modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-29 12:28:39
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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elgeish/gpt2-medium-arabic-poetry
|
elgeish
| 2021-05-21T15:45:14Z | 13 | 7 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"poetry",
"ar",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: ar
datasets:
- Arabic Poetry Dataset (6th - 21st century)
metrics:
- perplexity
tags:
- text-generation
- poetry
license: apache-2.0
widget:
- text: "للوهلة الأولى قرأت في عينيه"
model-index:
- name: elgeish Arabic GPT2 Medium
results:
- task:
name: Text Generation
type: text-generation
dataset:
name: Arabic Poetry Dataset (6th - 21st century)
type: poetry
args: ar
metrics:
- name: Validation Perplexity
type: perplexity
value: 282.09
---
# GPT2-Medium-Arabic-Poetry
Fine-tuned [aubmindlab/aragpt2-medium](https://huggingface.co/aubmindlab/aragpt2-medium) on
the [Arabic Poetry Dataset (6th - 21st century)](https://www.kaggle.com/fahd09/arabic-poetry-dataset-478-2017)
using 41,922 lines of poetry as the train split and 9,007 (by poets not in the train split) for validation.
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, set_seed
set_seed(42)
model_name = "elgeish/gpt2-medium-arabic-poetry"
model = AutoModelForCausalLM.from_pretrained(model_name).to("cuda")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "للوهلة الأولى قرأت في عينيه"
input_ids = tokenizer.encode(prompt, return_tensors="pt")
samples = model.generate(
input_ids.to("cuda"),
do_sample=True,
early_stopping=True,
max_length=32,
min_length=16,
num_return_sequences=3,
pad_token_id=50256,
repetition_penalty=1.5,
top_k=32,
top_p=0.95,
)
for sample in samples:
print(tokenizer.decode(sample.tolist()))
print("--")
```
Here's the output:
```
للوهلة الأولى قرأت في عينيه عن تلك النسم لم تذكر شيءا فلربما نامت علي كتفيها العصافير وتناثرت اوراق التوت عليها وغابت الوردة من
--
للوهلة الأولى قرأت في عينيه اية نشوة من ناره وهي تنظر الي المستقبل بعيون خلاقة ورسمت خطوطه العريضة علي جبينك العاري رسمت الخطوط الحمر فوق شعرك
--
للوهلة الأولى قرأت في عينيه كل ما كان وما سيكون غدا اذا لم تكن امراة ستكبر كثيرا علي الورق الابيض او لا تري مثلا خطوطا رفيعة فوق صفحة الماء
--
```
|
DebateLabKIT/cript
|
DebateLabKIT
| 2021-05-21T15:40:52Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- gpt2
---
# CRiPT Model (Critical Thinking Intermediarily Pretrained Transformer)
Small version of the trained model (`SYL01-2020-10-24-72K/gpt2-small-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html)
* [GitHub repo](https://github.com/debatelab/aacorpus)
* [paper](https://arxiv.org/pdf/2009.07185)
|
DebateLabKIT/cript-medium
|
DebateLabKIT
| 2021-05-21T15:39:12Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"arxiv:2009.07185",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- gpt2
---
# CRiPT Model Medium (Critical Thinking Intermediarily Pretrained Transformer)
Medium version of the trained model (`SYL01-2020-10-24-72K/gpt2-medium-train03-72K`) presented in the paper "Critical Thinking for Language Models" (Betz, Voigt and Richardson 2020). See also:
* [blog entry](https://debatelab.github.io/journal/critical-thinking-language-models.html)
* [GitHub repo](https://github.com/debatelab/aacorpus)
* [paper](https://arxiv.org/pdf/2009.07185)
|
ceostroff/harry-potter-gpt2-fanfiction
|
ceostroff
| 2021-05-21T14:51:47Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"harry-potter",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- harry-potter
license: mit
---
# Harry Potter Fanfiction Generator
This is a pre-trained GPT-2 generative text model that allows you to generate your own Harry Potter fanfiction, trained off of the top 100 rated fanficition stories. We intend for this to be used for individual fun and experimentation and not as a commercial product.
|
bolbolzaban/gpt2-persian
|
bolbolzaban
| 2021-05-21T14:23:14Z | 883 | 27 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"farsi",
"persian",
"fa",
"doi:10.57967/hf/1207",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: fa
license: apache-2.0
tags:
- farsi
- persian
---
# GPT2-Persian
bolbolzaban/gpt2-persian is gpt2 language model that is trained with hyper parameters similar to standard gpt2-medium with following differences:
1. The context size is reduced from 1024 to 256 sub words in order to make the training affordable
2. Instead of BPE, google sentence piece tokenizor is used for tokenization.
3. The training dataset only include Persian text. All non-persian characters are replaced with especial tokens (e.g [LAT], [URL], [NUM])
Please refer to this [blog post](https://medium.com/@khashei/a-not-so-dangerous-ai-in-the-persian-language-39172a641c84) for further detail.
Also try the model [here](https://huggingface.co/bolbolzaban/gpt2-persian?text=%D8%AF%D8%B1+%DB%8C%DA%A9+%D8%A7%D8%AA%D9%81%D8%A7%D9%82+%D8%B4%DA%AF%D9%81%D8%AA+%D8%A7%D9%86%DA%AF%DB%8C%D8%B2%D8%8C+%D9%BE%DA%98%D9%88%D9%87%D8%B4%DA%AF%D8%B1%D8%A7%D9%86) or on [Bolbolzaban.com](http://www.bolbolzaban.com/text).
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline, AutoTokenizer, GPT2LMHeadModel
tokenizer = AutoTokenizer.from_pretrained('bolbolzaban/gpt2-persian')
model = GPT2LMHeadModel.from_pretrained('bolbolzaban/gpt2-persian')
generator = pipeline('text-generation', model, tokenizer=tokenizer, config={'max_length':256})
sample = generator('در یک اتفاق شگفت انگیز، پژوهشگران')
```
If you are using Tensorflow import TFGPT2LMHeadModel instead of GPT2LMHeadModel.
## Fine-tuning
Find a basic fine-tuning example on this [Github Repo](https://github.com/khashei/bolbolzaban-gpt2-persian).
## Special Tokens
gpt-persian is trained for the purpose of research on Persian poetry. Because of that all english words and numbers are replaced with special tokens and only standard Persian alphabet is used as part of input text. Here is one example:
Original text: اگر آیفون یا آیپد شما دارای سیستم عامل iOS 14.3 یا iPadOS 14.3 یا نسخههای جدیدتر باشد
Text used in training: اگر آیفون یا آیپد شما دارای سیستم عامل [LAT] [NUM] یا [LAT] [NUM] یا نسخههای جدیدتر باشد
Please consider normalizing your input text using [Hazm](https://github.com/sobhe/hazm) or similar libraries and ensure only Persian characters are provided as input.
If you want to use classical Persian poetry as input use [BOM] (begining of mesra) at the beginning of each verse (مصرع) followed by [EOS] (end of statement) at the end of each couplet (بیت).
