pytorch
- causal-lm
license: apache-2.0
datasets:
- the Pile
---
# Genji-python 6B
For example usage or to easily use the model you can check our colab notebook:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Model Description
Genji is a transformer model finetuned on EleutherAI's GPT-J 6B model. This particular model is trained on python only code approaching 4GB in size.
Split model has the checkpoints splitted, which makes it use less system RAM while loading and makes it faster to load.
This model needs more effort to set up as you need to install git-lfs and pull the repo.
| Hyperparameter | Value |
|-------------------|--------|
| n_parameters | 6,053,381,344 |
| n_layers | 28* |
| d_model | 4,096 |
| d_ff | 16,384 |
| n_heads | 16 |
| d_head | 256 |
| n_ctx | 2,048 |
| n_vocab | 50,400 (same tokenizer as GPT-2/3) |
| position encoding | [Rotary position encodings (RoPE)](https://arxiv.org/abs/2104.09864) |
| RoPE dimensions | [64](https://github.com/kingoflolz/mesh-transformer-jax/blob/f2aa66e0925de6593dcbb70e72399b97b4130482/mesh_transformer/layers.py#L223) |
`*` each layer consists of one feedforward block and one self attention block
The model consists of 28 layers with a model dimension of 4096, and a feedforward dimension of 16384. The model
dimension is split into 16 heads, each with a dimension of 256. Rotary position encodings (RoPE) was applied to 64
dimensions of each head. The model is trained with a tokenization vocabulary of 50257, using the same set of BPEs as
GPT-2/GPT-3.
## Training data
GPT-J 6B was pretrained on the [Pile](pile.eleuther.ai), a large scale curated dataset created by EleutherAI for the purpose of training this model. After the pre-training, it's finetuned on the python code that was taken from the Pile.
## Training procedure
Genji-python-6B is trained for 20k steps on around 655 million tokens with learning rate of 2e-06
## Intended Use
This model is trained for assistence on writing python code and having fun trying weird stuff with it.
### How to use
This model is only usable with our fork because GPT-J is not merged to the main transformers repo yet. When it's merged, we will make this model easily loadable.
For now, you need to use this fork:
[Fork](https://github.com/finetuneanon/transformers)
to install with pip:
```bash
pip install git+https://github.com/finetuneanon/transformers@gpt-neo-localattention3-rp-b
```
**git-lfs** also needs to be installed, on ubuntu:
```bash
apt install git-lfs
```
after it's installed, initialize git-lfs:
```bash
git lfs install
```
then clone this repo:
```bash
git clone https://huggingface.co/NovelAI/genji-python-6B-split
```
Now we can load the model.
We recommend the usage of the model as FP16. That way, it fits in 16GB VRAM cards.
How to use:
```python
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
GPTNeoForCausalLM,
)
model = AutoModelForCausalLM.from_pretrained("genji-python-6B-split/model").half().eval().cuda()
tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
text = '''def print_customer_name'''
tokens = tokenizer(text, return_tensors="pt").input_ids
generated_tokens = model.generate(tokens.long().cuda(), use_cache=True, do_sample=True, top_k=50, temperature=0.3, top_p=0.9, repetition_penalty=1.125, min_length=1, max_length=len(tokens[0]) + 400, pad_token_id=tokenizer.eos_token_id)
last_tokens = generated_tokens[0][len(tokens[0]):]
generated_text = tokenizer.decode(last_tokens)
print("Generation:\n" + generated_text)
```
When ran, this code generates:
```python
Prompt:
def print_customer_name
Generation:
(self, customer):
"""Print the name of a customer."""
if not self.is_valid():
return
print("Customer: {}".format(customer))
```
For example usage, you can see our colab notebook as well:
[Notebook](https://colab.research.google.com/drive/1PnWpx02IEUkY8jhLKd_NewUGEXahAska?usp=sharing)
## Eval results
TBD
## Acknowledgements
This project was possible because of the compute provided by the
[TPU Research Cloud](https://sites.research.google/trc/) and [EleutherAI](https://eleuther.ai/) for pretraining of the GPT-J 6B.
Thanks to everyone who contributed to this project:
- [Aero](https://github.com/AeroScripts)
- [Finetune](https://github.com/finetuneanon)
- [Kurumuz](https://github.com/kurumuz) |
ncduy/opus-mt-en-ro-finetuned-en-to-ro | ncduy | 2021-08-06T15:55:10Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:wmt16",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- wmt16
model_index:
- name: opus-mt-en-ro-finetuned-en-to-ro
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
args: ro-en
---
<!-- 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. -->
# opus-mt-en-ro-finetuned-en-to-ro
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-ro](https://huggingface.co/Helsinki-NLP/opus-mt-en-ro) on the wmt16 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: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 382 | 1.4067 | 27.6209 | 33.5648 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
ncduy/bert-base-cased-wikitext2 | ncduy | 2021-08-06T15:08:09Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- null
model_index:
- name: bert-base-cased-wikitext2
results:
- task:
name: Masked Language Modeling
type: fill-mask
---
<!-- 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-wikitext2
This model is a fine-tuned version of [](https://huggingface.co/) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8565
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0916 | 1.0 | 2346 | 7.0492 |
| 6.9074 | 2.0 | 4692 | 6.8727 |
| 6.8588 | 3.0 | 7038 | 6.8914 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
osanseviero/fasttext_english | osanseviero | 2021-08-06T14:23:49Z | 0 | 3 | generic | [
"generic",
"feature-extraction",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
tags:
- feature-extraction
library_name: generic
---
# Pretrained FastText word vector for English
https://github.com/facebookresearch/fastText
Usage
```
import fasttext.util
ft = fasttext.load_model('cc.en.300.bin')
ft.get_word_vector('hello')
``` |
osanseviero/fasttext_test | osanseviero | 2021-08-06T14:23:49Z | 0 | 0 | generic | [
"generic",
"feature-extraction",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | ---
tags:
- feature-extraction
library_name: generic
---
# Pretrained FastText word vector for English
https://github.com/facebookresearch/fastText
Usage
```
import fasttext.util
ft = fasttext.load_model('cc.en.300.bin')
ft.get_word_vector('hello')
``` |
navteca/bart-large-mnli | navteca | 2021-08-06T13:59:01Z | 48 | 4 | transformers | [
"transformers",
"pytorch",
"jax",
"bart",
"text-classification",
"zero-shot-classification",
"en",
"dataset:multi_nli",
"arxiv:1909.00161",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | zero-shot-classification | 2022-03-02T23:29:05Z | ---
datasets:
- multi_nli
language: en
license: mit
pipeline_tag: zero-shot-classification
tags:
- bart
- zero-shot-classification
---
# Bart large model for NLI-based Zero Shot Text Classification
This model uses [bart-large](https://huggingface.co/facebook/bart-large).
## Training Data
This model was trained on the [MultiNLI (MNLI)](https://huggingface.co/datasets/multi_nli) dataset in the manner originally described in [Yin et al. 2019](https://arxiv.org/abs/1909.00161).
It can be used to predict whether a topic label can be assigned to a given sequence, whether or not the label has been seen before.
## Usage and Performance
The trained model can be used like this:
```python
from transformers import AutoModelForSequenceClassification, AutoTokenizer, pipeline
# Load model & tokenizer
bart_model = AutoModelForSequenceClassification.from_pretrained('navteca/bart-large-mnli')
bart_tokenizer = AutoTokenizer.from_pretrained('navteca/bart-large-mnli')
# Get predictions
nlp = pipeline('zero-shot-classification', model=bart_model, tokenizer=bart_tokenizer)
sequence = 'One day I will see the world.'
candidate_labels = ['cooking', 'dancing', 'travel']
result = nlp(sequence, candidate_labels, multi_label=True)
print(result)
#{
# "sequence": "One day I will see the world.",
# "labels": [
# "travel",
# "dancing",
# "cooking"
# ],
# "scores": [
# 0.9941897988319397,
# 0.0060537424869835,
# 0.0020010927692056
# ]
#}
``` |
Harveenchadha/wav2vec2-pretrained-clsril-23-10k | Harveenchadha | 2021-08-06T13:40:49Z | 190 | 5 | transformers | [
"transformers",
"pytorch",
"wav2vec2",
"feature-extraction",
"arxiv:2107.07402",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | ## Overview
We present a CLSRIL-23 (Cross Lingual Speech Representations on Indic Languages), a self supervised learning based audio pre-trained model which learns cross
lingual speech representations from raw audio across **23 Indic languages**. It is built on top of wav2vec
2.0 which is solved by training a contrastive task over masked latent speech representations and
jointly learns the quantization of latents shared across all languages.
[Arxiv Link](https://arxiv.org/pdf/2107.07402.pdf)
[Original Repo](https://github.com/Open-Speech-EkStep/vakyansh-models) contains models in fairseq format.
## Languages in the pretraining dataset
| Language | Data (In Hrs) |
|-----------|---------------|
| Assamese | 254.9 |
| Bengali | 331.3 |
| Bodo | 26.9 |
| Dogri | 17.1 |
| English | 819.7 |
| Gujarati | 336.7 |
| Hindi | 4563.7 |
| Kannada | 451.8 |
| Kashmiri | 67.8 |
| Konkani | 36.8 |
| Maithili | 113.8 |
| Malayalam | 297.7 |
| Manipuri | 171.9 |
| Marathi | 458.2 |
| Nepali | 31.6 |
| Odia | 131.4 |
| Punjabi | 486.05 |
| Sanskrit | 58.8 |
| Santali | 6.56 |
| Sindhi | 16 |
| Tamil | 542.6 |
| Telugu | 302.8 |
| Urdu | 259.68 |
## Repo for training:
[Experimentation](https://github.com/Open-Speech-EkStep/vakyansh-wav2vec2-experimentation) platform built on top of fairseq.
|
sarasarasara/sara-model | sarasarasara | 2021-08-06T10:57:48Z | 15 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.984018301110458
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0614
- Precision: 0.9288
- Recall: 0.9374
- F1: 0.9331
- Accuracy: 0.9840
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2399 | 1.0 | 878 | 0.0694 | 0.9126 | 0.9179 | 0.9152 | 0.9807 |
| 0.0522 | 2.0 | 1756 | 0.0604 | 0.9207 | 0.9342 | 0.9274 | 0.9833 |
| 0.0308 | 3.0 | 2634 | 0.0614 | 0.9288 | 0.9374 | 0.9331 | 0.9840 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Pyjay/sentence-transformers-multilingual-snli-v2-500k | Pyjay | 2021-08-05T21:42:55Z | 16 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:04Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Pyjay/sentence-transformers-multilingual-snli-v2-500k
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('Pyjay/sentence-transformers-multilingual-snli-v2-500k')
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('Pyjay/sentence-transformers-multilingual-snli-v2-500k')
model = AutoModel.from_pretrained('Pyjay/sentence-transformers-multilingual-snli-v2-500k')
# 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, max 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=Pyjay/sentence-transformers-multilingual-snli-v2-500k)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 15604 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 180 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 4,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 72,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(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 --> |
jegormeister/bert-base-dutch-cased | jegormeister | 2021-08-05T19:28:55Z | 30 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# bert-base-dutch-cased-snli
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 256 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('bert-base-dutch-cased-snli')
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('bert-base-dutch-cased-snli')
model = AutoModel.from_pretrained('bert-base-dutch-cased-snli')
# 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, max 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=bert-base-dutch-cased-snli)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 339 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "utils.CombEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"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': 256, '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 --> |
microsoft/xtremedistil-l6-h256-uncased | microsoft | 2021-08-05T17:49:53Z | 1,941 | 33 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation](https://arxiv.org/abs/2106.04563).
We leverage task transfer combined with multi-task distillation techniques from the papers [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://www.aclweb.org/anthology/2020.acl-main.202.pdf) and [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://proceedings.neurips.cc/paper/2020/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) with the following [Github code](https://github.com/microsoft/xtreme-distil-transformers).
This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
Other available checkpoints: [xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased) and [xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased)
The following table shows the results on GLUE dev set and SQuAD-v2.
| Models | #Params | Speedup | MNLI | QNLI | QQP | RTE | SST | MRPC | SQUAD2 | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 1x | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 76.8 | 84.8 |
| DistilBERT | 66 | 2x | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 70.7 | 81.3 |
| TinyBERT | 66 | 2x | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 73.1 | 84.3 |
| MiniLM | 66 | 2x | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 76.4 | 84.9 |
| MiniLM | 22 | 5.3x | 82.8 | 90.3 | 90.6 | 68.9 | 91.3 | 86.6 | 72.9 | 83.3 |
| XtremeDistil-l6-h256 | 13 | 8.7x | 83.9 | 89.5 | 90.6 | 80.1 | 91.2 | 90.0 | 74.1 | 85.6 |
| XtremeDistil-l6-h384 | 22 | 5.3x | 85.4 | 90.3 | 91.0 | 80.9 | 92.3 | 90.0 | 76.6 | 86.6 |
| XtremeDistil-l12-h384 | 33 | 2.7x | 87.2 | 91.9 | 91.3 | 85.6 | 93.1 | 90.4 | 80.2 | 88.5 |
Tested with `tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@misc{mukherjee2021xtremedistiltransformers,
title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation},
author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
year={2021},
eprint={2106.04563},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
microsoft/xtremedistil-l12-h384-uncased | microsoft | 2021-08-05T17:49:31Z | 1,132 | 15 | transformers | [
"transformers",
"pytorch",
"tf",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation](https://arxiv.org/abs/2106.04563).
We leverage task transfer combined with multi-task distillation techniques from the papers [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://www.aclweb.org/anthology/2020.acl-main.202.pdf) and [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://proceedings.neurips.cc/paper/2020/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) with the following [Github code](https://github.com/microsoft/xtreme-distil-transformers).