See following links for example:
[[BOM] توانا بود](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF)
[[BOM] توانا بود هر که دانا بود [BOM]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیر](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1)
[[BOM] توانا بود هر که دانا بود [BOM] ز دانش دل پیربرنا بود [EOS]](https://huggingface.co/bolbolzaban/gpt2-persian?text=%5BBOM%5D+%D8%AA%D9%88%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%D9%87%D8%B1+%DA%A9%D9%87+%D8%AF%D8%A7%D9%86%D8%A7+%D8%A8%D9%88%D8%AF+%5BBOM%5D+%D8%B2+%D8%AF%D8%A7%D9%86%D8%B4+%D8%AF%D9%84+%D9%BE%DB%8C%D8%B1%D8%A8%D8%B1%D9%86%D8%A7+%D8%A8%D9%88%D8%AF++%5BEOS%5D)
If you like to know about structure of classical Persian poetry refer to these [blog posts](https://medium.com/@khashei).
## Acknowledgment
This project is supported by Cloud TPUs from Google’s TensorFlow Research Cloud (TFRC).
## Citation and Reference
Please reference "bolbolzaban.com" website if you are using gpt2-persian in your research or commertial application.
## Contacts
Please reachout on [Linkedin](https://www.linkedin.com/in/khashei/) or [Telegram](https://t.me/khasheia) if you have any question or need any help to use the model.
Follow [Bolbolzaban](http://bolbolzaban.com/about) on [Twitter](https://twitter.com/bolbol_zaban), [Telegram](https://t.me/bolbol_zaban) or [Instagram](https://www.instagram.com/bolbolzaban/)
|
bigjoedata/rockbot355M
|
bigjoedata
| 2021-05-21T14:17:25Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# 🎸 🥁 Rockbot 🎤 🎧
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise and Rockbot is no exception. You may see NSFW lyrics unexpectedly. I have made no attempts to censor. Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable.
Oh, and generation is resource intense and can be slow. I set governors on song length to keep generation time somewhat reasonable. You may adjust song length and other parameters on the left or check out [Github](https://github.com/bigjoedata/rockbot) to spin up your own Rockbot.
Just have fun.
[Demo](https://share.streamlit.io/bigjoedata/rockbot/main/src/main.py) Adjust settings to increase speed
[Github](https://github.com/bigjoedata/rockbot)
[GPT-2 124M version Model page on Hugging Face](https://huggingface.co/bigjoedata/rockbot)
[DistilGPT2 version Model page on Hugging Face](https://huggingface.co/bigjoedata/rockbot-distilgpt2/) This is leaner with the tradeoff being that the lyrics are more simplistic.
🎹 🪘 🎷 🎺 🪗 🪕 🎻
## Background
With the shutdown of [Google Play Music](https://en.wikipedia.org/wiki/Google_Play_Music) I used Google's takeout function to gather the metadata from artists I've listened to over the past several years. I wanted to take advantage of this bounty to build something fun. I scraped the top 50 lyrics for artists I'd listened to at least once from [Genius](https://genius.com/), then fine tuned [GPT-2's](https://openai.com/blog/better-language-models/) 124M token model using the [AITextGen](https://github.com/minimaxir/aitextgen) framework after considerable post-processing. For more on generation, see [here.](https://huggingface.co/blog/how-to-generate)
### Full Tech Stack
[Google Play Music](https://en.wikipedia.org/wiki/Google_Play_Music) (R.I.P.).
[Python](https://www.python.org/).
[Streamlit](https://www.streamlit.io/).
[GPT-2](https://openai.com/blog/better-language-models/).
[AITextGen](https://github.com/minimaxir/aitextgen).
[Pandas](https://pandas.pydata.org/).
[LyricsGenius](https://lyricsgenius.readthedocs.io/en/master/).
[Google Colab](https://colab.research.google.com/) (GPU based Training).
[Knime](https://www.knime.com/) (data cleaning).
## How to Use The Model
Please refer to [AITextGen](https://github.com/minimaxir/aitextgen) for much better documentation.
### Training Parameters Used
ai.train("lyrics.txt",
line_by_line=False,
from_cache=False,
num_steps=10000,
generate_every=2000,
save_every=2000,
save_gdrive=False,
learning_rate=1e-3,
batch_size=3,
eos_token="<|endoftext|>",
#fp16=True
)
### To Use
Generate With Prompt (Use Title Case):
Song Name
BY
Artist Name
|
bigjoedata/rockbot
|
bigjoedata
| 2021-05-21T14:15:36Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# 🎸 🥁 Rockbot 🎤 🎧
A [GPT-2](https://openai.com/blog/better-language-models/) based lyrics generator fine-tuned on the writing styles of 16000 songs by 270 artists across MANY genres (not just rock).
**Instructions:** Type in a fake song title, pick an artist, click "Generate".
Most language models are imprecise and Rockbot is no exception. You may see NSFW lyrics unexpectedly. I have made no attempts to censor. Generated lyrics may be repetitive and/or incoherent at times, but hopefully you'll encounter something interesting or memorable.
Oh, and generation is resource intense and can be slow. I set governors on song length to keep generation time somewhat reasonable. You may adjust song length and other parameters on the left or check out [Github](https://github.com/bigjoedata/rockbot) to spin up your own Rockbot.
Just have fun.
[Demo](https://share.streamlit.io/bigjoedata/rockbot/main/src/main.py) Adjust settings to increase speed
[Github](https://github.com/bigjoedata/rockbot)
[GPT-2 124M version Model page on Hugging Face](https://huggingface.co/bigjoedata/rockbot)
[DistilGPT2 version Model page on Hugging Face](https://huggingface.co/bigjoedata/rockbot-distilgpt2/) This is leaner with the tradeoff being that the lyrics are more simplistic.
🎹 🪘 🎷 🎺 🪗 🪕 🎻
## Background
With the shutdown of [Google Play Music](https://en.wikipedia.org/wiki/Google_Play_Music) I used Google's takeout function to gather the metadata from artists I've listened to over the past several years. I wanted to take advantage of this bounty to build something fun. I scraped the top 50 lyrics for artists I'd listened to at least once from [Genius](https://genius.com/), then fine tuned [GPT-2's](https://openai.com/blog/better-language-models/) 124M token model using the [AITextGen](https://github.com/minimaxir/aitextgen) framework after considerable post-processing. For more on generation, see [here.](https://huggingface.co/blog/how-to-generate)
### Full Tech Stack
[Google Play Music](https://en.wikipedia.org/wiki/Google_Play_Music) (R.I.P.).
[Python](https://www.python.org/).
[Streamlit](https://www.streamlit.io/).
[GPT-2](https://openai.com/blog/better-language-models/).
[AITextGen](https://github.com/minimaxir/aitextgen).
[Pandas](https://pandas.pydata.org/).
[LyricsGenius](https://lyricsgenius.readthedocs.io/en/master/).
[Google Colab](https://colab.research.google.com/) (GPU based Training).
[Knime](https://www.knime.com/) (data cleaning).
## How to Use The Model
Please refer to [AITextGen](https://github.com/minimaxir/aitextgen) for much better documentation.
### Training Parameters Used
ai.train("lyrics.txt",
line_by_line=False,
from_cache=False,
num_steps=10000,
generate_every=2000,
save_every=2000,
save_gdrive=False,
learning_rate=1e-3,
batch_size=3,
eos_token="<|endoftext|>",
#fp16=True
)
### To Use
Generate With Prompt (Use Title Case):
Song Name
BY
Artist Name
|
Dongjae/mrc2reader
|
Dongjae
| 2021-05-21T13:25:57Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:04Z |
The Reader model is for Korean Question Answering
The backbone model is deepset/xlm-roberta-large-squad2.
It is a finetuned model with KorQuAD-v1 dataset.
As a result of verification using KorQuAD evaluation dataset, it showed approximately 87% and 92% respectively for the EM score and F1 score.
Thank you
|
anonymous-german-nlp/german-gpt2
|
anonymous-german-nlp
| 2021-05-21T13:20:42Z | 338 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"de",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: de
widget:
- text: "Heute ist sehr schönes Wetter in"
license: mit
---
# German GPT-2 model
**Note**: This model was de-anonymized and now lives at:
https://huggingface.co/dbmdz/german-gpt2
Please use the new model name instead!
|
aliosm/ComVE-gpt2-large
|
aliosm
| 2021-05-21T13:12:02Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"exbert",
"commonsense",
"semeval2020",
"comve",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- gpt2
- exbert
- commonsense
- semeval2020
- comve
license: "mit"
datasets:
- https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation
metrics:
- bleu
widget:
- text: "Chicken can swim in water. <|continue|>"
---
# ComVE-gpt2-large
## Model description
Finetuned model on Commonsense Validation and Explanation (ComVE) dataset introduced in [SemEval2020 Task4](https://competitions.codalab.org/competitions/21080) using a causal language modeling (CLM) objective.
The model is able to generate a reason why a given natural language statement is against commonsense.
## Intended uses & limitations
You can use the raw model for text generation to generate reasons why natural language statements are against commonsense.
#### How to use
You can use this model directly to generate reasons why the given statement is against commonsense using [`generate.sh`](https://github.com/AliOsm/SemEval2020-Task4-ComVE/tree/master/TaskC-Generation) script.
*Note:* make sure that you are using version `2.4.1` of `transformers` package. Newer versions has some issue in text generation and the model repeats the last token generated again and again.
#### Limitations and bias
The model biased to negate the entered sentence usually instead of producing a factual reason.
## Training data
The model is initialized from the [gpt2-large](https://github.com/huggingface/transformers/blob/master/model_cards/gpt2-README.md) model and finetuned using [ComVE](https://github.com/wangcunxiang/SemEval2020-Task4-Commonsense-Validation-and-Explanation) dataset which contains 10K against commonsense sentences, each of them is paired with three reference reasons.
## Training procedure
Each natural language statement that against commonsense is concatenated with its reference reason with `<|conteniue|>` as a separator, then the model finetuned using CLM objective.
The model trained on Nvidia Tesla P100 GPU from Google Colab platform with 5e-5 learning rate, 5 epochs, 128 maximum sequence length and 64 batch size.
<center>
<img src="https://i.imgur.com/xKbrwBC.png">
</center>
## Eval results
The model achieved 16.5110/15.9299 BLEU scores on SemEval2020 Task4: Commonsense Validation and Explanation development and testing dataset.
### BibTeX entry and citation info
```bibtex
@article{fadel2020justers,
title={JUSTers at SemEval-2020 Task 4: Evaluating Transformer Models Against Commonsense Validation and Explanation},
author={Fadel, Ali and Al-Ayyoub, Mahmoud and Cambria, Erik},
year={2020}
}
```
<a href="https://huggingface.co/exbert/?model=aliosm/ComVE-gpt2-large">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
ainize/gpt2-rnm-with-spongebob
|
ainize
| 2021-05-21T12:09:02Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Fine tuning data 2: https://www.kaggle.com/mikhailgaerlan/spongebob-squarepants-completed-transcripts
Base model: e-tony/gpt2-rnm
Epoch: 2
Train runtime: 790.0612 secs
Loss: 2.8569
API page: [Ainize](https://ainize.ai/fpem123/GPT2-Rick-N-Morty-with-SpongeBob?branch=master)
Demo page: [End-point](https://master-gpt2-rick-n-morty-with-sponge-bob-fpem123.endpoint.ainize.ai/)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
ainize/gpt2-rnm-with-season-1
|
ainize
| 2021-05-21T12:08:00Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 3
Train runtime: 7.1779 secs
Loss: 2.5694
Training notebook: [Colab](https://colab.research.google.com/drive/12NvO1SIZevF8ybJqfN9O21I3i9bU1dOO#scrollTo=KUsyn02WWmf5)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
ainize/gpt2-rnm-with-only-rick
|
ainize
| 2021-05-21T12:06:44Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
### Model information
Fine tuning data 1: https://www.kaggle.com/andradaolteanu/rickmorty-scripts
Base model: e-tony/gpt2-rnm
Epoch: 1
Train runtime: 3.4982 secs
Loss: 3.0894
Training notebook: [Colab](https://colab.research.google.com/drive/1RawVxulLETFicWMY0YANUdP-H-e7Eeyc)
### ===Teachable NLP=== ###
To train a GPT-2 model, write code and require GPU resources, but can easily fine-tune and get an API to use the model here for free.
Teachable NLP: [Teachable NLP](https://ainize.ai/teachable-nlp)
Tutorial: [Tutorial](https://forum.ainetwork.ai/t/teachable-nlp-how-to-use-teachable-nlp/65?utm_source=community&utm_medium=huggingface&utm_campaign=model&utm_content=teachable%20nlp)
|
adresgezgini/turkish-gpt-2
|
adresgezgini
| 2021-05-21T11:53:09Z | 14 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
AdresGezgini Inc. R&D Center Turkish GPT-2 Model Trained with Turkish Wiki Corpus for 10 Epochs
|
SIC98/GPT2-python-code-generator
|
SIC98
| 2021-05-21T11:13:58Z | 17 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
Github
- https://github.com/SIC98/GPT2-python-code-generator
|
Ochiroo/tiny_mn_gpt
|
Ochiroo
| 2021-05-21T10:59:47Z | 6 | 1 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"mn",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: mn
---
# GPT2-Mongolia
## Model description
GPT-2 is a transformers model pretrained on a very small corpus of Mongolian news 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 trained to guess the next word in sentences.
## How to use
```python
import tensorflow as tf
from transformers import GPT2Config, TFGPT2LMHeadModel, GPT2Tokenizer
from transformers import WEIGHTS_NAME, CONFIG_NAME
tokenizer = GPT2Tokenizer.from_pretrained('Ochiroo/tiny_mn_gpt')
model = TFGPT2LMHeadModel.from_pretrained('Ochiroo/tiny_mn_gpt')
text = "Намайг Эрдэнэ-Очир гэдэг. Би"
input_ids = tokenizer.encode(text, return_tensors='tf')
beam_outputs = model.generate(
input_ids,
max_length = 25,
num_beams = 5,
temperature = 0.7,
no_repeat_ngram_size=2,
num_return_sequences=5
)
print(tokenizer.decode(beam_outputs[0]))
```
## Training data and biases
Trained on 500MB of Mongolian news dataset (IKON) on RTX 2060.
|
Meli/GPT2-Prompt
|
Meli
| 2021-05-21T10:55:36Z | 311 | 11 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language:
- en
tags:
- gpt2
- text-generation
pipeline_tag: text-generation
widget:
- text: "A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]"
- text: "A kid doodling in a math class accidentally creates the world's first functional magic circle in centuries. [endprompt]"
---
# GPT-2 Story Generator
## Model description
Generate a short story from an input prompt.
Put the vocab ` [endprompt]` after your input.