This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
Other available checkpoints: [xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) and [xtremedistil-l6-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h384-uncased)
The following table shows the results on GLUE dev set and SQuAD-v2.
| Models | #Params | Speedup | MNLI | QNLI | QQP | RTE | SST | MRPC | SQUAD2 | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 1x | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 76.8 | 84.8 |
| DistilBERT | 66 | 2x | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 70.7 | 81.3 |
| TinyBERT | 66 | 2x | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 73.1 | 84.3 |
| MiniLM | 66 | 2x | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 76.4 | 84.9 |
| MiniLM | 22 | 5.3x | 82.8 | 90.3 | 90.6 | 68.9 | 91.3 | 86.6 | 72.9 | 83.3 |
| XtremeDistil-l6-h256 | 13 | 8.7x | 83.9 | 89.5 | 90.6 | 80.1 | 91.2 | 90.0 | 74.1 | 85.6 |
| XtremeDistil-l6-h384 | 22 | 5.3x | 85.4 | 90.3 | 91.0 | 80.9 | 92.3 | 90.0 | 76.6 | 86.6 |
| XtremeDistil-l12-h384 | 33 | 2.7x | 87.2 | 91.9 | 91.3 | 85.6 | 93.1 | 90.4 | 80.2 | 88.5 |
Tested with `tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@misc{mukherjee2021xtremedistiltransformers,
title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation},
author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
year={2021},
eprint={2106.04563},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
microsoft/xtremedistil-l6-h384-uncased | microsoft | 2021-08-05T17:48:58Z | 824 | 22 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"feature-extraction",
"text-classification",
"en",
"arxiv:2106.04563",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
tags:
- text-classification
license: mit
---
# XtremeDistilTransformers for Distilling Massive Neural Networks
XtremeDistilTransformers is a distilled task-agnostic transformer model that leverages task transfer for learning a small universal model that can be applied to arbitrary tasks and languages as outlined in the paper [XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation](https://arxiv.org/abs/2106.04563).
We leverage task transfer combined with multi-task distillation techniques from the papers [XtremeDistil: Multi-stage Distillation for Massive Multilingual Models](https://www.aclweb.org/anthology/2020.acl-main.202.pdf) and [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://proceedings.neurips.cc/paper/2020/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf) with the following [Github code](https://github.com/microsoft/xtreme-distil-transformers).
This l6-h384 checkpoint with **6** layers, **384** hidden size, **12** attention heads corresponds to **22 million** parameters with **5.3x** speedup over BERT-base.
Other available checkpoints: [xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) and [xtremedistil-l12-h384-uncased](https://huggingface.co/microsoft/xtremedistil-l12-h384-uncased)
The following table shows the results on GLUE dev set and SQuAD-v2.
| Models | #Params | Speedup | MNLI | QNLI | QQP | RTE | SST | MRPC | SQUAD2 | Avg |
|----------------|--------|---------|------|------|------|------|------|------|--------|-------|
| BERT | 109 | 1x | 84.5 | 91.7 | 91.3 | 68.6 | 93.2 | 87.3 | 76.8 | 84.8 |
| DistilBERT | 66 | 2x | 82.2 | 89.2 | 88.5 | 59.9 | 91.3 | 87.5 | 70.7 | 81.3 |
| TinyBERT | 66 | 2x | 83.5 | 90.5 | 90.6 | 72.2 | 91.6 | 88.4 | 73.1 | 84.3 |
| MiniLM | 66 | 2x | 84.0 | 91.0 | 91.0 | 71.5 | 92.0 | 88.4 | 76.4 | 84.9 |
| MiniLM | 22 | 5.3x | 82.8 | 90.3 | 90.6 | 68.9 | 91.3 | 86.6 | 72.9 | 83.3 |
| XtremeDistil-l6-h256 | 13 | 8.7x | 83.9 | 89.5 | 90.6 | 80.1 | 91.2 | 90.0 | 74.1 | 85.6 |
| XtremeDistil-l6-h384 | 22 | 5.3x | 85.4 | 90.3 | 91.0 | 80.9 | 92.3 | 90.0 | 76.6 | 86.6 |
| XtremeDistil-l12-h384 | 33 | 2.7x | 87.2 | 91.9 | 91.3 | 85.6 | 93.1 | 90.4 | 80.2 | 88.5 |
Tested with `tensorflow 2.3.1, transformers 4.1.1, torch 1.6.0`
If you use this checkpoint in your work, please cite:
``` latex
@misc{mukherjee2021xtremedistiltransformers,
title={XtremeDistilTransformers: Task Transfer for Task-agnostic Distillation},
author={Subhabrata Mukherjee and Ahmed Hassan Awadallah and Jianfeng Gao},
year={2021},
eprint={2106.04563},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
jamarju/roberta-large-bne-squad-2.0-es | jamarju | 2021-08-05T14:59:41Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:squad_es",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:05Z | ---
language:
- es
datasets:
- squad_es
widget:
- text: "¿Quién era el duque en la batalla de Hastings?"
context: "La dinastía normanda tuvo un gran impacto político, cultural y militar en la Europa medieval e incluso en el Cercano Oriente. Los normandos eran famosos por su espíritu marcial y, finalmente, por su piedad cristiana, convirtiéndose en exponentes de la ortodoxia católica en la que se asimilaron. Adoptaron la lengua galorromance de la tierra franca que establecieron, siendo su dialecto conocido como francés normando, normando o normando, una lengua literaria importante. El ducado de Normandía, que formaron por tratado con la corona francesa, fue un gran feudo de la Francia medieval, y bajo Ricardo I de Normandía se forjó en un principado cohesionado y formidable en la tenencia feudal. Los normandos se caracterizan tanto por su cultura, como por su singular arquitectura románica y sus tradiciones musicales, y por sus importantes logros e innovaciones militares. Aventureros normandos fundaron el Reino de Sicilia bajo Roger II después de conquistar el sur de Italia con los sarracenos y bizantinos, y una expedición en nombre de su duque, Guillermo el Conquistador, condujo a la conquista normanda de Inglaterra. La influencia cultural y militar normanda se extendió desde estos nuevos centros europeos a los estados cruzados del Cercano Oriente, donde su príncipe Bohemundo I fundó el Principado de Antioquía en el Levante mediterráneo, a Escocia y Gales en Gran Bretaña."
---
This is the [BSC-TeMU/roberta-large-bne](https://huggingface.co/BSC-TeMU/roberta-large-bne) model ([source](https://github.com/PlanTL-SANIDAD/lm-spanish)) trained on the [squad_es v2.0.0](https://huggingface.co/datasets/squad_es) dataset ([source](https://github.com/ccasimiro88/TranslateAlignRetrieve)).
Current achievement: em=60.21, f1=68.61
Results:
```
{
"epoch": 4.0,
"eval_HasAns_exact": 48.44804318488529,
"eval_HasAns_f1": 65.24520506718169,
"eval_HasAns_total": 5928,
"eval_NoAns_exact": 71.97301854974705,
"eval_NoAns_f1": 71.97301854974705,
"eval_NoAns_total": 5930,
"eval_best_exact": 60.22094788328555,
"eval_best_exact_thresh": 0.0,
"eval_best_f1": 68.6181122987237,
"eval_best_f1_thresh": 0.0,
"eval_exact": 60.2125147579693,
"eval_f1": 68.60967917340695,
"eval_samples": 12203,
"eval_total": 11858
}
```
Training script:
```
python -m torch.distributed.launch --nproc_per_node=3 ./run_qa.py \
--model_name_or_path BSC-TeMU/roberta-large-bne \
--dataset_name squad_es \
--dataset_config_name v2.0.0 \
--do_train \
--do_eval \
--learning_rate 3e-5 \
--num_train_epochs 4 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir ./models/roberta-large-bne-finetuned-squad-es/ \
--per_device_eval_batch_size=24 \
--per_device_train_batch_size=12 \
--version_2_with_negative \
--ddp_find_unused_parameters=False \
```
|
huggingtweets/dril-gnomeszs-s4m31p4n | huggingtweets | 2021-08-05T12:24:53Z | 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: https://www.huggingtweets.com/dril-gnomeszs-s4m31p4n/1628166288972/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/847818629840228354/VXyQHfn0_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/1393094522008080385/1urtPrKy_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/1404609739883954183/gta_5zXG_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">wint & gnome 👼🏻 & ppigg</div>
<div style="text-align: center; font-size: 14px;">@dril-gnomeszs-s4m31p4n</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 wint & gnome 👼🏻 & ppigg.
| Data | wint | gnome 👼🏻 | ppigg |
| --- | --- | --- | --- |
| Tweets downloaded | 3192 | 3220 | 3156 |
| Retweets | 456 | 1075 | 992 |
| Short tweets | 307 | 438 | 907 |
| Tweets kept | 2429 | 1707 | 1257 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2370ibjc/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 @dril-gnomeszs-s4m31p4n's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/yu2suj5m) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/yu2suj5m/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/dril-gnomeszs-s4m31p4n')
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)
|
ehdwns1516/bert-base-uncased_SWAG | ehdwns1516 | 2021-08-05T09:49:18Z | 16 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"multiple-choice",
"endpoints_compatible",
"region:us"
] | multiple-choice | 2022-03-02T23:29:05Z | # ehdwns1516/bert-base-uncased_SWAG
* This model has been trained as a [SWAG dataset](https://huggingface.co/ehdwns1516/bert-base-uncased_SWAG).
* Sentence Inference Multiple Choice DEMO: [Ainize DEMO](https://main-sentence-inference-multiple-choice-ehdwns1516.endpoint.ainize.ai/)
* Sentence Inference Multiple Choice API: [Ainize API](https://ainize.web.app/redirect?git_repo=https://github.com/ehdwns1516/sentence_inference_multiple_choice)
## Overview
Language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
Language: English
Training data: [SWAG dataset](https://huggingface.co/datasets/swag)
Code: See [Ainize Workspace](https://ainize.ai/workspace/create?imageId=hnj95592adzr02xPTqss&git=https://github.com/ehdwns1516/Multiple_choice_SWAG_finetunning)
## Usage
## In Transformers
```
from transformers import AutoTokenizer, AutoModelForMultipleChoice
tokenizer = AutoTokenizer.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
model = AutoModelForMultipleChoice.from_pretrained("ehdwns1516/bert-base-uncased_SWAG")
def run_model(candicates_count, context: str, candicates: list[str]):
assert len(candicates) == candicates_count, "you need " + candicates_count + " candidates"
choices_inputs = []
for c in candicates:
text_a = "" # empty context
text_b = context + " " + c
inputs = tokenizer(
text_a,
text_b,
add_special_tokens=True,
max_length=128,
padding="max_length",
truncation=True,
return_overflowing_tokens=True,
)
choices_inputs.append(inputs)
input_ids = torch.LongTensor([x["input_ids"] for x in choices_inputs])
output = model(input_ids=input_ids)
return {"result": candicates[torch.argmax(output.logits).item()]}
items = list()
count = 4 # candicates count
context = "your context"
for i in range(int(count)):
items.append("sentence")
result = run_model(count, context, items)
```
|
syndi-models/ms-marco-MiniLM-L-12-v2 | syndi-models | 2021-08-05T08:39:01Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2023-05-09T19:06:32Z | ---
license: apache-2.0
---
# Cross-Encoder for MS Marco
This model was trained on the [MS Marco Passage Ranking](https://github.com/microsoft/MSMARCO-Passage-Ranking) task.
The model can be used for Information Retrieval: Given a query, encode the query will all possible passages (e.g. retrieved with ElasticSearch). Then sort the passages in a decreasing order. See [SBERT.net Retrieve & Re-rank](https://www.sbert.net/examples/applications/retrieve_rerank/README.html) for more details. The training code is available here: [SBERT.net Training MS Marco](https://github.com/UKPLab/sentence-transformers/tree/master/examples/training/ms_marco)
## Usage with Transformers
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
model = AutoModelForSequenceClassification.from_pretrained('model_name')
tokenizer = AutoTokenizer.from_pretrained('model_name')
features = tokenizer(['How many people live in Berlin?', 'How many people live in Berlin?'], ['Berlin has a population of 3,520,031 registered inhabitants in an area of 891.82 square kilometers.', 'New York City is famous for the Metropolitan Museum of Art.'], padding=True, truncation=True, return_tensors="pt")
model.eval()
with torch.no_grad():
scores = model(**features).logits
print(scores)
```
## Usage with SentenceTransformers
The usage becomes easier when you have [SentenceTransformers](https://www.sbert.net/) installed. Then, you can use the pre-trained models like this:
```python
from sentence_transformers import CrossEncoder
model = CrossEncoder('model_name', max_length=512)
scores = model.predict([('Query', 'Paragraph1'), ('Query', 'Paragraph2') , ('Query', 'Paragraph3')])
```
## Performance
In the following table, we provide various pre-trained Cross-Encoders together with their performance on the [TREC Deep Learning 2019](https://microsoft.github.io/TREC-2019-Deep-Learning/) and the [MS Marco Passage Reranking](https://github.com/microsoft/MSMARCO-Passage-Ranking/) dataset.
| Model-Name | NDCG@10 (TREC DL 19) | MRR@10 (MS Marco Dev) | Docs / Sec |
| ------------- |:-------------| -----| --- |
| **Version 2 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2-v2 | 69.84 | 32.56 | 9000
| cross-encoder/ms-marco-MiniLM-L-2-v2 | 71.01 | 34.85 | 4100
| cross-encoder/ms-marco-MiniLM-L-4-v2 | 73.04 | 37.70 | 2500
| cross-encoder/ms-marco-MiniLM-L-6-v2 | 74.30 | 39.01 | 1800
| cross-encoder/ms-marco-MiniLM-L-12-v2 | 74.31 | 39.02 | 960
| **Version 1 models** | | |
| cross-encoder/ms-marco-TinyBERT-L-2 | 67.43 | 30.15 | 9000
| cross-encoder/ms-marco-TinyBERT-L-4 | 68.09 | 34.50 | 2900
| cross-encoder/ms-marco-TinyBERT-L-6 | 69.57 | 36.13 | 680
| cross-encoder/ms-marco-electra-base | 71.99 | 36.41 | 340
| **Other models** | | |
| nboost/pt-tinybert-msmarco | 63.63 | 28.80 | 2900
| nboost/pt-bert-base-uncased-msmarco | 70.94 | 34.75 | 340
| nboost/pt-bert-large-msmarco | 73.36 | 36.48 | 100
| Capreolus/electra-base-msmarco | 71.23 | 36.89 | 340
| amberoad/bert-multilingual-passage-reranking-msmarco | 68.40 | 35.54 | 330
| sebastian-hofstaetter/distilbert-cat-margin_mse-T2-msmarco | 72.82 | 37.88 | 720
Note: Runtime was computed on a V100 GPU.