Example of an input:
```
A person with a high school education gets sent back into the 1600s and tries to explain science and technology to the people. [endprompt]
```
#### Limitations and bias
The data we used to train was collected from reddit, so it could be very biased towards young, white, male demographic.
## Training data
The data was collected from scraping reddit.
|
HooshvareLab/gpt2-fa-poetry
|
HooshvareLab
| 2021-05-21T10:50:14Z | 65 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: fa
license: apache-2.0
widget:
- text: "<s>رودکی<|startoftext|>"
- text: "<s>فردوسی<|startoftext|>"
- text: "<s>خیام<|startoftext|>"
- text: "<s>عطار<|startoftext|>"
- text: "<s>نظامی<|startoftext|>"
---
# Persian Poet GPT2
## Poets
The model can generate poetry based on your favorite poet, and you need to add one of the following lines as the input the box on the right side or follow the [fine-tuning notebook](https://colab.research.google.com/github/hooshvare/parsgpt/blob/master/notebooks/Persian_Poetry_FineTuning.ipynb).
```text
<s>رودکی<|startoftext|>
<s>فردوسی<|startoftext|>
<s>کسایی<|startoftext|>
<s>ناصرخسرو<|startoftext|>
<s>منوچهری<|startoftext|>
<s>فرخی سیستانی<|startoftext|>
<s>مسعود سعد سلمان<|startoftext|>
<s>ابوسعید ابوالخیر<|startoftext|>
<s>باباطاهر<|startoftext|>
<s>فخرالدین اسعد گرگانی<|startoftext|>
<s>اسدی توسی<|startoftext|>
<s>هجویری<|startoftext|>
<s>خیام<|startoftext|>
<s>نظامی<|startoftext|>
<s>عطار<|startoftext|>
<s>سنایی<|startoftext|>
<s>خاقانی<|startoftext|>
<s>انوری<|startoftext|>
<s>عبدالواسع جبلی<|startoftext|>
<s>نصرالله منشی<|startoftext|>
<s>مهستی گنجوی<|startoftext|>
<s>باباافضل کاشانی<|startoftext|>
<s>مولوی<|startoftext|>
<s>سعدی<|startoftext|>
<s>خواجوی کرمانی<|startoftext|>
<s>عراقی<|startoftext|>
<s>سیف فرغانی<|startoftext|>
<s>حافظ<|startoftext|>
<s>اوحدی<|startoftext|>
<s>شیخ محمود شبستری<|startoftext|>
<s>عبید زاکانی<|startoftext|>
<s>امیرخسرو دهلوی<|startoftext|>
<s>سلمان ساوجی<|startoftext|>
<s>شاه نعمتالله ولی<|startoftext|>
<s>جامی<|startoftext|>
<s>هلالی جغتایی<|startoftext|>
<s>وحشی<|startoftext|>
<s>محتشم کاشانی<|startoftext|>
<s>شیخ بهایی<|startoftext|>
<s>عرفی<|startoftext|>
<s>رضیالدین آرتیمانی<|startoftext|>
<s>صائب تبریزی<|startoftext|>
<s>فیض کاشانی<|startoftext|>
<s>بیدل دهلوی<|startoftext|>
<s>هاتف اصفهانی<|startoftext|>
<s>فروغی بسطامی<|startoftext|>
<s>قاآنی<|startoftext|>
<s>ملا هادی سبزواری<|startoftext|>
<s>پروین اعتصامی<|startoftext|>
<s>ملکالشعرای بهار<|startoftext|>
<s>شهریار<|startoftext|>
<s>رهی معیری<|startoftext|>
<s>اقبال لاهوری<|startoftext|>
<s>خلیلالله خلیلی<|startoftext|>
<s>شاطرعباس صبوحی<|startoftext|>
<s>نیما یوشیج ( آوای آزاد )<|startoftext|>
<s>احمد شاملو<|startoftext|>
<s>سهراب سپهری<|startoftext|>
<s>فروغ فرخزاد<|startoftext|>
<s>سیمین بهبهانی<|startoftext|>
<s>مهدی اخوان ثالث<|startoftext|>
<s>محمدحسن بارق شفیعی<|startoftext|>
<s>شیون فومنی<|startoftext|>
<s>کامبیز صدیقی کسمایی<|startoftext|>
<s>بهرام سالکی<|startoftext|>
<s>عبدالقهّار عاصی<|startoftext|>
<s>اِ لیـــار (جبار محمدی )<|startoftext|>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo.
|
HooshvareLab/gpt2-fa-comment
|
HooshvareLab
| 2021-05-21T10:47:25Z | 30 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"fa",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: fa
license: apache-2.0
widget:
- text: "<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>"
- text: "<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>"
- text: "<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>"
- text: "<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>"
---
# Persian Comment Generator
The model can generate comments based on your aspects, and the model was fine-tuned on [persiannlp/parsinlu](https://github.com/persiannlp/parsinlu). Currently, the model only supports aspects in the food and movie scope. You can see the whole aspects in the following section.
## Comments Aspects
```text
<s>نمونه دیدگاه هم خوب هم بد به طور کلی <sep>
<s>نمونه دیدگاه خوب به طور کلی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی به طور کلی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال <sep>
<s>نمونه دیدگاه منفی از نظر طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم به طور کلی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم <sep>
<s>نمونه دیدگاه خیلی منفی به طور کلی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه منفی از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و طعم <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر طعم و کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و کیفیت و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و ارسال <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش خرید و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و ارزش خرید و طعم <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارزش غذایی و بسته بندی <sep>
<s>نمونه دیدگاه خوب از نظر ارسال و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال <sep>
<s>نمونه دیدگاه نظری ندارم از نظر طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر طعم و بسته بندی و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و کیفیت و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه نظری ندارم از نظر ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و ارزش خرید و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و طعم و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کیفیت و ارسال و بسته بندی <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و کیفیت <sep>
<s>نمونه دیدگاه خوب از نظر کیفیت و ارسال <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و ارزش غذایی <sep>
<s>نمونه دیدگاه خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارسال و بسته بندی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه منفی از نظر بسته بندی و ارزش غذایی <sep>
<s>نمونه دیدگاه منفی از نظر طعم و کیفیت و ارزش خرید <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش خرید و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کیفیت و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و کیفیت و طعم <sep>
<s>نمونه دیدگاه منفی از نظر کیفیت و طعم و ارزش غذایی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارسال و کیفیت و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر طعم و بسته بندی و ارسال <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و بسته بندی و طعم <sep>
<s>نمونه دیدگاه خیلی خوب از نظر ارزش غذایی و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش غذایی و کیفیت <sep>
<s>نمونه دیدگاه منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کیفیت و طعم و بسته بندی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر ارسال و ارزش خرید <sep>
<s>نمونه دیدگاه خیلی منفی از نظر ارزش خرید و طعم و کیفیت <sep>
<s>نمونه دیدگاه خیلی منفی از نظر طعم و ارسال <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری و کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صحنه و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر صحنه و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان <sep>
<s>نمونه دیدگاه خوب از نظر بازی و داستان و موسیقی و کارگردانی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر بازی و داستان <sep>
<s>نمونه دیدگاه خوب از نظر فیلمبرداری و صحنه و موسیقی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه نظری ندارم از نظر بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و بازی و صحنه <sep>
<s>نمونه دیدگاه خوب از نظر کارگردانی و داستان و بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و صحنه <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و صحنه و داستان و کارگردانی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر داستان و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر فیلمبرداری و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر کارگردانی و بازی و داستان و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر موسیقی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و داستان و موسیقی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و صدا و صحنه و داستان <sep>
<s>نمونه دیدگاه خوب از نظر داستان و کارگردانی و بازی <sep>
<s>نمونه دیدگاه منفی از نظر داستان و بازی و کارگردانی <sep>
<s>نمونه دیدگاه خوب از نظر داستان و بازی و موسیقی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر کارگردانی و بازی و صحنه <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر کارگردانی و بازی <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خوب از نظر موسیقی و داستان <sep>
<s>نمونه دیدگاه منفی از نظر موسیقی و بازی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر صدا و بازی <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و صحنه و فیلمبرداری <sep>
<s>نمونه دیدگاه خیلی منفی از نظر بازی و فیلمبرداری و داستان و کارگردانی <sep>
<s>نمونه دیدگاه خیلی منفی از نظر صحنه <sep>
<s>نمونه دیدگاه منفی از نظر داستان و صحنه <sep>
<s>نمونه دیدگاه منفی از نظر بازی و صحنه و صدا <sep>
<s>نمونه دیدگاه خیلی منفی از نظر فیلمبرداری و صدا <sep>
<s>نمونه دیدگاه خیلی خوب از نظر موسیقی <sep>
<s>نمونه دیدگاه خوب از نظر بازی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و فیلمبرداری و موسیقی و کارگردانی و داستان <sep>
<s>نمونه دیدگاه هم خوب هم بد از نظر فیلمبرداری و داستان و بازی <sep>
<s>نمونه دیدگاه منفی از نظر صحنه و فیلمبرداری و داستان <sep>
<s>نمونه دیدگاه خیلی خوب از نظر بازی و کارگردانی و داستان <sep>
```
## Questions?
Post a Github issue on the [ParsGPT2 Issues](https://github.com/hooshvare/parsgpt/issues) repo.
|
Ferch423/gpt2-small-portuguese-wikipediabio
|
Ferch423
| 2021-05-21T09:42:53Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"pt",
"wikipedia",
"finetuning",
"dataset:wikipedia",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
language: "pt"
tags:
- pt
- wikipedia
- gpt2
- finetuning
datasets:
- wikipedia
widget:
- "André Um"
- "Maria do Santos"
- "Roberto Carlos"
licence: "mit"
---
# GPT2-SMALL-PORTUGUESE-WIKIPEDIABIO
This is a finetuned model version of gpt2-small-portuguese(https://huggingface.co/pierreguillou/gpt2-small-portuguese) by pierreguillou.
It was trained on a person abstract dataset extracted from DBPEDIA (over 100000 people's abstracts). The model is intended as a simple and fun experiment for generating texts abstracts based on ordinary people's names.
|
CallumRai/HansardGPT2
|
CallumRai
| 2021-05-21T09:33:25Z | 15 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
A PyTorch GPT-2 model trained on hansard from 2019-01-01 to 2020-06-01
For more information see: https://github.com/CallumRai/Hansard/
|
lg/ghpy_40k
|
lg
| 2021-05-20T23:37:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# This model is probably not what you're looking for.
|
lg/openinstruct_1k1
|
lg
| 2021-05-20T23:37:33Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# This model is probably not what you're looking for.
|
lg/fexp_1
|
lg
| 2021-05-20T23:37:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
# This model is probably not what you're looking for.
|
tlemberger/sd-ner
|
tlemberger
| 2021-05-20T22:31:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"token-classification",
"token classification",
"dataset:EMBO/sd-panels",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language:
- english
thumbnail:
tags:
- token classification
license:
datasets:
- EMBO/sd-panels
metrics:
-
---
# sd-ner
## Model description
This model is a [RoBERTa base model](https://huggingface.co/roberta-base) that was further trained using a masked language modeling task on a compendium of english scientific textual examples from the life sciences using the [BioLang dataset](https://huggingface.co/datasets/EMBO/biolang) and fine-tuned for token classification on the SourceData [sd-panels](https://huggingface.co/datasets/EMBO/sd-panels) dataset to perform Named Entity Recognition of bioentities.
## Intended uses & limitations
#### How to use
The intended use of this model is for Named Entity Recognition of biological entitie used in SourceData annotations (https://sourcedata.embo.org), including small molecules, gene products (genes and proteins), subcellular components, cell line and cell types, organ and tissues, species as well as experimental methods.
To have a quick check of the model:
```python
from transformers import pipeline, RobertaTokenizerFast, RobertaForTokenClassification
example = """<s> F. Western blot of input and eluates of Upf1 domains purification in a Nmd4-HA strain. The band with the # might corresponds to a dimer of Upf1-CH, bands marked with a star correspond to residual signal with the anti-HA antibodies (Nmd4). Fragments in the eluate have a smaller size because the protein A part of the tag was removed by digestion with the TEV protease. G6PDH served as a loading control in the input samples </s>"""
tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base', max_len=512)
model = RobertaForTokenClassification.from_pretrained('EMBO/sd-ner')
ner = pipeline('ner', model, tokenizer=tokenizer)
res = ner(example)
for r in res:
print(r['word'], r['entity'])
```
#### Limitations and bias
The model must be used with the `roberta-base` tokenizer.
## Training data
The model was trained for token classification using the [EMBO/sd-panels dataset](https://huggingface.co/datasets/EMBO/biolang) wich includes manually annotated examples.
## Training procedure
The training was run on a NVIDIA DGX Station with 4XTesla V100 GPUs.
Training code is available at https://github.com/source-data/soda-roberta
- Command: `python -m tokcl.train /data/json/sd_panels NER --num_train_epochs=3.5`
- Tokenizer vocab size: 50265
- Training data: EMBO/biolang MLM
- Training with 31410 examples.
- Evaluating on 8861 examples.
- Training on 15 features: O, I-SMALL_MOLECULE, B-SMALL_MOLECULE, I-GENEPROD, B-GENEPROD, I-SUBCELLULAR, B-SUBCELLULAR, I-CELL, B-CELL, I-TISSUE, B-TISSUE, I-ORGANISM, B-ORGANISM, I-EXP_ASSAY, B-EXP_ASSAY
- Epochs: 3.5
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
## Eval results
On test set with `sklearn.metrics`:
```
precision recall f1-score support
CELL 0.77 0.81 0.79 3477
EXP_ASSAY 0.71 0.70 0.71 7049
GENEPROD 0.86 0.90 0.88 16140
ORGANISM 0.80 0.82 0.81 2759
SMALL_MOLECULE 0.78 0.82 0.80 4446
SUBCELLULAR 0.71 0.75 0.73 2125
TISSUE 0.70 0.75 0.73 1971
micro avg 0.79 0.82 0.81 37967
macro avg 0.76 0.79 0.78 37967
weighted avg 0.79 0.82 0.81 37967
```
|
textattack/roberta-base-rotten_tomatoes
|
textattack
| 2021-05-20T22:18:23Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
## roberta-base fine-tuned with TextAttack on the rotten_tomatoes dataset
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 128, a learning
rate of 5e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9033771106941839, as measured by the
eval set accuracy, found after 9 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-rotten-tomatoes
|
textattack
| 2021-05-20T22:17:29Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classificationusing TextAttack
and the rotten_tomatoes dataset loaded using the `nlp` library. The model was fine-tuned
for 10 epochs with a batch size of 64, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9033771106941839, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-WNLI
|
textattack
| 2021-05-20T22:13:50Z | 42 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.5633802816901409, as measured by the
eval set accuracy, found after 0 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/roberta-base-MRPC
|
textattack
| 2021-05-20T22:07:47Z | 206 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `roberta-base` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9117647058823529, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
sramasamy8/testModel
|
sramasamy8
| 2021-05-20T20:58:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"exbert",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1810.04805",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- exbert
license: apache-2.0
datasets:
- bookcorpus
- wikipedia
---
# BERT base model (uncased)
Pretrained model on English language using a masked language modeling (MLM) objective. It was introduced in
[this paper](https://arxiv.org/abs/1810.04805) and first released in
[this repository](https://github.com/google-research/bert). This model is uncased: it does not make a difference
between english and English.