|
ehddnr301/bert-base-ehddnr-ynat | ehddnr301 | 2021-08-05T06:28:30Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model_index:
- name: bert-base-ehddnr-ynat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metric:
name: F1
type: f1
value: 0.8720568553403009
---
<!-- 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-ehddnr-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3587
- F1: 0.8721
## 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: 256
- eval_batch_size: 256
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.4398 | 0.8548 |
| No log | 2.0 | 358 | 0.3587 | 0.8721 |
| 0.3859 | 3.0 | 537 | 0.3639 | 0.8707 |
| 0.3859 | 4.0 | 716 | 0.3592 | 0.8692 |
| 0.3859 | 5.0 | 895 | 0.3646 | 0.8717 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
shashank2123/t5-finetuned-for-GEC | shashank2123 | 2021-08-05T06:16:09Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- bleu
model_index:
- name: t5-finetuned-for-GEC
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
metric:
name: Bleu
type: bleu
value: 0.3571
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-finetuned-for-GEC
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unkown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3949
- Bleu: 0.3571
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:-------:|
| 0.3958 | 1.0 | 4053 | 0.4236 | 0.3493 | 19.0 |
| 0.3488 | 2.0 | 8106 | 0.4076 | 0.3518 | 19.0 |
| 0.319 | 3.0 | 12159 | 0.3962 | 0.3523 | 19.0 |
| 0.3105 | 4.0 | 16212 | 0.3951 | 0.3567 | 19.0 |
| 0.3016 | 5.0 | 20265 | 0.3949 | 0.3571 | 19.0 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/centenaryla | huggingtweets | 2021-08-05T01:44:58Z | 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: https://www.huggingtweets.com/centenaryla/1628127894127/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/2562399680/4wib1f3f19eup8pvy7w6_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">Centenary College</div>
<div style="text-align: center; font-size: 14px;">@centenaryla</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 Centenary College.
| Data | Centenary College |
| --- | --- |
| Tweets downloaded | 3187 |
| Retweets | 708 |
| Short tweets | 143 |
| Tweets kept | 2336 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3h9syzxm/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 @centenaryla's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6ym91qam) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6ym91qam/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/centenaryla')
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)
|
huggingtweets/profleeper | huggingtweets | 2021-08-05T01:15:27Z | 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: https://www.huggingtweets.com/profleeper/1628126123136/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/1271223798470266884/zHwuzmAN_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">Mark Leeper</div>
<div style="text-align: center; font-size: 14px;">@profleeper</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 Mark Leeper.
| Data | Mark Leeper |
| --- | --- |
| Tweets downloaded | 3188 |
| Retweets | 836 |
| Short tweets | 375 |
| Tweets kept | 1977 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1f45tto5/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 @profleeper's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2khjronw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2khjronw/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/profleeper')
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)
|
tbs17/MathBERT | tbs17 | 2021-08-05T00:44:29Z | 5,196 | 18 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | #### MathBERT model (original vocab)
*Disclaimer: the format of the documentation follows the official BERT model readme.md*
Pretrained model on pre-k to graduate math language (English) using a masked language modeling (MLM) objective. This model is uncased: it does not make a difference between english and English.
#### Model description
MathBERT is a transformers model pretrained on a large corpus of English math corpus data in a self-supervised fashion. This means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely, it was pretrained with two objectives:
Masked language modeling (MLM): taking a sentence, the model randomly masks 15% of the words in the input then run the entire masked sentence through the model and has to predict the masked words. This is different from traditional recurrent neural networks (RNNs) that usually see the words one after the other, or from autoregressive models like GPT which internally mask the future tokens. It allows the model to learn a bidirectional representation of the sentence.
Next sentence prediction (NSP): the models concatenates two masked sentences as inputs during pretraining. Sometimes they correspond to sentences that were next to each other in the original text, sometimes not. The model then has to predict if the two sentences were following each other or not.
This way, the model learns an inner representation of the math language that can then be used to extract features useful for downstream tasks: if you have a dataset of labeled sentences for instance, you can train a standard classifier using the features produced by the MathBERT model as inputs.
#### Intended uses & limitations
You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a math-related downstream task.
Note that this model is primarily aimed at being fine-tuned on math-related tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as math text generation you should look at model like GPT2.
#### How to use
Here is how to use this model to get the features of a given text in PyTorch:
```from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT'，output_hidden_states=True)
model = BertModel.from_pretrained("tbs17/MathBERT")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(encoded_input)
```
and in TensorFlow:
```
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('tbs17/MathBERT',output_hidden_states=True)
model = TFBertModel.from_pretrained("tbs17/MathBERT")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Comparing to the original BERT on fill-mask tasks
The original BERT (i.e.,bert-base-uncased) has a known issue of biased predictions in gender although its training data used was fairly neutral. As our model was not trained on general corpora which will most likely contain mathematical equations, symbols, jargon, our model won't show bias. See below:
##### from original BERT
```
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("The man worked as a [MASK].")
[{'sequence': '[CLS] the man worked as a carpenter. [SEP]',
'score': 0.09747550636529922,
'token': 10533,
'token_str': 'carpenter'},
{'sequence': '[CLS] the man worked as a waiter. [SEP]',
'score': 0.0523831807076931,
'token': 15610,
'token_str': 'waiter'},
{'sequence': '[CLS] the man worked as a barber. [SEP]',
'score': 0.04962705448269844,
'token': 13362,
'token_str': 'barber'},
{'sequence': '[CLS] the man worked as a mechanic. [SEP]',
'score': 0.03788609802722931,
'token': 15893,
'token_str': 'mechanic'},
{'sequence': '[CLS] the man worked as a salesman. [SEP]',
'score': 0.037680890411138535,
'token': 18968,
'token_str': 'salesman'}]
>>> unmasker("The woman worked as a [MASK].")
[{'sequence': '[CLS] the woman worked as a nurse. [SEP]',
'score': 0.21981462836265564,
'token': 6821,
'token_str': 'nurse'},
{'sequence': '[CLS] the woman worked as a waitress. [SEP]',
'score': 0.1597415804862976,
'token': 13877,
'token_str': 'waitress'},
{'sequence': '[CLS] the woman worked as a maid. [SEP]',
'score': 0.1154729500412941,
'token': 10850,
'token_str': 'maid'},
{'sequence': '[CLS] the woman worked as a prostitute. [SEP]',
'score': 0.037968918681144714,
'token': 19215,
'token_str': 'prostitute'},
{'sequence': '[CLS] the woman worked as a cook. [SEP]',
'score': 0.03042375110089779,
'token': 5660,
'token_str': 'cook'}]
```
##### from MathBERT
```
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='tbs17/MathBERT')
>>> unmasker("The man worked as a [MASK].")
[{'score': 0.6469377875328064,
'sequence': 'the man worked as a book.',
'token': 2338,
'token_str': 'book'},
{'score': 0.07073448598384857,
'sequence': 'the man worked as a guide.',
'token': 5009,
'token_str': 'guide'},
{'score': 0.031362924724817276,
'sequence': 'the man worked as a text.',
'token': 3793,
'token_str': 'text'},
{'score': 0.02306508645415306,
'sequence': 'the man worked as a man.',
'token': 2158,
'token_str': 'man'},
{'score': 0.020547250285744667,
'sequence': 'the man worked as a distance.',
'token': 3292,
'token_str': 'distance'}]
>>> unmasker("The woman worked as a [MASK].")
[{'score': 0.8999770879745483,
'sequence': 'the woman worked as a woman.',
'token': 2450,
'token_str': 'woman'},
{'score': 0.025878004729747772,
'sequence': 'the woman worked as a guide.',
'token': 5009,
'token_str': 'guide'},
{'score': 0.006881994660943747,
'sequence': 'the woman worked as a table.',
'token': 2795,
'token_str': 'table'},
{'score': 0.0066248285584151745,
'sequence': 'the woman worked as a b.',
'token': 1038,
'token_str': 'b'},
{'score': 0.00638660229742527,
'sequence': 'the woman worked as a book.',
'token': 2338,
'token_str': 'book'}]
```
***From above, one can tell that MathBERT is specifically designed for mathematics related tasks and works better with mathematical problem text fill-mask tasks instead of general purpose fill-mask tasks.***
```
>>> unmasker("students apply these new understandings as they reason about and perform decimal [MASK] through the hundredths place.")
#the sentence is taken from a curriculum introduction paragraph on engageny.org: https://www.engageny.org/resource/grade-5-mathematics-module-1
[{'score': 0.832804799079895,
'sequence': 'students apply these new understandings as they reason about and perform decimal numbers through the hundredths place.',
'token': 3616,
'token_str': 'numbers'},
{'score': 0.0865366980433464,
'sequence': 'students apply these new understandings as they reason about and perform decimals through the hundredths place.',
'token': 2015,
'token_str': '##s'},
{'score': 0.03134258836507797,
'sequence': 'students apply these new understandings as they reason about and perform decimal operations through the hundredths place.',
'token': 3136,
'token_str': 'operations'},
{'score': 0.01993160881102085,
'sequence': 'students apply these new understandings as they reason about and perform decimal placement through the hundredths place.',
'token': 11073,
'token_str': 'placement'},
{'score': 0.012547064572572708,
'sequence': 'students apply these new understandings as they reason about and perform decimal places through the hundredths place.',
'token': 3182,
'token_str': 'places'}]
```
***Therefore, to try the 'fill-mask' hosted API on the right corner of the page, please use the sentences similar to below:***
```
1 tenth times any [MASK] on the place value chart moves it one place value to the right. #from https://www.engageny.org/resource/grade-5-mathematics-module-1
```
#### Training data
The MathBERT model was pretrained on pre-k to HS math curriculum (engageNY, Utah Math, Illustrative Math), college math books from openculture.com as well as graduate level math from arxiv math paper abstracts. There is about 100M tokens got pretrained on.
#### Training procedure
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,522 which is from original BERT vocab.txt. The inputs of the model are then of the form:
```
[CLS] Sentence A [SEP] Sentence B [SEP]
```
With probability 0.5, sentence A and sentence B correspond to two consecutive sentence spans from the original corpus. Note that what is considered a sentence here is a consecutive span of text usually longer than a single sentence, but less than 512 tokens.
The details of the masking procedure for each sentence are the following:
+ 15% of the tokens are masked.
+ In 80% of the cases, the masked tokens are replaced by [MASK].
+ In 10% of the cases, the masked tokens are replaced by a random token (different) from the one they replace.
+ In the 10% remaining cases, the masked tokens are left as is.
#### Pretraining
The model was trained on a 8-core cloud TPUs from Google Colab for 600k steps with a batch size of 128. The sequence length was limited to 512 for the entire time. The optimizer used is Adam with a learning rate of 5e-5, beta_{1} = 0.9 and beta_{2} =0.999, a weight decay of 0.01, learning rate warmup for 10,000 steps and linear decay of the learning rate after.
You can refer to the training and fine-tuning code at https://github.com/tbs17/MathBERT. |
PremalMatalia/roberta-base-best-squad2 | PremalMatalia | 2021-08-04T18:54:35Z | 243 | 1 | transformers | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] | question-answering | 2022-03-02T23:29:04Z | ---
datasets:
- squad_v2
---
# RoBERTa-base for QA
## Overview
**Language model:** 'roberta-base' </br>
**Language:** English </br>
**Downstream-task:** Extractive QA </br>
**Training data:** SQuAD 2.0 </br>
**Eval data:** SQuAD 2.0 </br>
**Code:** <TBD> </br>
## Env Information
`transformers` version: 4.9.1 </br>
Platform: Linux-5.4.104+-x86_64-with-Ubuntu-18.04-bionic </br>
Python version: 3.7.11 </br>
PyTorch version (GPU?): 1.9.0+cu102 (False)</br>
Tensorflow version (GPU?): 2.5.0 (False)</br>
## Hyperparameters
```
max_seq_len=386
doc_stride=128
n_best_size=20
max_answer_length=30
min_null_score=7.0
batch_size=8
n_epochs=6
base_LM_model = "roberta-base"
learning_rate=1.5e-5
adam_epsilon=1e-5
adam_beta1=0.95
adam_beta2=0.999
warmup_steps=100
weight_decay=0.01
optimizer=AdamW
lr_scheduler="polynomial"
```
##### There is a special threshold value CLS_threshold=-3 used to more accurately identify no answers [Logic will be available in GitHub Repo [TBD]
## Performance
```
"exact": 81.192622
"f1": 83.95408
"total": 11873
"HasAns_exact": 74.190283
"HasAns_f1": 79.721119
"HasAns_total": 5928
"NoAns_exact": 88.174937
"NoAns_f1": 88.174937
"NoAns_total": 5945
```
## Usage
### In Transformers
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "PremalMatalia/roberta-base-best-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
QA_input = {
'question': 'Which name is also used to describe the Amazon rainforest in English?',
'context': 'The Amazon rainforest (Portuguese: Floresta Amazônica or Amazônia; Spanish: Selva Amazónica, Amazonía or usually Amazonia; French: Forêt amazonienne; Dutch: Amazoneregenwoud), also known in English as Amazonia or the Amazon Jungle, is a moist broadleaf forest that covers most of the Amazon basin of South America. This basin encompasses 7,000,000 square kilometres (2,700,000 sq mi), of which 5,500,000 square kilometres (2,100,000 sq mi) are covered by the rainforest. This region includes territory belonging to nine nations. The majority of the forest is contained within Brazil, with 60% of the rainforest, followed by Peru with 13%, Colombia with 10%, and with minor amounts in Venezuela, Ecuador, Bolivia, Guyana, Suriname and French Guiana. States or departments in four nations contain "Amazonas" in their names. The Amazon represents over half of the planet\'s remaining rainforests, and comprises the largest and most biodiverse tract of tropical rainforest in the world, with an estimated 390 billion individual trees divided into 16,000 species.'