Disclaimer: The team releasing BERT did not write a model card for this model so this model card has been written by
the Hugging Face team.
## Model description
BERT is a transformers model pretrained on a large corpus of English 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 English 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 BERT 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 downstream task. See the [model hub](https://huggingface.co/models?filter=bert) to look for
fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked)
to make decisions, such as sequence classification, token classification or question answering. For tasks such as text
generation you should look at model like GPT2.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>> from transformers import pipeline
>>> unmasker = pipeline('fill-mask', model='bert-base-uncased')
>>> unmasker("Hello I'm a [MASK] model.")
[{'sequence': "[CLS] hello i'm a fashion model. [SEP]",
'score': 0.1073106899857521,
'token': 4827,
'token_str': 'fashion'},
{'sequence': "[CLS] hello i'm a role model. [SEP]",
'score': 0.08774490654468536,
'token': 2535,
'token_str': 'role'},
{'sequence': "[CLS] hello i'm a new model. [SEP]",
'score': 0.05338378623127937,
'token': 2047,
'token_str': 'new'},
{'sequence': "[CLS] hello i'm a super model. [SEP]",
'score': 0.04667217284440994,
'token': 3565,
'token_str': 'super'},
{'sequence': "[CLS] hello i'm a fine model. [SEP]",
'score': 0.027095865458250046,
'token': 2986,
'token_str': 'fine'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = BertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
model = TFBertModel.from_pretrained("bert-base-uncased")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
Even if the training data used for this model could be characterized as fairly neutral, this model can have biased
predictions:
```python
>>> 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'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The BERT model was pretrained on [BookCorpus](https://yknzhu.wixsite.com/mbweb), a dataset consisting of 11,038
unpublished books and [English Wikipedia](https://en.wikipedia.org/wiki/English_Wikipedia) (excluding lists, tables and
headers).
## Training procedure
### Preprocessing
The texts are lowercased and tokenized using WordPiece and a vocabulary size of 30,000. 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 sentences in the original corpus and in
the other cases, it's another random sentence in the corpus. Note that what is considered a sentence here is a
consecutive span of text usually longer than a single sentence. The only constrain is that the result with the two
"sentences" has a combined length of 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 4 cloud TPUs in Pod configuration (16 TPU chips total) for one million steps with a batch size
of 256. The sequence length was limited to 128 tokens for 90% of the steps and 512 for the remaining 10%. The optimizer
used is Adam with a learning rate of 1e-4, \\(\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.
## Evaluation results
When fine-tuned on downstream tasks, this model achieves the following results:
Glue test results:
| Task | MNLI-(m/mm) | QQP | QNLI | SST-2 | CoLA | STS-B | MRPC | RTE | Average |
|:----:|:-----------:|:----:|:----:|:-----:|:----:|:-----:|:----:|:----:|:-------:|
| | 84.6/83.4 | 71.2 | 90.5 | 93.5 | 52.1 | 85.8 | 88.9 | 66.4 | 79.6 |
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1810-04805,
author = {Jacob Devlin and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {{BERT:} Pre-training of Deep Bidirectional Transformers for Language
Understanding},
journal = {CoRR},
volume = {abs/1810.04805},
year = {2018},
url = {http://arxiv.org/abs/1810.04805},
archivePrefix = {arXiv},
eprint = {1810.04805},
timestamp = {Tue, 30 Oct 2018 20:39:56 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1810-04805.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
<a href="https://huggingface.co/exbert/?model=bert-base-uncased">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
pulp/CHILDES-ParentBERTo
|
pulp
| 2021-05-20T19:46:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
The language model trained on a fill-mask task with all the North American parent's data in CHILDES.
The parent's data can be found here: https://github.com/xiaomeng-ma/CHILDES
|
nyu-mll/roberta-med-small-1M-3
|
nyu-mll
| 2021-05-20T19:09:09Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-med-small-1M-2
|
nyu-mll
| 2021-05-20T19:07:56Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-1B-2
|
nyu-mll
| 2021-05-20T19:04:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-10M-1
|
nyu-mll
| 2021-05-20T18:57:10Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-100M-3
|
nyu-mll
| 2021-05-20T18:56:02Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nyu-mll/roberta-base-100M-2
|
nyu-mll
| 2021-05-20T18:54:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa Pretrained on Smaller Datasets
We pretrain RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). We release 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: We combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
### Hyperparameters and Validation Perplexity
The hyperparameters and validation perplexities corresponding to each model are as follows:
| Model Name | Training Size | Model Size | Max Steps | Batch Size | Validation Perplexity |
|--------------------------|---------------|------------|-----------|------------|-----------------------|
| [roberta-base-1B-1][link-roberta-base-1B-1] | 1B | BASE | 100K | 512 | 3.93 |
| [roberta-base-1B-2][link-roberta-base-1B-2] | 1B | BASE | 31K | 1024 | 4.25 |
| [roberta-base-1B-3][link-roberta-base-1B-3] | 1B | BASE | 31K | 4096 | 3.84 |
| [roberta-base-100M-1][link-roberta-base-100M-1] | 100M | BASE | 100K | 512 | 4.99 |
| [roberta-base-100M-2][link-roberta-base-100M-2] | 100M | BASE | 31K | 1024 | 4.61 |
| [roberta-base-100M-3][link-roberta-base-100M-3] | 100M | BASE | 31K | 512 | 5.02 |
| [roberta-base-10M-1][link-roberta-base-10M-1] | 10M | BASE | 10K | 1024 | 11.31 |
| [roberta-base-10M-2][link-roberta-base-10M-2] | 10M | BASE | 10K | 512 | 10.78 |
| [roberta-base-10M-3][link-roberta-base-10M-3] | 10M | BASE | 31K | 512 | 11.58 |
| [roberta-med-small-1M-1][link-roberta-med-small-1M-1] | 1M | MED-SMALL | 100K | 512 | 153.38 |
| [roberta-med-small-1M-2][link-roberta-med-small-1M-2] | 1M | MED-SMALL | 10K | 512 | 134.18 |
| [roberta-med-small-1M-3][link-roberta-med-small-1M-3] | 1M | MED-SMALL | 31K | 512 | 139.39 |
The hyperparameters corresponding to model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P |
|------------|----|----|-----|------|------|
| BASE | 12 | 12 | 768 | 3072 | 125M |
| MED-SMALL | 6 | 8 | 512 | 2048 | 45M |
(AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters.)