}
res = nlp(QA_input)
print(res)
# b) Load model & tokenizer
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name)
```
## Authors
Premal Matalia |
arampacha/DialoGPT-medium-simpsons | arampacha | 2021-08-04T14:41:54Z | 6 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
tags:
- conversational
---
# DialoGPT-medium-simpsons
This is a version of [DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) fine-tuned on The Simpsons scripts. |
ricardo-filho/bert-portuguese-cased-nli-assin-assin-2 | ricardo-filho | 2021-08-04T13:24:42Z | 3 | 4 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
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, max 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 701 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.SoftmaxLoss.SoftmaxLoss`
Parameters of the fit()-Method:
```
{
"callback": null,
"epochs": 1,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 71,
"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 --> |
microsoft/infoxlm-base | microsoft | 2021-08-04T11:42:14Z | 5,911 | 7 | transformers | [
"transformers",
"pytorch",
"xlm-roberta",
"fill-mask",
"arxiv:2007.07834",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | # InfoXLM
**InfoXLM** (NAACL 2021, [paper](https://arxiv.org/pdf/2007.07834.pdf), [repo](https://github.com/microsoft/unilm/tree/master/infoxlm), [model](https://huggingface.co/microsoft/infoxlm-base)) InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training.
**MD5**
```
b9d214025837250ede2f69c9385f812c config.json
bd6b1f392293f0cd9cd829c02971ecd9 pytorch_model.bin
bf25eb5120ad92ef5c7d8596b5dc4046 sentencepiece.bpe.model
eedbd60a7268b9fc45981b849664f747 tokenizer.json
```
**BibTeX**
```
@inproceedings{chi-etal-2021-infoxlm,
title = "{I}nfo{XLM}: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training",
author={Chi, Zewen and Dong, Li and Wei, Furu and Yang, Nan and Singhal, Saksham and Wang, Wenhui and Song, Xia and Mao, Xian-Ling and Huang, Heyan and Zhou, Ming},
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.naacl-main.280",
doi = "10.18653/v1/2021.naacl-main.280",
pages = "3576--3588",}
``` |
eliza-dukim/bert-base-finetuned-ynat | eliza-dukim | 2021-08-04T10:03:32Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:klue",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- f1
model_index:
- name: bert-base-finetuned-ynat
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: ynat
metric:
name: F1
type: f1
value: 0.8699556378491373
---
<!-- 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-finetuned-ynat
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3741
- F1: 0.8700
## 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: 256
- eval_batch_size: 256
- 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 | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.0 | 179 | 0.4458 | 0.8516 |
| No log | 2.0 | 358 | 0.3741 | 0.8700 |
| 0.385 | 3.0 | 537 | 0.3720 | 0.8693 |
| 0.385 | 4.0 | 716 | 0.3744 | 0.8689 |
| 0.385 | 5.0 | 895 | 0.3801 | 0.8695 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
huggingtweets/dril-gnomeszs-methwaffles | huggingtweets | 2021-08-04T08:11:08Z | 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: https://www.huggingtweets.com/dril-gnomeszs-methwaffles/1628064664319/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/847818629840228354/VXyQHfn0_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/1410800729590308868/UYAyBj1Y_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/1393094522008080385/1urtPrKy_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">wint & Chet & gnome 👼🏻</div>
<div style="text-align: center; font-size: 14px;">@dril-gnomeszs-methwaffles</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 wint & Chet & gnome 👼🏻.
| Data | wint | Chet | gnome 👼🏻 |
| --- | --- | --- | --- |
| Tweets downloaded | 3188 | 1923 | 3219 |
| Retweets | 456 | 664 | 1078 |
| Short tweets | 307 | 211 | 438 |
| Tweets kept | 2425 | 1048 | 1703 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3sv8rebo/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 @dril-gnomeszs-methwaffles's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2d941f4u) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2d941f4u/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/dril-gnomeszs-methwaffles')
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)
|
huggingartists/fascinoma | huggingartists | 2021-08-04T07:45:42Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/fascinoma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/fascinoma
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://assets.genius.com/images/default_avatar_300.png?1627659427')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Fascinoma</div>
<a href="https://genius.com/artists/fascinoma">
<div style="text-align: center; font-size: 14px;">@fascinoma</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Fascinoma.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/fascinoma).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/fascinoma")
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/fascinoma")
model = AutoModelWithLMHead.from_pretrained("huggingartists/fascinoma")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/za989b3u/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 Fascinoma's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/kklye04t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/kklye04t/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='huggingartists/fascinoma')
generator("I am", 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingartists/sugar-ray | huggingartists | 2021-08-04T07:38:52Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/sugar-ray",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/sugar-ray
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/8b5c8fe74f6176047b2b5681e0e0e2d4.273x273x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sugar Ray</div>
<a href="https://genius.com/artists/sugar-ray">
<div style="text-align: center; font-size: 14px;">@sugar-ray</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Sugar Ray.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/sugar-ray).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/sugar-ray")
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/sugar-ray")
model = AutoModelWithLMHead.from_pretrained("huggingartists/sugar-ray")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/10440qj4/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 Sugar Ray's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2n3xk5nv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2n3xk5nv/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='huggingartists/sugar-ray')
generator("I am", 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
ybybybybybybyb/autonlp-revanalysis-6711455 | ybybybybybybyb | 2021-08-04T04:38:05Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"funnel",
"text-classification",
"autonlp",
"ko",
"dataset:ybybybybybybyb/autonlp-data-revanalysis",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: ko
widget:
- text: "I love AutoNLP 🤗"
datasets:
- ybybybybybybyb/autonlp-data-revanalysis
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 6711455
## Validation Metrics
- Loss: 0.8241586089134216
- Accuracy: 0.7835820895522388
- Macro F1: 0.5297383029341792
- Micro F1: 0.783582089552239
- Weighted F1: 0.7130091019920225
- Macro Precision: 0.48787061994609165
- Micro Precision: 0.7835820895522388
- Weighted Precision: 0.6541416904694856
- Macro Recall: 0.5795454545454546
- Micro Recall: 0.7835820895522388
- Weighted Recall: 0.7835820895522388
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/ybybybybybybyb/autonlp-revanalysis-6711455
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("ybybybybybybyb/autonlp-revanalysis-6711455", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("ybybybybybybyb/autonlp-revanalysis-6711455", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
huggingtweets/frepno_mytoff | huggingtweets | 2021-08-03T17:58:24Z | 6 | 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: https://www.huggingtweets.com/frepno_mytoff/1628013500631/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/1410804877538869249/sFFdL9zJ_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">acousticConductor (quadrants filled edition!! ♥♦♠)</div>
<div style="text-align: center; font-size: 14px;">@frepno_mytoff</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 acousticConductor (quadrants filled edition!! ♥♦♠).
| Data | acousticConductor (quadrants filled edition!! ♥♦♠) |
| --- | --- |
| Tweets downloaded | 3218 |
| Retweets | 1944 |
| Short tweets | 487 |
| Tweets kept | 787 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/aujqwhay/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 @frepno_mytoff's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2i5d4dgv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2i5d4dgv/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/frepno_mytoff')
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)
|
vasudevgupta/gsoc-wav2vec2-960h | vasudevgupta | 2021-08-03T15:07:58Z | 9 | 0 | transformers | [
"transformers",
"tf",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z | TensorFlow version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h). Obtained using script from https://github.com/vasudevgupta7/gsoc-wav2vec2. |
liam168/chat-DialoGPT-small-en | liam168 | 2021-08-03T10:25:14Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
widget:
- text: "I got a surprise for you, Morty."
license: apache-2.0
---
# liam168/chat-DialoGPT-small-en
## Model description
用英文聊天数据训练的模型；
### How to use
Now we are ready to try out how the model works as a chatting partner!
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
mode_name = 'liam168/chat-DialoGPT-small-en'
tokenizer = AutoTokenizer.from_pretrained(mode_name)
model = AutoModelForCausalLM.from_pretrained(mode_name)
# Let's chat for 5 lines
for step in range(5):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
# pretty print last ouput tokens from bot
print("Answer: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
```
|
prao/distilbert-base-uncased-finetuned-ner | prao | 2021-08-03T07:15:20Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9842883695807584
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0586
- Precision: 0.9293
- Recall: 0.9385
- F1: 0.9339
- Accuracy: 0.9843
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2436 | 1.0 | 878 | 0.0670 | 0.9190 | 0.9240 | 0.9215 | 0.9815 |
| 0.0505 | 2.0 | 1756 | 0.0591 | 0.9252 | 0.9351 | 0.9301 | 0.9836 |
| 0.0304 | 3.0 | 2634 | 0.0586 | 0.9293 | 0.9385 | 0.9339 | 0.9843 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
Maltehb/aelaectra-danish-electra-small-uncased-ner-dane | Maltehb | 2021-08-03T05:06:18Z | 13 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"electra",
"token-classification",
"ælæctra",
"danish",
"ELECTRA-Small",
"replaced token detection",
"da",
"dataset:DAGW",
"arxiv:2003.10555",
"arxiv:1810.04805",
"arxiv:2005.03521",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language: "da"
tags:
- ælæctra
- pytorch
- danish
- ELECTRA-Small
- replaced token detection
license: "mit"
datasets:
- DAGW
widget:
- text: "Chili Jensen, som bor på Danmarksgade 12, køber chilifrugter fra Netto."
metrics:
- f1
---
# Ælæctra - Finetuned for Named Entity Recognition on the [DaNE dataset](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) by Malte Højmark-Bertelsen.
**Ælæctra** is a Danish Transformer-based language model created to enhance the variety of Danish NLP resources with a more efficient model compared to previous state-of-the-art (SOTA) models.
Ælæctra was pretrained with the ELECTRA-Small (Clark et al., 2020) pretraining approach by using the Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020) and evaluated on Named Entity Recognition (NER) tasks. Since NER only presents a limited picture of Ælæctra's capabilities I am very interested in further evaluations. Therefore, if you employ it for any task, feel free to hit me up your findings!
Ælæctra was, as mentioned, created to enhance the Danish NLP capabilties and please do note how this GitHub still does not support the Danish characters "*Æ, Ø and Å*" as the title of this repository becomes "*-l-ctra*". How ironic.🙂
Here is an example on how to load the finetuned Ælæctra-uncased model for Named Entity Recognition in [PyTorch](https://pytorch.org/) using the [🤗Transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane")
model = AutoModelForTokenClassification.from_pretrained("Maltehb/-l-ctra-danish-electra-small-uncased-ner-dane")
```
### Evaluation of current Danish Language Models
Ælæctra, Danish BERT (DaBERT) and multilingual BERT (mBERT) were evaluated:
| Model | Layers | Hidden Size | Params | AVG NER micro-f1 (DaNE-testset) | Average Inference Time (Sec/Epoch) | Download |
| --- | --- | --- | --- | --- | --- | --- |
| Ælæctra Uncased | 12 | 256 | 13.7M | 78.03 (SD = 1.28) | 10.91 | [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) |
| Ælæctra Cased | 12 | 256 | 14.7M | 80.08 (SD = 0.26) | 10.92 | [Link for model](https://www.dropbox.com/s/cag7prs1nvdchqs/%C3%86l%C3%A6ctra.zip?dl=0) |
| DaBERT | 12 | 768 | 110M | 84.89 (SD = 0.64) | 43.03 | [Link for model](https://www.dropbox.com/s/19cjaoqvv2jicq9/danish_bert_uncased_v2.zip?dl=1) |
| mBERT Uncased | 12 | 768 | 167M | 80.44 (SD = 0.82) | 72.10 | [Link for model](https://storage.googleapis.com/bert_models/2018_11_03/multilingual_L-12_H-768_A-12.zip) |
| mBERT Cased | 12 | 768 | 177M | 83.79 (SD = 0.91) | 70.56 | [Link for model](https://storage.googleapis.com/bert_models/2018_11_23/multi_cased_L-12_H-768_A-12.zip) |
On [DaNE](https://danlp.alexandra.dk/304bd159d5de/datasets/ddt.zip) (Hvingelby et al., 2020) without the *MISC-tag*, Ælæctra scores slightly worse than both cased and uncased Multilingual BERT (Devlin et al., 2019) and Danish BERT (Danish BERT, 2019/2020), however, Ælæctra is less than one third the size, and uses significantly fewer computational resources to pretrain and instantiate.
### Pretraining
To pretrain Ælæctra it is recommended to build a Docker Container from the [Dockerfile](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/). Next, simply follow the [pretraining notebooks](https://github.com/MalteHB/Ælæctra/tree/master/infrastructure/Dockerfile/)
The pretraining was done by utilizing a single NVIDIA Tesla V100 GPU with 16 GiB, endowed by the Danish data company [KMD](https://www.kmd.dk/). The pretraining took approximately 4 days and 9.5 hours for both the cased and uncased model
### Fine-tuning
To fine-tune any Ælæctra model follow the [fine-tuning notebooks](https://github.com/MalteHB/Ælæctra/tree/master/notebooks/fine-tuning/)
### References
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020). ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. ArXiv:2003.10555 [Cs]. http://arxiv.org/abs/2003.10555
Danish BERT. (2020). BotXO. https://github.com/botxo/nordic_bert (Original work published 2019)
Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. ArXiv:1810.04805 [Cs]. http://arxiv.org/abs/1810.04805
Hvingelby, R., Pauli, A. B., Barrett, M., Rosted, C., Lidegaard, L. M., & Søgaard, A. (2020). DaNE: A Named Entity Resource for Danish. Proceedings of the 12th Language Resources and Evaluation Conference, 4597–4604. https://www.aclweb.org/anthology/2020.lrec-1.565
Strømberg-Derczynski, L., Baglini, R., Christiansen, M. H., Ciosici, M. R., Dalsgaard, J. A., Fusaroli, R., Henrichsen, P. J., Hvingelby, R., Kirkedal, A., Kjeldsen, A. S., Ladefoged, C., Nielsen, F. Å., Petersen, M. L., Rystrøm, J. H., & Varab, D. (2020). The Danish Gigaword Project. ArXiv:2005.03521 [Cs]. http://arxiv.org/abs/2005.03521
#### Acknowledgements
As the majority of this repository is build upon [the works](https://github.com/google-research/electra) by the team at Google who created ELECTRA, a HUGE thanks to them is in order.
A Giga thanks also goes out to the incredible people who collected The Danish Gigaword Corpus (Strømberg-Derczynski et al., 2020).
Furthermore, I would like to thank my supervisor [Riccardo Fusaroli](https://github.com/fusaroli) for the support with the thesis, and a special thanks goes out to [Kenneth Enevoldsen](https://github.com/KennethEnevoldsen) for his continuous feedback.
Lastly, i would like to thank KMD, my colleagues from KMD, and my peers and co-students from Cognitive Science for encouriging me to keep on working hard and holding my head up high!
#### Contact
For help or further information feel free to connect with the author Malte Højmark-Bertelsen on [[email protected]](mailto:[email protected]?subject=[GitHub]%20ÆlæctraUncasedNER) or any of the following platforms:
[<img align="left" alt="MalteHB | Twitter" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/twitter.svg" />][twitter]
[<img align="left" alt="MalteHB | LinkedIn" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/linkedin.svg" />][linkedin]
[<img align="left" alt="MalteHB | Instagram" width="22px" src="https://cdn.jsdelivr.net/npm/simple-icons@v3/icons/instagram.svg" />][instagram]
<br />
</details>
[twitter]: https://twitter.com/malteH_B
[instagram]: https://www.instagram.com/maltemusen/
[linkedin]: https://www.linkedin.com/in/malte-h%C3%B8jmark-bertelsen-9a618017b/ |
flax-community/clip-vit-base-patch32_mbart-large-50 | flax-community | 2021-08-02T18:25:58Z | 4 | 2 | transformers | [
"transformers",
"jax",
"tensorboard",
"clip-vision-mbart",
"text2text-generation",
"arxiv:2102.08981",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | # CLIP-Vision-mBART50 Seq2Seq Encoder-Decoder Model
Pretrained CLIP-Vision-mBART50 pre-trained on subset of translated Conceptual-12M image-text pairs using a seq2seq model training objective. 2.5M cleaned English image-text pairs are translated using Marian Model for respective languages to 2.5M examples each in English, French, German and Spanish. We trained CLIP-Vision-mBART50 model during community week hosted by Huggingface 🤗 using JAX/Flax.
## Model description
CLIP-Vision-mBART50 is a modified transformers model which takes in visual embeddings from CLIP-Vision transformer and feeds into the `encoder_hidden_states` of a mBART50 decoder. This is done for deep cross-modal interaction via `cross-attention` between the two modes. The decoder then predicts logits for the `input_ids` provided and can be used for generation.
## Intended uses & limitations❗️
You can use the raw model for encoder decoder network where you want the encoder to encode images and decoder to decode text.
Note that this model is primarily aimed at being fine-tuned on tasks like multi-lingual/mono-lingual image captioning.
### How to use❓
You will need to clone the model from [here](https://github.com/gchhablani/multilingual-image-captioning). An example of usage is shown below:
```python
from torchvision.io import read_image
import numpy as np
import os, wget
from transformers import CLIPProcessor, MBart50TokenizerFast
from model.flax_clip_vision_mbart.modeling_clip_vision_mbart import FlaxCLIPVisionMBartForConditionalGeneration
img = wget("http://images.cocodataset.org/val2017/000000397133.jpg")
img = read_image(img) # reading image
clip_processor = CLIPProcessor.from_pretrained('openai/clip-vit-base-patch32')
clip_outputs = clip_processor(images=img)
clip_outputs['pixel_values'][0] = clip_outputs['pixel_values'][0].transpose(1,2,0) # Need to transpose images as model expected channel last images.
tokenizer = MBart50TokenizerFast.from_pretrained('facebook/mbart-large-50"')
model = FlaxCLIPVisionBertForMaskedLM.from_pretrained('flax-community/clip-vit-base-patch32_mbart-large-50')
output_ids = model.generate(batch["pixel_values"], forced_bos_token_id=tokenizer.lang_code_to_id["es_XX"], num_beams=4, max_length=64).sequences # "es_XX is the language code in which you want the translation
# en_XX: English, fr_XX: French, es_XX: Spanish, de_DE: Deutsch
output_string = tokenizer.batch_decode(output_ids.reshape(-1, 64), skip_special_tokens=True, max_length=64)
output_string # Un restaurante u otro lugar para comer en el Hotel
```
## Training data 🏋🏻♂️
The Multi-lingual image captioning model was trained on a subset of Conceptual 12M dataset by Google:
<br>
<br>
[Conceptual 12M](https://github.com/google-research-datasets/conceptual-12m), Introduced by Changpinyo et al. in [Conceptual 12M: Pushing Web-Scale Image-Text Pre-Training To Recognize Long-Tail Visual Concepts](https://arxiv.org/abs/2102.08981).
The translated dataset can be downloaded from [conceptual-12m-multilingual-marian](https://huggingface.co/datasets/flax-community/conceptual-12m-multilingual-marian). We do not provide images as we do not own any of them. One can download images from the `image_url` section of the original Conceptual 12M dataset.
## Data Cleaning 🧹
Though the original dataset contains 12M image-text pairs, a lot of the URLs are invalid now, and in some cases, images are corrupt or broken. We remove such examples from our data, which leaves us with approximately 10M image-text pairs.
#### **Train set:**
Total data: 10010625 captions, 2502656 images <br>
Language-wise captions distribution: <br>
English: 2502656<br>
Spanish: 2502656<br>
Deutsch: 2502656<br>
French: 2502656<br>
#### **Validation set**
Total data: 110592 captions, 27648 images <br>
Language-wise captions distribution: <br>
English: 27648<br>
Spanish: 27648<br>
Deutsch: 27648<br>
French: 27648<br>
## Training procedure 👨🏻💻
### Training
The model was trained on Google Cloud Engine TPUv3-8 machine (with 335 GB of RAM, 1000 GB of hard drive, 96 CPU cores) **8 v3 TPU cores** for 42K steps with a batch size of 128 and a sequence length of 128. The
optimizer used is Adam with a learning rate of 3e-4, β1 = 0.9, β2 = 0.98 and
ε = 1e-8, a weight decay of 0.01, learning rate warmup for 1,000 steps and linear decay of the learning
rate after.
We tracked experiments using Tensorboard which can be found in `Training Metrics` tab. BLEU scores for languages other than English might be wrongly tracked but the model gives good performance in other languages too as evident from the evaluation scores.
#### **Pretraining Results 📊**
Our model reached **eval loss of ~2.6** around ~60k steps. Here are the BLEU scores (out of 1) for different languages:
|Language |BLEU-1|BLEU-2|BLEU-3|BLEU-4|
|--------------------------|------|------|------|------|
|English | 0.13083| 0.08887| 0.06681 | 0.04899|
|Spanish | 0.15981| 0.09858| 0.06918| 0.04776|
|German | 0.14234| 0.09817| 0.07405| 0.0515|
|French | 0.13021| 0.08862| 0.06598| 0.04647|
Model used: ckpt-51999/
In order to reproduce the results, one can use the [evaluation script](https://github.com/gchhablani/multilingual-image-captioning/blob/main/evaluation.py) available in this project's repository.
## **App Demo**
You can try out our model on 🤗 Huggingface's spaces 🪐 :
[Streamlit app of Multi-lingual Image Captioning model on Huggingface Spaces](https://huggingface.co/spaces/flax-community/multilingual-image-captioning)
## Team Members
- Bhavitvya Malik [@bhavitvyamalik](https://github.com/bhavitvyamalik)
- Gunjan Chhablani [@gchhablani](https://github.com/gchhablani)
## Credits
Thanks to Huggingface 🤗 & Google JAX/FLAX team for such a wonderful community week. Big thanks to [@patrickvonplaten](https://github.com/patrickvonplaten) and [@patil-suraj](https://github.com/patil-suraj) for helping us with our solution during the community week.
<img src=https://pbs.twimg.com/media/E443fPjX0AY1BsR.jpg:large>
|
huggingtweets/the_leonardo_dc | huggingtweets | 2021-08-02T18:13:41Z | 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: https://www.huggingtweets.com/the_leonardo_dc/1627928018016/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/1366829899181412354/UlskX9p8_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">Leonardo DC</div>
<div style="text-align: center; font-size: 14px;">@the_leonardo_dc</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 Leonardo DC.
| Data | Leonardo DC |
| --- | --- |
| Tweets downloaded | 522 |
| Retweets | 414 |
| Short tweets | 2 |
| Tweets kept | 106 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/269jk1ld/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 @the_leonardo_dc's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1ayij55f) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1ayij55f/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/the_leonardo_dc')
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)
|
avneet/distilbert-base-uncased-finetuned-sst2 | avneet | 2021-08-02T16:33:20Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: sst2
metric:
name: Accuracy
type: accuracy
value: 0.9151376146788991
---
<!-- 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-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3651
- Accuracy: 0.9151
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.1902 | 1.0 | 4210 | 0.3102 | 0.9117 |
| 0.1293 | 2.0 | 8420 | 0.3672 | 0.9048 |
| 0.084 | 3.0 | 12630 | 0.3651 | 0.9151 |
| 0.0682 | 4.0 | 16840 | 0.3971 | 0.9037 |
| 0.0438 | 5.0 | 21050 | 0.4720 | 0.9117 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
al00014/distilbert-base-uncased-finetuned-ner | al00014 | 2021-08-02T15:53:31Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:05Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model_index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metric:
name: Accuracy
type: accuracy
value: 0.9833669595056158
---
<!-- 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-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9250
- Recall: 0.9321
- F1: 0.9285
- Accuracy: 0.9834
## 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 | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2399 | 1.0 | 878 | 0.0702 | 0.9118 | 0.9208 | 0.9163 | 0.9805 |
| 0.0503 | 2.0 | 1756 | 0.0614 | 0.9176 | 0.9311 | 0.9243 | 0.9824 |
| 0.0304 | 3.0 | 2634 | 0.0611 | 0.9250 | 0.9321 | 0.9285 | 0.9834 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
mkmoisio/xlm-r-cross-lingual-english-finnish-sts | mkmoisio | 2021-08-02T12:22:25Z | 1 | 0 | sentence-transformers | [
"sentence-transformers",
"sentence-similarity",
"fi",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2022-03-02T23:29:05Z | ---
language:
- fi
- en
tags:
- sentence-similarity
- sentence-transformers
widget:
- source-sentence: "mikä on teidän paras telkkari"
---
An XML-RoBERTa based cross-lingual Sentence-BERT model distilled to cover semantic textual similarity in Finnish in addition to English. At the time of creation there were no models performing better in Finnish STS that I was aware of.
# Usage instructions
This model is essentially an extended SentenceTransformer so instructions described at [sbert.net](https://www.sbert.net) apply.
# The other things
The training setup, data, optimizer parameters, limitations and evaluation is described in Ch 6 [here](http://hdl.handle.net/10138/332588) and [repository](https://github.com/mkmoisio/sts-en-to-fi-distillation).
# Credit
This heavily builds on the work done by [Nils Reimers](https://scholar.google.com/citations?user=57GA3A8AAAAJ&hl=de) et al.
# Contact
Still got questions?
[email protected] |
aristotletan/roberta-base-finetuned-sst2 | aristotletan | 2021-08-02T09:50:03Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:scim",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- scim
metrics:
- accuracy
model_index:
- name: roberta-base-finetuned-sst2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: scim
type: scim
args: eod
metric:
name: Accuracy
type: accuracy
value: 0.9111111111111111
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-finetuned-sst2
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the scim dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4632
- Accuracy: 0.9111
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 90 | 2.0273 | 0.6667 |
| No log | 2.0 | 180 | 0.8802 | 0.8556 |
| No log | 3.0 | 270 | 0.5908 | 0.8889 |
| No log | 4.0 | 360 | 0.4632 | 0.9111 |
| No log | 5.0 | 450 | 0.4294 | 0.9111 |
### Framework versions
- Transformers 4.9.1
- Pytorch 1.9.0+cu102
- Datasets 1.11.0
- Tokenizers 0.10.3
|
andi611/distilbert-base-uncased-qa-boolq | andi611 | 2021-08-02T09:45:17Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:boolq",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- boolq
metrics:
- accuracy
model_index:
- name: distilbert-base-uncased-boolq
results:
- task:
name: Question Answering
type: question-answering
dataset:
name: boolq
type: boolq
args: default
metric:
name: Accuracy
type: accuracy
value: 0.7314984709480122
---
<!-- 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-boolq
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2071
- Accuracy: 0.7315
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6506 | 1.0 | 531 | 0.6075 | 0.6681 |
| 0.575 | 2.0 | 1062 | 0.5816 | 0.6978 |
| 0.4397 | 3.0 | 1593 | 0.6137 | 0.7253 |
| 0.2524 | 4.0 | 2124 | 0.8124 | 0.7466 |
| 0.126 | 5.0 | 2655 | 1.1437 | 0.7370 |
### Framework versions
- Transformers 4.8.2
- Pytorch 1.8.1+cu111
- Datasets 1.8.0
- Tokenizers 0.10.3
|
Galuh/id-journal-gpt2 | Galuh | 2021-08-01T14:07:43Z | 14 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"id",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:04Z | ---
language: id
widget:
- text: "Penelitian ini bertujuan untuk menentukan identitas invertebrata laut dari Perairan Papua dengan teknik DNA barcoding"
---
# Indonesian GPT-2 finetuned on Indonesian academic journals
This is the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian) fine-tuned to abstracts of Indonesian academic journals. All training was done on a TPUv2-8 VM sponsored by [TPU Research Cloud](https://sites.research.google/trc/).