For other hyperparameters, we select:
- Peak Learning rate: 5e-4
- Warmup Steps: 6% of max steps
- Dropout: 0.1
[link-roberta-med-small-1M-1]: https://huggingface.co/nyu-mll/roberta-med-small-1M-1
[link-roberta-med-small-1M-2]: https://huggingface.co/nyu-mll/roberta-med-small-1M-2
[link-roberta-med-small-1M-3]: https://huggingface.co/nyu-mll/roberta-med-small-1M-3
[link-roberta-base-10M-1]: https://huggingface.co/nyu-mll/roberta-base-10M-1
[link-roberta-base-10M-2]: https://huggingface.co/nyu-mll/roberta-base-10M-2
[link-roberta-base-10M-3]: https://huggingface.co/nyu-mll/roberta-base-10M-3
[link-roberta-base-100M-1]: https://huggingface.co/nyu-mll/roberta-base-100M-1
[link-roberta-base-100M-2]: https://huggingface.co/nyu-mll/roberta-base-100M-2
[link-roberta-base-100M-3]: https://huggingface.co/nyu-mll/roberta-base-100M-3
[link-roberta-base-1B-1]: https://huggingface.co/nyu-mll/roberta-base-1B-1
[link-roberta-base-1B-2]: https://huggingface.co/nyu-mll/roberta-base-1B-2
[link-roberta-base-1B-3]: https://huggingface.co/nyu-mll/roberta-base-1B-3
|
nkoh01/MSRoberta
|
nkoh01
| 2021-05-20T18:51:20Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# MSRoBERTa
Fine-tuned RoBERTa MLM model for [`Miscrosoft Sentence Completion Challenge`](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/MSR_SCCD.pdf). This model case-sensitive following the `Roberta-base` model.
# Model description (taken from: [here](https://huggingface.co/roberta-base))
RoBERTa is a transformers model pretrained on a large corpus of English 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 the Masked language modeling (MLM) objective. 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.
This way, the model learns an inner representation of the English 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 BERT model as inputs.
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
from transformers import pipeline,AutoModelForMaskedLM,AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nkoh01/MSRoberta")
model = AutoModelForMaskedLM.from_pretrained("nkoh01/MSRoberta")
unmasker = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
unmasker("Hello, it is a <mask> to meet you.")
[{'score': 0.9508683085441589,
'sequence': 'hello, it is a pleasure to meet you.',
'token': 10483,
'token_str': ' pleasure'},
{'score': 0.015089659951627254,
'sequence': 'hello, it is a privilege to meet you.',
'token': 9951,
'token_str': ' privilege'},
{'score': 0.013942377641797066,
'sequence': 'hello, it is a joy to meet you.',
'token': 5823,
'token_str': ' joy'},
{'score': 0.006964420434087515,
'sequence': 'hello, it is a delight to meet you.',
'token': 13213,
'token_str': ' delight'},
{'score': 0.0024567877408117056,
'sequence': 'hello, it is a honour to meet you.',
'token': 6671,
'token_str': ' honour'}]
```
## Installations
Make sure you run `!pip install transformers` command to install the transformers library before running the commands above.
## Bias and limitations
Under construction.
|
neurocode/IsRoBERTa
|
neurocode
| 2021-05-20T18:50:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"is",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: is
datasets:
- Icelandic portion of the OSCAR corpus from INRIA
- oscar
---
# IsRoBERTa a RoBERTa-like masked language model
Probably the first icelandic transformer language model!
## Overview
**Language:** Icelandic
**Downstream-task:** masked-lm
**Training data:** OSCAR corpus
**Code:** See [here](https://github.com/neurocode-io/icelandic-language-model)
**Infrastructure**: 1x Nvidia K80
## Hyperparameters
```
per_device_train_batch_size = 48
n_epochs = 1
vocab_size = 52.000
max_position_embeddings = 514
num_attention_heads = 12
num_hidden_layers = 6
type_vocab_size = 1
learning_rate=0.00005
```
## Usage
### In Transformers
```python
from transformers import (
pipeline,
AutoTokenizer,
AutoModelWithLMHead
)
model_name = "neurocode/IsRoBERTa"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelWithLMHead.from_pretrained(model_name)
>>> fill_mask = pipeline(
... "fill-mask",
... model=model,
... tokenizer=tokenizer
... )
>>> result = fill_mask("Hann fór út að <mask>.")
>>> result
[
{'sequence': '<s>Hann fór út að nýju.</s>', 'score': 0.03395755589008331, 'token': 2219, 'token_str': 'Ġnýju'},
{'sequence': '<s>Hann fór út að undanförnu.</s>', 'score': 0.029087543487548828, 'token': 7590, 'token_str': 'Ġundanförnu'},
{'sequence': '<s>Hann fór út að lokum.</s>', 'score': 0.024420788511633873, 'token': 4384, 'token_str': 'Ġlokum'},
{'sequence': '<s>Hann fór út að þessu.</s>', 'score': 0.021231256425380707, 'token': 921, 'token_str': 'Ġþessu'},
{'sequence': '<s>Hann fór út að honum.</s>', 'score': 0.0205782949924469, 'token': 1136, 'token_str': 'Ġhonum'}
]
```
## Authors
Bobby Donchev: `contact [at] donchev.is`
Elena Cramer: `elena.cramer [at] neurocode.io`
## About us
We bring AI software for our customers live
Our focus: AI software development
Get in touch:
[LinkedIn](https://de.linkedin.com/company/neurocodeio) | [Website](https://neurocode.io)
|
mrm8488/roberta-large-finetuned-wsc
|
mrm8488
| 2021-05-20T18:30:59Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1905.06290",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# RoBERTa (large) fine-tuned on Winograd Schema Challenge (WSC) data
Step from its original [repo](https://github.com/pytorch/fairseq/blob/master/examples/roberta/wsc/README.md)
The following instructions can be used to finetune RoBERTa on the WSC training
data provided by [SuperGLUE](https://super.gluebenchmark.com/).
Note that there is high variance in the results. For our GLUE/SuperGLUE
submission we swept over the learning rate (1e-5, 2e-5, 3e-5), batch size (16,
32, 64) and total number of updates (500, 1000, 2000, 3000), as well as the
random seed. Out of ~100 runs we chose the best 7 models and ensembled them.
**Approach:** The instructions below use a slightly different loss function than
what's described in the original RoBERTa arXiv paper. In particular,
[Kocijan et al. (2019)](https://arxiv.org/abs/1905.06290) introduce a margin
ranking loss between `(query, candidate)` pairs with tunable hyperparameters
alpha and beta. This is supported in our code as well with the `--wsc-alpha` and
`--wsc-beta` arguments. However, we achieved slightly better (and more robust)
results on the development set by instead using a single cross entropy loss term
over the log-probabilities for the query and all mined candidates. **The
candidates are mined using spaCy from each input sentence in isolation, so the
approach remains strictly pointwise.** This reduces the number of
hyperparameters and our best model achieved 92.3% development set accuracy,
compared to ~90% accuracy for the margin loss. Later versions of the RoBERTa
arXiv paper will describe this updated formulation.
### 1) Download the WSC data from the SuperGLUE website:
```bash
wget https://dl.fbaipublicfiles.com/glue/superglue/data/v2/WSC.zip
unzip WSC.zip
# we also need to copy the RoBERTa dictionary into the same directory
wget -O WSC/dict.txt https://dl.fbaipublicfiles.com/fairseq/gpt2_bpe/dict.txt
```
### 2) Finetune over the provided training data:
```bash
TOTAL_NUM_UPDATES=2000 # Total number of training steps.