The demo can be found [here](https://huggingface.co/spaces/flax-community/gpt2-indonesian).
## How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness,
we set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='Galuh/id-journal-gpt2')
>>> set_seed(42)
>>> generator("Penelitian ini menggunakan teknik DNA barcoding untuk", max_length=30, num_return_sequences=5)
[{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mendeteksi perubahan genetik bakteri pada udang windu. Empat tahap telah dilakukan, meliputi preparasi media untuk larva,'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk identifikasi gen pengasil flavonoid. Data yang diperoleh dari hasil PCR diidentifikasi dengan teknik sekuensing'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk mengekstraksi fragmen DNA dari sampel kulit buaya dan tulang anjing, di mana proses ini melibatkan karakterisasi enzim yang'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk melakukan transformasi. Tahapan transformasi meliputi seleksi sel dengan urutan (2, 8, 16,..., 18) dan'},
{'generated_text': 'Penelitian ini menggunakan teknik DNA barcoding untuk amplifikasi genom DNA dengan menggunakan primer TG8226 dan TG806. Metode pol'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = GPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('Galuh/id-journal-gpt2')
model = TFGPT2Model.from_pretrained('Galuh/id-journal-gpt2')
text = "Ubah dengan teks apa saja."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
## Limitations and bias
This model is originally the [Indonesian gpt2-small model](https://huggingface.co/flax-community/gpt2-small-indonesian), thus this model is also subject to the same [limitations and bias as the original model](https://huggingface.co/flax-community/gpt2-small-indonesian#limitations-and-bias). More detailed bias and analysis on this specific model is coming soon.
## Training data
The model was trained on a dataset of Indonesian journals. We only trained this model on the abstracts. We extract the abstract by writing a script to find any text that is located between the word "Abstrak" (abstract) and "Kata kunci" (keywords). The extraction script can be found [here](https://github.com/galuhsahid/id-journal-gpt2/). To separate each abstract, we also add an end of text token (`<|endoftext|>`) between each abstract.
The information of the sub-dataset and the distribution of the training and evaluation dataset are as follows:
| split | count | percentage |
| ---------- | ---------- | -------------- |
| train | 146,248 | 90% |
| validation | 16,250 | 10% |
## Training procedure
The model was trained on a TPUv2-8 VM provided by [TPU Research Cloud](https://sites.research.google/trc/). The training duration was `2h 30m 57s`.
### Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| dataset | train loss | eval loss | eval perplexity |
| ---------- | ---------- | -------------- | ---------- |
| Indonesian journals dataset (abstract only) | 2.913 | 2.855 | 17.37 |
### Tracking
The training process was tracked in [TensorBoard](https://huggingface.co/Galuh/id-journal-gpt2/tensorboard). |
huggingtweets/cuckolddna-jennyyoyo92-thaiqos | huggingtweets | 2021-08-01T11:46:03Z | 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: https://www.huggingtweets.com/cuckolddna-jennyyoyo92-thaiqos/1627818315619/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/1255121613357486088/7dEgo0f3_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/1231086579336257536/cwkV33rb_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/1342468924496031745/GQXNyPSq_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">♠️ Jenny Snowbunny ♠️ & 🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K & Cuckold DNA</div>
<div style="text-align: center; font-size: 14px;">@cuckolddna-jennyyoyo92-thaiqos</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 ♠️ Jenny Snowbunny ♠️ & 🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K & Cuckold DNA.
| Data | ♠️ Jenny Snowbunny ♠️ | 🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K | Cuckold DNA |
| --- | --- | --- | --- |
| Tweets downloaded | 222 | 639 | 2928 |
| Retweets | 33 | 247 | 1607 |
| Short tweets | 64 | 37 | 108 |
| Tweets kept | 125 | 355 | 1213 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/21bck17h/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 @cuckolddna-jennyyoyo92-thaiqos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/jf6bm27t) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/jf6bm27t/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/cuckolddna-jennyyoyo92-thaiqos')
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)
|
huggingtweets/hannabbc-hfrost3000-thaiqos | huggingtweets | 2021-08-01T10:38:35Z | 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: 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/1231086579336257536/cwkV33rb_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/1338621721750941699/o0kTXA0A_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/1229217557535756288/jzA5Ph7n_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">🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K & Hanna ♠ & ♠️ Hayley ♠️</div>
<div style="text-align: center; font-size: 14px;">@hannabbc-hfrost3000-thaiqos</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 🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K & Hanna ♠ & ♠️ Hayley ♠️.
| Data | 🇹🇭👸🏽♠️ Thai Queen of Spades ♠️👸🏽🇹🇭 7.25K | Hanna ♠ | ♠️ Hayley ♠️ |
| --- | --- | --- | --- |
| Tweets downloaded | 639 | 1044 | 365 |
| Retweets | 247 | 0 | 114 |
| Short tweets | 37 | 164 | 19 |
| Tweets kept | 355 | 880 | 232 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1512srx0/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 @hannabbc-hfrost3000-thaiqos's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/kzlnl9be) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/kzlnl9be/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/hannabbc-hfrost3000-thaiqos')
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)
|
huggingtweets/snoopdogg | huggingtweets | 2021-08-01T10:18:46Z | 5 | 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: 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/1162394412845944832/iruV4hUN_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">Snoop Dogg</div>
<div style="text-align: center; font-size: 14px;">@snoopdogg</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 Snoop Dogg.
| Data | Snoop Dogg |
| --- | --- |
| Tweets downloaded | 3186 |
| Retweets | 587 |
| Short tweets | 967 |
| Tweets kept | 1632 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/19tw1fi3/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 @snoopdogg's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1a00yt39) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1a00yt39/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/snoopdogg')
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)
|
Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization | Narrativa | 2021-08-01T09:45:32Z | 129 | 6 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"summarization",
"news",
"es",
"dataset:mlsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | summarization | 2022-03-02T23:29:04Z | ---
tags:
- summarization
- news
language: es
datasets:
- mlsum
widget:
- text: 'Al filo de las 22.00 horas del jueves, la Asamblea de Madrid vive un momento sorprendente: Vox decide no apoyar una propuesta del PP en favor del blindaje fiscal de la Comunidad. Se ha roto la unidad de los tres partidos de derechas. Es un hecho excepcional. Desde que arrancó la legislatura, PP, Cs y Vox han votado en bloque casi el 75% de las veces en el pleno de la Cámara. Juntos decidieron la composición de la Mesa de la Asamblea. Juntos invistieron presidenta a Isabel Díaz Ayuso. Y juntos han votado la mayoría de proposiciones no de ley, incluida la que ha marcado el esprint final de la campaña para las elecciones generales: acaban de instar al Gobierno de España a "la ilegalización inmediata" de los partidos separatistas "que atenten contra la unidad de la Nación". Los críticos de Cs no comparten el apoyo al texto de Vox contra el secesionisimo Ese balance retrata una necesidad antes que una complicidad, según fuentes del PP con predicamento en la dirección regional y nacional. Tras casi 15 años gobernando con mayoría absoluta, la formación conservadora vivió como una tortura la pasada legislatura, en la que dependió de Cs para sacar adelante sus iniciativas. El problema se agudizó tras las elecciones autonómicas de mayo. El PP ha tenido que formar con Cs el primer gobierno de coalición de la historia de la región, y ni siquiera con eso le basta para ganar las votaciones de la Cámara. Los dos socios gubernamentales necesitan a Vox, la menos predecible de las tres formaciones. "Tenemos que trabajar juntos defendiendo la unidad del país, por eso no quisimos dejar a Vox solo", dijo ayer Díaz Ayuso para justificar el apoyo de PP y Cs a la proposición de la extrema derecha sobre Cataluña. "Después nosotros llevábamos otra proposición para defender el blindaje fiscal de Madrid, y ahí Vox nos dejó atrás. No permitió que esto saliera. Es un grave error por su parte", prosiguió, recalcando el enfado del PP. "Demuestra que está más en cuestiones electoralistas", subrayó. "Los que pensamos, con nuestras inmensas diferencias, que tenemos cosas en común que nos unen como partidos que queremos Comunidades libres, con bajos impuestos, en las que se viva con seguridad y en paz, tenemos que estar unidos", argumentó. "Y por lo menos nosotros de nuestra línea no nos separamos". Al contrario de lo que está ocurriendo el Ayuntamiento de Madrid, donde el PP y Cs ya han defendido posiciones de voto distintas, pese a compartir el Gobierno, en la Asamblea los partidos de Díaz Ayuso e Ignacio Aguado están actuando con la máxima lealtad en las votaciones del pleno. Otra cosa son las comisiones. Y el caso Avalmadrid. Es en ese terreno donde Cs y Vox están buscando el margen de maniobra necesario para separarse del PP en plena campaña electoral, abandonando a su suerte a su socio para distinguirse ante los electores. —"Usted me ha dejado tirada", le espetó la presidenta de la Comunidad de Madrid a Rocío Monasterio tras saber que Vox permitiría que la izquierda tuviera mayoría en la comisión parlamentaria que investigará los avales concedidos por la empresa semipública entre 2007 y 2018, lo que podría incluir el de 400.000 euros aprobado en 2011, y nunca devuelto al completo, para una empresa participada por el padre de Isabel Díaz Ayuso. "Monasterio no es de fiar. Dice una cosa y hace la contraria", dice una fuente popular sobre las negociaciones mantenidas para repartirse los puestos de las diferentes comisiones, que Vox no cumplió tras buscar un segundo pacto con otras formaciones (que no llegó a buen puerto). Ilegalización de Vox Los tres partidos de derechas también se han enfrentado por la ubicación de Vox en el pleno. Las largas negociaciones para la investidura de Díaz Ayuso dejaron heridas abiertas. Y los diputados de Cs no desaprovechan la oportunidad de lanzar dardos contra los de Vox, pero luego coinciden con ellos en la mayoría de votaciones. Ocurrió, por ejemplo, el jueves, cuando se debatía la polémica proposición para instar al Gobierno nacional a ilegalizar a los partidos separatistas que atenten contra la unidad de España. —"Mostrar nuestra sorpresa ante la presentación por parte de Vox de esta propuesta", lanzó Araceli Gómez, diputada de la formación de Aguado. "Sorprende que planteen ustedes este asunto cuando está también sobre la mesa el debate de su propia ilegalización por atentar contra el ordenamiento jurídico o contra valores constitucionales como la igualdad o la no discriminación". Luego de esa descalificación, y ante la incredulidad de los diputados de los partidos de izquierdas, Cs unió sus votos a los de Vox y a los del PP. La decisión ha provocado polémica interna, como demuestra que Albert Rivera no la apoyara ayer explícitamente. Tampoco ha sido bien acogida por el sector crítico de la formación. Pero ha demostrado una cosa: en Madrid hay tres partidos que casi siempre votan como uno.'
---
# Spanish RoBERTa2RoBERTa (roberta-base-bne) fine-tuned on MLSUM ES for summarization
## Model
[BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) (RoBERTa Checkpoint)
## Dataset
**MLSUM** is the first large-scale MultiLingual SUMmarization dataset. Obtained from online newspapers, it contains 1.5M+ article/summary pairs in five different languages -- namely, French, German, **Spanish**, Russian, Turkish. Together with English newspapers from the popular CNN/Daily mail dataset, the collected data form a large scale multilingual dataset which can enable new research directions for the text summarization community. We report cross-lingual comparative analyses based on state-of-the-art systems. These highlight existing biases which motivate the use of a multi-lingual dataset.
[MLSUM es](https://huggingface.co/datasets/viewer/?dataset=mlsum)
## Results
|Set|Metric| Value|
|----|------|------|
| Test |Rouge2 - mid -precision | 11.42|
| Test | Rouge2 - mid - recall | 10.58 |
| Test | Rouge2 - mid - fmeasure | 10.69|
| Test | Rouge1 - fmeasure | 28.83 |
| Test | RougeL - fmeasure | 23.15 |
Raw metrics using HF/metrics `rouge`:
```python
rouge = datasets.load_metric("rouge")
rouge.compute(predictions=results["pred_summary"], references=results["summary"])
{'rouge1': AggregateScore(low=Score(precision=0.30393366820245, recall=0.27905239591639935, fmeasure=0.283148902808752), mid=Score(precision=0.3068521142101569, recall=0.2817252494122592, fmeasure=0.28560373425206464), high=Score(precision=0.30972608774202665, recall=0.28458152325781716, fmeasure=0.2883786700591887)),
'rougeL': AggregateScore(low=Score(precision=0.24184668819794716, recall=0.22401171380621518, fmeasure=0.22624104698839514), mid=Score(precision=0.24470388406868163, recall=0.22665793214539162, fmeasure=0.2289118878817394), high=Score(precision=0.2476594458951327, recall=0.22932683203591905, fmeasure=0.23153001570662513))}
rouge.compute(predictions=results["pred_summary"], references=results["summary"], rouge_types=["rouge2"])["rouge2"].mid
Score(precision=0.11423200347113865, recall=0.10588038944902506, fmeasure=0.1069921217219595)
```
## Usage
```python
import torch
from transformers import RobertaTokenizerFast, EncoderDecoderModel
device = 'cuda' if torch.cuda.is_available() else 'cpu'
ckpt = 'Narrativa/bsc_roberta2roberta_shared-spanish-finetuned-mlsum-summarization'
tokenizer = RobertaTokenizerFast.from_pretrained(ckpt)
model = EncoderDecoderModel.from_pretrained(ckpt).to(device)
def generate_summary(text):
inputs = tokenizer([text], padding="max_length", truncation=True, max_length=512, return_tensors="pt")
input_ids = inputs.input_ids.to(device)
attention_mask = inputs.attention_mask.to(device)
output = model.generate(input_ids, attention_mask=attention_mask)
return tokenizer.decode(output[0], skip_special_tokens=True)
text = "Your text here..."
generate_summary(text)
```
Created by: [Narrativa](https://www.narrativa.com/)
About Narrativa: Natural Language Generation (NLG) | Gabriele, our machine learning-based platform, builds and deploys natural language solutions. #NLG #AI
|
flax-community/putting-nerf-on-a-diet | flax-community | 2021-08-01T09:33:49Z | 3 | 6 | null | [
"arxiv:2104.00677",
"region:us"
] | null | 2022-03-02T23:29:05Z | # Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis Implementation
[](https://huggingface.co/spaces/flax-community/DietNerf-Demo) [](https://colab.research.google.com/drive/1etYeMTntw5mh3FvJv4Ubb7XUoTtt5J9G?usp=sharing)
<p align="center"><img width="450" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/126361638-4aad58e8-4efb-4fc5-bf78-f53d03799e1e.png"></p>
This project attempted to implement the paper **[Putting NeRF on a Diet](https://arxiv.org/abs/2104.00677)** (DietNeRF) in JAX/Flax.
DietNeRF is designed for rendering quality novel views in few-shot learning scheme, a task that vanilla NeRF (Neural Radiance Field) struggles.
To achieve this, the author coins **Semantic Consistency Loss** to supervise DietNeRF by prior knowledge from CLIP Vision Transformer. Such supervision enables DietNeRF to learn 3D scene reconstruction with CLIP's prior knowledge on 2D views.
Besides this repo, you can check our write-up and demo here:
- ✍️ **[Write-up in Notion](https://steep-cycle-f6b.notion.site/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745)**: more details of DietNeRF and our experiments
- ✨ **[Demo in Hugging Face Space](https://huggingface.co/spaces/flax-community/DietNerf-Demo)**: showcase our trained DietNeRFs by Streamlit
## 🤩 Demo
1. You can check out [our demo in Hugging Face Space](https://huggingface.co/spaces/flax-community/DietNerf-Demo)
2. Or you can set up our Streamlit demo locally (model checkpoints will be fetched automatically upon startup)
```shell
pip install -r requirements_demo.txt
streamlit run app.py
```
<p align="center"><img width="600" height="400" alt="Streamlit Demo" src="assets/space_demo.png"></p>
## ✨ Implementation
Our code is written in JAX/ Flax and mainly based upon [jaxnerf](https://github.com/google-research/google-research/tree/master/jaxnerf) from Google Research. The base code is highly optimized in GPU & TPU. For semantic consistency loss, we utilize pretrained CLIP Vision Transformer from [transformers](https://github.com/huggingface/transformers) library.
To learn more about DietNeRF, our experiments and implementation, you are highly recommended to check out our very detailed **[Notion write-up](https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745)**!
<p align="center"><img width="500" height="600" alt="스크린샷 2021-07-04 오후 4 11 51" src="assets/report_thumbnail.png"></p>
## 🤗 Hugging Face Model Hub Repo
You can also find our project on the [Hugging Face Model Hub Repository](https://huggingface.co/flax-community/putting-nerf-on-a-diet/).
Our JAX/Flax implementation currently supports:
<table class="tg">
<thead>
<tr>
<th class="tg-0lax"><span style="font-weight:bold">Platform</span></th>
<th class="tg-0lax" colspan="2"><span style="font-weight:bold">Single-Host GPU</span></th>
<th class="tg-0lax" colspan="2"><span style="font-weight:bold">Multi-Device TPU</span></th>
</tr>
</thead>
<tbody>
<tr>
<td class="tg-0lax"><span style="font-weight:bold">Type</span></td>
<td class="tg-0lax">Single-Device</td>
<td class="tg-0lax">Multi-Device</td>
<td class="tg-0lax">Single-Host</td>
<td class="tg-0lax">Multi-Host</td>
</tr>
<tr>
<td class="tg-0lax"><span style="font-weight:bold">Training</span></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
</tr>
<tr>
<td class="tg-0lax"><span style="font-weight:bold">Evaluation</span></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
<td class="tg-0lax"><img src="http://storage.googleapis.com/gresearch/jaxnerf/check.png" alt="Supported" width=18px height=18px></td>
</tr>
</tbody>
</table>
## 💻 Installation
```bash
# Clone the repo
git clone https://github.com/codestella/putting-nerf-on-a-diet
# Create a conda environment, note you can use python 3.6-3.8 as
# one of the dependencies (TensorFlow) hasn't supported python 3.9 yet.
conda create --name jaxnerf python=3.6.12; conda activate jaxnerf
# Prepare pip
conda install pip; pip install --upgrade pip
# Install requirements
pip install -r requirements.txt
# [Optional] Install GPU and TPU support for Jax
# Remember to change cuda101 to your CUDA version, e.g. cuda110 for CUDA 11.0.
!pip install --upgrade jax "jax[cuda110]" -f https://storage.googleapis.com/jax-releases/jax_releases.html
# install flax and flax-transformer
pip install flax transformers[flax]
```
## ⚽ Dataset
Download the datasets from the [NeRF official Google Drive](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1).
Please download the `nerf_synthetic.zip` and unzip them
in the place you like. Let's assume they are placed under `/tmp/jaxnerf/data/`.
## 💖 Methods
* 👉👉 You can check VEEEERY detailed explanation about our project on [Notion Report](https://www.notion.so/DietNeRF-Putting-NeRF-on-a-Diet-4aeddae95d054f1d91686f02bdb74745)
<p align="center"><img width="400" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/124376591-b312b780-dce2-11eb-80ad-9129d6f5eedb.png"></p>
Based on the principle
that “a bulldozer is a bulldozer from any perspective”, Our proposed DietNeRF supervises the radiance field from arbitrary poses
(DietNeRF cameras). This is possible because we compute a semantic consistency loss in a feature space capturing high-level
scene attributes, not in pixel space. We extract semantic representations of renderings using the CLIP Vision Transformer, then
maximize similarity with representations of ground-truth views. In
effect, we use prior knowledge about scene semantics learned by
single-view 2D image encoders to constrain a 3D representation.
You can check detail information on the author's paper. Also, you can check the CLIP based semantic loss structure on the following image.
<p align="center"><img width="600" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/126386709-a4ce7ff8-2a68-442f-b4ed-26971fb90e51.png"></p>
Our code used JAX/FLAX framework for implementation. So that it can achieve much speed up than other NeRF codes. At last, our code used hugging face, transformer, CLIP model library.
## 🤟 How to use
```
python -m train \
--data_dir=/PATH/TO/YOUR/SCENE/DATA \ % e.g., nerf_synthetic/lego
--train_dir=/PATH/TO/THE/PLACE/YOU/WANT/TO/SAVE/CHECKPOINTS \
--config=configs/CONFIG_YOU_LIKE
```
You can toggle the semantic loss by “use_semantic_loss” in configuration files.
## 💎 Experimental Results
### ❗ Rendered Rendering images by 8-shot learned Diet-NeRF
DietNeRF has a strong capacity to generalise on novel and challenging views with EXTREMELY SMALL TRAINING SAMPLES!
### HOTDOG / DRUM / SHIP / CHAIR / LEGO / MIC
<img alt="" src="https://user-images.githubusercontent.com/77657524/126976706-caec6d6c-6126-45d0-8680-4c883f71f5bb.png" width="250"/></td><td><img alt="" src="https://user-images.githubusercontent.com/77657524/126976868-183af09a-47b3-4c76-ba20-90e9fef17bcc.png" width="250"/><td><img alt="" src="https://user-images.githubusercontent.com/77657524/126977843-18b4b077-1db0-4287-8e5c-baa10c46e647.png" width="250"/>
<img alt="" src="https://user-images.githubusercontent.com/77657524/126977066-9c99a882-7a46-4a1d-921f-cdb0eee60f39.gif" width="250"/><img alt="" src="https://user-images.githubusercontent.com/77657524/126913553-19ebd2f2-c5f1-4332-a253-950e41cb5229.gif" width="300"/><img alt="" src="https://user-images.githubusercontent.com/77657524/126913559-dfce4b88-84a8-4a0a-91eb-ed12716ab328.gif" width="300"/>
### ❗ Rendered GIF by occluded 14-shot learned NeRF and Diet-NeRF
We made artificial occlusion on the right side of image (Only picked left side training poses).
The reconstruction quality can be compared with this experiment.
DietNeRF shows better quality than Original NeRF when It is occluded.
#### Training poses
<img width="1400" src="https://user-images.githubusercontent.com/26036843/126111980-4f332c87-a7f0-42e0-a355-8e77621bbca4.png">
#### LEGO
[DietNeRF]
<img alt="" src="https://user-images.githubusercontent.com/77657524/126913404-800777f8-8f88-451a-92de-3dda25075206.gif" width="300"/>
[NeRF]
<img alt="" src="https://user-images.githubusercontent.com/77657524/126913412-f10dfb3e-e918-4ff4-aa2c-63529fec91d8.gif" width="300"/>
#### SHIP
[DietNeRF]
<img alt="" src="https://user-images.githubusercontent.com/77657524/126913430-0014a904-6ca1-4a7b-9cd6-6f73b36552fb.gif" width="300"/>
[NeRF]
<img alt="" src="https://user-images.githubusercontent.com/77657524/126913439-2e3128ef-c7ef-4c21-8261-6e3b8fe51f86.gif" width="300"/>
## 👨👧👦 Our Teams
| Teams | Members |
|------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------|
| Project Managing | [Stella Yang](https://github.com/codestella) To Watch Our Project Progress, Please Check [Our Project Notion](https://www.notion.so/Putting-NeRF-on-a-Diet-e0caecea0c2b40c3996c83205baf870d) |
| NeRF Team | [Stella Yang](https://github.com/codestella), [Alex Lau](https://github.com/riven314), [Seunghyun Lee](https://github.com/sseung0703), [Hyunkyu Kim](https://github.com/minus31), [Haswanth Aekula](https://github.com/hassiahk), [JaeYoung Chung](https://github.com/robot0321) |
| CLIP Team | [Seunghyun Lee](https://github.com/sseung0703), [Sasikanth Kotti](https://github.com/ksasi), [Khali Sifullah](https://github.com/khalidsaifullaah) , [Sunghyun Kim](https://github.com/MrBananaHuman) |
| Cloud TPU Team | [Alex Lau](https://github.com/riven314), [Aswin Pyakurel](https://github.com/masapasa), [JaeYoung Chung](https://github.com/robot0321), [Sunghyun Kim](https://github.com/MrBananaHuman) |
* Extremely Don't Sleep Contributors 🤣: [Seunghyun Lee](https://github.com/sseung0703), [Alex Lau](https://github.com/riven314), [Stella Yang](https://github.com/codestella), [Haswanth Aekula](https://github.com/hassiahk)
## 😎 What we improved from original JAX-NeRF : Innovation
- Neural rendering with fewshot images
- Hugging face CLIP based semantic loss loop
- You can choose coarse mlp / coarse + fine mlp training
(coarse + fine is on the `main` branch / coarse is on the `coarse_only` branch)
* coarse + fine : shows good geometric reconstruction
* coarse : shows good PSNR/SSIM result
- Make Video/GIF rendering result, `--generate_gif_only` arg can run fast rendering GIF.
- Cleaning / refactoring the code
- Made multiple models / colab / space for Nice demo
## 💞 Social Impact
- Game Industry
- Augmented Reality Industry
- Virtual Reality Industry
- Graphics Industry
- Online shopping
- Metaverse
- Digital Twin
- Mapping / SLAM
## 🌱 References
This project is based on “JAX-NeRF”.
```
@software{jaxnerf2020github,
author = {Boyang Deng and Jonathan T. Barron and Pratul P. Srinivasan},
title = {{JaxNeRF}: an efficient {JAX} implementation of {NeRF}},
url = {https://github.com/google-research/google-research/tree/master/jaxnerf},
version = {0.0},
year = {2020},
}
```
This project is based on “Putting NeRF on a Diet”.
```
@misc{jain2021putting,
title={Putting NeRF on a Diet: Semantically Consistent Few-Shot View Synthesis},
author={Ajay Jain and Matthew Tancik and Pieter Abbeel},
year={2021},
eprint={2104.00677},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## 🔑 License
[Apache License 2.0](https://github.com/codestella/putting-nerf-on-a-diet/blob/main/LICENSE)
## ❤️ Special Thanks
Our Project is started in the [HuggingFace X GoogleAI (JAX) Community Week Event](https://discuss.huggingface.co/t/open-to-the-community-community-week-using-jax-flax-for-nlp-cv/7104).
Thank you for our mentor Suraj and organizers in JAX/Flax Community Week!
Our team grows up with this community learning experience. It was wonderful time!
<img width="250" alt="스크린샷 2021-07-04 오후 4 11 51" src="https://user-images.githubusercontent.com/77657524/126369170-5664076c-ac99-4157-bc53-b91dfb7ed7e1.jpeg">
[Common Computer AI](https://comcom.ai/en/) sponsored multiple V100 GPUs for our project!
Thank you so much for your support!
<img width="250" alt="스크린샷" src="https://user-images.githubusercontent.com/77657524/126914984-d959be06-19f4-4228-8d3a-a855396b2c3f.jpeg">
|
huggingtweets/ebnhussein1424 | huggingtweets | 2021-08-01T05:43:39Z | 5 | 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: https://www.huggingtweets.com/ebnhussein1424/1627796615447/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/1400100365174030338/UqASw3rD_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">EBN HUSSEIN 🏳️🏴</div>
<div style="text-align: center; font-size: 14px;">@ebnhussein1424</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 EBN HUSSEIN 🏳️🏴.
| Data | EBN HUSSEIN 🏳️🏴 |
| --- | --- |
| Tweets downloaded | 3240 |
| Retweets | 201 |
| Short tweets | 231 |
| Tweets kept | 2808 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3mn8msuv/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 @ebnhussein1424's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2h81akvn) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2h81akvn/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/ebnhussein1424')
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)
|
firebolt/llama_or_what2 | firebolt | 2021-07-31T19:52:32Z | 73 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: llama_or_what2
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.4166666567325592
---
# llama_or_what2
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### alpaca
#### guanaco
#### llama
#### vicuna
 |
firebolt/llama_or_what | firebolt | 2021-07-31T19:27:52Z | 77 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2022-03-02T23:29:05Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: llama_or_what
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.3125
---
# llama_or_what
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### alpaca
#### guanaco
#### llama
#### vicuna
 |
huggingartists/suicideoscope | huggingartists | 2021-07-31T10:55:21Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/suicideoscope",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/suicideoscope
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/86b0ba099a6797bab3deeba685f3dbc2.800x800x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Suicideoscope</div>
<a href="https://genius.com/artists/suicideoscope">
<div style="text-align: center; font-size: 14px;">@suicideoscope</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Suicideoscope.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/suicideoscope).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/suicideoscope")
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/suicideoscope")
model = AutoModelWithLMHead.from_pretrained("huggingartists/suicideoscope")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/17opu10a/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 Suicideoscope's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/2w46luqb) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/2w46luqb/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='huggingartists/suicideoscope')
generator("I am", 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 Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
GKLMIP/electra-laos-base-uncased | GKLMIP | 2021-07-31T06:21:25Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:04Z | The Usage of tokenizer for Lao is in https://github.com/GKLMIP/Pretrained-Models-For-Laos.
|
GKLMIP/bert-laos-base-uncased | GKLMIP | 2021-07-31T06:12:22Z | 5 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | The Usage of tokenizer for Lao is in https://github.com/GKLMIP/Pretrained-Models-For-Laos. |
GKLMIP/electra-khmer-base-uncased | GKLMIP | 2021-07-31T05:29:24Z | 5 | 0 | transformers | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
``` |
mrm8488/bert2bert_shared-spanish-finetuned-paus-x-paraphrasing | mrm8488 | 2021-07-31T05:12:47Z | 18 | 4 | transformers | [
"transformers",
"pytorch",
"encoder-decoder",
"text2text-generation",
"spanish",
"paraphrasing",
"paraphrase",
"es",
"dataset:pausx",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ---
language: es
datasets:
- pausx
tags:
- spanish
- paraphrasing
- paraphrase
widget:
- text: "El pionero suizo John Sutter (1803-1880) llegó a Alta California con otros colonos euroamericanos en agosto de 1839."
---
# Spanish Bert2Bert (shared) fine-tuned on PAUS-X es for paraphrasing |
GKLMIP/bert-khmer-small-uncased-tokenized | GKLMIP | 2021-07-31T04:53:16Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
``` |
GKLMIP/bert-khmer-small-uncased | GKLMIP | 2021-07-31T04:46:38Z | 17 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
``` |
huggingtweets/mr_bubblezzz | huggingtweets | 2021-07-31T04:45:24Z | 5 | 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: https://www.huggingtweets.com/mr_bubblezzz/1627706719452/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/1412634280388296704/71wQ8pT4_400x400.png')">
</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">Mr_Bubblez</div>
<div style="text-align: center; font-size: 14px;">@mr_bubblezzz</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 Mr_Bubblez.
| Data | Mr_Bubblez |
| --- | --- |
| Tweets downloaded | 387 |
| Retweets | 97 |
| Short tweets | 55 |
| Tweets kept | 235 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2abt71za/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 @mr_bubblezzz's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/28jx54ax) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/28jx54ax/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/mr_bubblezzz')
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)
|
GKLMIP/bert-khmer-base-uncased-tokenized | GKLMIP | 2021-07-31T03:07:47Z | 55 | 1 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Khmer
If you use our model, please consider citing our paper:
```
@article{,
author="Jiang, Shengyi
and Fu, Sihui
and Lin, Nankai
and Fu, Yingwen",
title="Pre-trained Models and Evaluation Data for the Khmer Language",
year="2021",
publisher="Tsinghua Science and Technology",
}
``` |
huggingtweets/thisisaito | huggingtweets | 2021-07-31T03:03:34Z | 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: https://www.huggingtweets.com/thisisaito/1627700610096/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/1379854432616247301/meLxK4Wc_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">aito.eth 🥚❤️</div>
<div style="text-align: center; font-size: 14px;">@thisisaito</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 aito.eth 🥚❤️.
| Data | aito.eth 🥚❤️ |
| --- | --- |
| Tweets downloaded | 596 |
| Retweets | 102 |
| Short tweets | 112 |
| Tweets kept | 382 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1hyn9w99/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 @thisisaito's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/uecmgl4h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/uecmgl4h/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/thisisaito')
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)
|
GKLMIP/bert-tagalog-base-uncased | GKLMIP | 2021-07-31T02:14:37Z | 19 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Tagalog
If you use our model, please consider citing our paper:
```
@InProceedings{,
author="Jiang, Shengyi
and Fu, Yingwen
and Lin, Xiaotian
and Lin, Nankai",
title="Pre-trained Language models for Tagalog with Multi-source data",
booktitle="Natural Language Processing and Chinese Computing",
year="2021",
publisher="Springer International Publishing",
address="Cham",
}
``` |
GKLMIP/electra-tagalog-base-uncased | GKLMIP | 2021-07-31T02:14:00Z | 61 | 1 | transformers | [
"transformers",
"pytorch",
"electra",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:04Z | https://github.com/GKLMIP/Pretrained-Models-For-Tagalog
If you use our model, please consider citing our paper:
```
@InProceedings{,
author="Jiang, Shengyi
and Fu, Yingwen
and Lin, Xiaotian
and Lin, Nankai",
title="Pre-trained Language models for Tagalog with Multi-source data",
booktitle="Natural Language Processing and Chinese Computing",
year="2021",
publisher="Springer International Publishing",
address="Cham",
}
``` |
noah-ai/mt5-base-question-generation-vi | noah-ai | 2021-07-31T01:23:40Z | 4 | 1 | transformers | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2022-03-02T23:29:05Z | ## Model description
This model is a sequence-to-sequence question generator that takes an answer and context as an input and generates a question as an output. It is based on a pre-trained mt5-base by [Google](https://github.com/google-research/multilingual-t5) model.
## Training data
The model was fine-tuned on [XQuAD](https://github.com/deepmind/xquad)
## Example usage
```python
from transformers import MT5ForConditionalGeneration, AutoTokenizer
import torch
model = MT5ForConditionalGeneration.from_pretrained("noah-ai/mt5-base-question-generation-vi")
tokenizer = AutoTokenizer.from_pretrained("noah-ai/mt5-base-question-generation-vi")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = model.to(device)
# Content used to create a set of questions
context = '''Thành phố Hồ Chí Minh (còn gọi là Sài Gòn) tên gọi cũ trước 1975 là Sài Gòn hay Sài Gòn-Gia Định là thành phố lớn nhất ở Việt Nam về dân số và quy mô đô thị hóa. Đây còn là trung tâm kinh tế, chính trị, văn hóa và giáo dục tại Việt Nam. Thành phố Hồ Chí Minh là thành phố trực thuộc trung ương thuộc loại đô thị đặc biệt của Việt Nam cùng với thủ đô Hà Nội.Nằm trong vùng chuyển tiếp giữa Đông Nam Bộ và Tây Nam Bộ, thành phố này hiện có 16 quận, 1 thành phố và 5 huyện, tổng diện tích 2.061 km². Theo kết quả điều tra dân số chính thức vào thời điểm ngày một tháng 4 năm 2009 thì dân số thành phố là 7.162.864 người (chiếm 8,34% dân số Việt Nam), mật độ dân số trung bình 3.419 người/km². Đến năm 2019, dân số thành phố tăng lên 8.993.082 người và cũng là nơi có mật độ dân số cao nhất Việt Nam. Tuy nhiên, nếu tính những người cư trú không đăng ký hộ khẩu thì dân số thực tế của thành phố này năm 2018 là gần 14 triệu người.'''
encoding = tokenizer.encode_plus(context, return_tensors="pt")
input_ids, attention_masks = encoding["input_ids"].to(device), encoding["attention_mask"].to(device)
output = model.generate(input_ids=input_ids, attention_mask=attention_masks, max_length=256)
question = tokenizer.decode(output[0], skip_special_tokens=True,clean_up_tokenization_spaces=True)
question
#question: Thành phố hồ chí minh có bao nhiêu quận?
```
> Created by [Duong Thanh Nguyen](https://www.facebook.com/thanhnguyen.dev) |
veronica320/TE-for-Event-Extraction | veronica320 | 2021-07-30T23:11:05Z | 127 | 2 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | # TE-for-Event-Extraction
## Model description
This is a TE model as part of the event extraction system in the ACL2021 paper: [Zero-shot Event Extraction via Transfer Learning: Challenges and Insights](https://aclanthology.org/2021.acl-short.42/). The pretrained architecture is [roberta-large](https://huggingface.co/roberta-large) and the fine-tuning data is [MNLI](https://cims.nyu.edu/~sbowman/multinli/).
The label mapping is:
```
LABEL_0: Contradiction
LABEL_1: Neutral
LABEL_2: Entailment
```
## Demo
To see how the model works, type a sentence and a hypothesis separated by "\<\/s\>\<\/s\>" in the right-hand-side textbox under "Hosted inference API".
Example:
- Input:
```
A car bomb exploded Thursday in a crowded outdoor market in the heart of Jerusalem. </s></s> This text is about an attack.
```
- Output:
```
LABEL_2 (Entailment)
```
## Usage
- To use the TE model independently, follow the [huggingface documentation on AutoModelForSequenceClassification](https://huggingface.co/transformers/task_summary.html#sequence-classification).
- To use it as part of the event extraction system, please check out [our Github repo](https://github.com/veronica320/Zeroshot-Event-Extraction).
### BibTeX entry and citation info
```
@inproceedings{lyu-etal-2021-zero,
title = "Zero-shot Event Extraction via Transfer Learning: {C}hallenges and Insights",
author = "Lyu, Qing and
Zhang, Hongming and
Sulem, Elior and
Roth, Dan",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-short.42",
doi = "10.18653/v1/2021.acl-short.42",
pages = "322--332",
abstract = "Event extraction has long been a challenging task, addressed mostly with supervised methods that require expensive annotation and are not extensible to new event ontologies. In this work, we explore the possibility of zero-shot event extraction by formulating it as a set of Textual Entailment (TE) and/or Question Answering (QA) queries (e.g. {``}A city was attacked{''} entails {``}There is an attack{''}), exploiting pretrained TE/QA models for direct transfer. On ACE-2005 and ERE, our system achieves acceptable results, yet there is still a large gap from supervised approaches, showing that current QA and TE technologies fail in transferring to a different domain. To investigate the reasons behind the gap, we analyze the remaining key challenges, their respective impact, and possible improvement directions.",
}
``` |
abhishek/autonlp-fred2-2682064 | abhishek | 2021-07-30T13:11:02Z | 3 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-fred2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-fred2
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2682064
## Validation Metrics
- Loss: 0.4454168379306793
- Accuracy: 0.8188976377952756
- Precision: 0.8442028985507246
- Recall: 0.7103658536585366
- AUC: 0.8699702146791053
- F1: 0.771523178807947
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-fred2-2682064
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-fred2-2682064", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
abhishek/autonlp-ferd1-2652021 | abhishek | 2021-07-30T12:27:14Z | 6 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autonlp",
"en",
"dataset:abhishek/autonlp-data-ferd1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- abhishek/autonlp-data-ferd1
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 2652021
## Validation Metrics
- Loss: 0.3934604227542877
- Accuracy: 0.8411030860144452
- Precision: 0.8201550387596899
- Recall: 0.8076335877862595
- AUC: 0.8946767157983608
- F1: 0.8138461538461538
## 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 AutoNLP"}' https://api-inference.huggingface.co/models/abhishek/autonlp-ferd1-2652021
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("abhishek/autonlp-ferd1-2652021", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
huggingtweets/_scottcondron | huggingtweets | 2021-07-30T11:31:50Z | 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: https://www.huggingtweets.com/_scottcondron/1627644706283/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/1016982898556141569/R09dBwgv_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">Scott Condron</div>
<div style="text-align: center; font-size: 14px;">@_scottcondron</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 Scott Condron.
| Data | Scott Condron |
| --- | --- |
| Tweets downloaded | 483 |
| Retweets | 59 |
| Short tweets | 15 |
| Tweets kept | 409 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/20roqlwk/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 @_scottcondron's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/y1w16jqr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/y1w16jqr/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/_scottcondron')
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)
|
eli4s/Bert-L12-h240-A12 | eli4s | 2021-07-30T10:39:52Z | 26 | 2 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | This model was pretrained on the bookcorpus dataset using knowledge distillation.
The particularity of this model is that even though it shares the same architecture as BERT, it has a hidden size of 240. Since it has 12 attention heads, the head size (20) is different from the one of the BERT base model (64).
The knowledge distillation was performed using multiple loss functions.
The weights of the model were initialized from scratch.
PS : the tokenizer is the same as the one of the model bert-base-uncased.
To load the model \& tokenizer :
````python
from transformers import AutoModelForMaskedLM, BertTokenizer
model_name = "eli4s/Bert-L12-h240-A12"
model = AutoModelForMaskedLM.from_pretrained(model_name)
tokenizer = BertTokenizer.from_pretrained(model_name)
````
To use it as a masked language model :
````python
import torch
sentence = "Let's have a [MASK]."
model.eval()
inputs = tokenizer([sentence], padding='longest', return_tensors='pt')
output = model(inputs['input_ids'], attention_mask=inputs['attention_mask'])
mask_index = inputs['input_ids'].tolist()[0].index(103)
masked_token = output['logits'][0][mask_index].argmax(axis=-1)
predicted_token = tokenizer.decode(masked_token)
print(predicted_token)
````
Or we can also predict the n most relevant predictions :
````python
top_n = 5
vocab_size = model.config.vocab_size
logits = output['logits'][0][mask_index].tolist()
top_tokens = sorted(list(range(vocab_size)), key=lambda i:logits[i], reverse=True)[:top_n]
tokenizer.decode(top_tokens)
````
|
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