WARMUP_UPDATES=250 # Linearly increase LR over this many steps.
LR=2e-05 # Peak LR for polynomial LR scheduler.
MAX_SENTENCES=16 # Batch size per GPU.
SEED=1 # Random seed.
ROBERTA_PATH=/path/to/roberta/model.pt
# we use the --user-dir option to load the task and criterion
# from the examples/roberta/wsc directory:
FAIRSEQ_PATH=/path/to/fairseq
FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc
CUDA_VISIBLE_DEVICES=0,1,2,3 fairseq-train WSC/ \
--restore-file $ROBERTA_PATH \
--reset-optimizer --reset-dataloader --reset-meters \
--no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \
--best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
--valid-subset val \
--fp16 --ddp-backend no_c10d \
--user-dir $FAIRSEQ_USER_DIR \
--task wsc --criterion wsc --wsc-cross-entropy \
--arch roberta_large --bpe gpt2 --max-positions 512 \
--dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
--optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
--lr-scheduler polynomial_decay --lr $LR \
--warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \
--max-sentences $MAX_SENTENCES \
--max-update $TOTAL_NUM_UPDATES \
--log-format simple --log-interval 100 \
--seed $SEED
```
The above command assumes training on 4 GPUs, but you can achieve the same
results on a single GPU by adding `--update-freq=4`.
### 3) Evaluate
```python
from fairseq.models.roberta import RobertaModel
from examples.roberta.wsc import wsc_utils # also loads WSC task and criterion
roberta = RobertaModel.from_pretrained('checkpoints', 'checkpoint_best.pt', 'WSC/')
roberta.cuda()
nsamples, ncorrect = 0, 0
for sentence, label in wsc_utils.jsonl_iterator('WSC/val.jsonl', eval=True):
pred = roberta.disambiguate_pronoun(sentence)
nsamples += 1
if pred == label:
ncorrect += 1
print('Accuracy: ' + str(ncorrect / float(nsamples)))
# Accuracy: 0.9230769230769231
```
## RoBERTa training on WinoGrande dataset
We have also provided `winogrande` task and criterion for finetuning on the
[WinoGrande](https://mosaic.allenai.org/projects/winogrande) like datasets
where there are always two candidates and one is correct.
It's more efficient implementation for such subcases.
```bash
TOTAL_NUM_UPDATES=23750 # Total number of training steps.
WARMUP_UPDATES=2375 # Linearly increase LR over this many steps.
LR=1e-05 # Peak LR for polynomial LR scheduler.
MAX_SENTENCES=32 # Batch size per GPU.
SEED=1 # Random seed.
ROBERTA_PATH=/path/to/roberta/model.pt
# we use the --user-dir option to load the task and criterion
# from the examples/roberta/wsc directory:
FAIRSEQ_PATH=/path/to/fairseq
FAIRSEQ_USER_DIR=${FAIRSEQ_PATH}/examples/roberta/wsc
cd fairseq
CUDA_VISIBLE_DEVICES=0 fairseq-train winogrande_1.0/ \
--restore-file $ROBERTA_PATH \
--reset-optimizer --reset-dataloader --reset-meters \
--no-epoch-checkpoints --no-last-checkpoints --no-save-optimizer-state \
--best-checkpoint-metric accuracy --maximize-best-checkpoint-metric \
--valid-subset val \
--fp16 --ddp-backend no_c10d \
--user-dir $FAIRSEQ_USER_DIR \
--task winogrande --criterion winogrande \
--wsc-margin-alpha 5.0 --wsc-margin-beta 0.4 \
--arch roberta_large --bpe gpt2 --max-positions 512 \
--dropout 0.1 --attention-dropout 0.1 --weight-decay 0.01 \
--optimizer adam --adam-betas '(0.9, 0.98)' --adam-eps 1e-06 \
--lr-scheduler polynomial_decay --lr $LR \
--warmup-updates $WARMUP_UPDATES --total-num-update $TOTAL_NUM_UPDATES \
--max-sentences $MAX_SENTENCES \
--max-update $TOTAL_NUM_UPDATES \
--log-format simple --log-interval 100
```
[Original repo](https://github.com/pytorch/fairseq/tree/master/examples/roberta/wsc)
|
mrm8488/roberta-base-1B-1-finetuned-squadv2
|
mrm8488
| 2021-05-20T18:27:20Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"en",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
---
# RoBERTa-base (1B-1) + SQuAD v2 ❓
[roberta-base-1B-1](https://huggingface.co/nyu-mll/roberta-base-1B-1) fine-tuned on [SQUAD v2 dataset](https://rajpurkar.github.io/SQuAD-explorer/explore/v2.0/dev/) for **Q&A** downstream task.
## Details of the downstream task (Q&A) - Model 🧠
RoBERTa Pretrained on Smaller Datasets
[NYU Machine Learning for Language](https://huggingface.co/nyu-mll) pretrained RoBERTa on smaller datasets (1M, 10M, 100M, 1B tokens). They released 3 models with lowest perplexities for each pretraining data size out of 25 runs (or 10 in the case of 1B tokens). The pretraining data reproduces that of BERT: They combine English Wikipedia and a reproduction of BookCorpus using texts from smashwords in a ratio of approximately 3:1.
## Details of the downstream task (Q&A) - Dataset 📚
**S**tanford **Q**uestion **A**nswering **D**ataset (SQuAD) is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable.
**SQuAD2.0** combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
## Model training 🏋️
The model was trained on a Tesla P100 GPU and 25GB of RAM with the following command:
```bash
python transformers/examples/question-answering/run_squad.py \
--model_type roberta \
--model_name_or_path 'nyu-mll/roberta-base-1B-1' \
--do_eval \
--do_train \
--do_lower_case \
--train_file /content/dataset/train-v2.0.json \
--predict_file /content/dataset/dev-v2.0.json \
--per_gpu_train_batch_size 16 \
--learning_rate 3e-5 \
--num_train_epochs 10 \
--max_seq_length 384 \
--doc_stride 128 \
--output_dir /content/output \
--overwrite_output_dir \
--save_steps 1000 \
--version_2_with_negative
```
## Test set Results 🧾
| Metric | # Value |
| ------ | --------- |
| **EM** | **64.86** |
| **F1** | **68.99** |
```json
{
'exact': 64.86145034953255,
'f1': 68.9902640378272,
'total': 11873,
'HasAns_exact': 64.03508771929825,
'HasAns_f1': 72.3045554860189,
'HasAns_total': 5928,
'NoAns_exact': 65.68544995794785,
'NoAns_f1': 65.68544995794785,
'NoAns_total': 5945,
'best_exact': 64.86987282068559,
'best_exact_thresh': 0.0,
'best_f1': 68.99868650898054,
'best_f1_thresh': 0.0
}
```
### Model in action 🚀
Fast usage with **pipelines**:
```python
from transformers import pipeline
QnA_pipeline = pipeline('question-answering', model='mrm8488/roberta-base-1B-1-finetuned-squadv2')
QnA_pipeline({
'context': 'A new strain of flu that has the potential to become a pandemic has been identified in China by scientists.',
'question': 'What has been discovered by scientists from China ?'
})
# Output:
{'answer': 'A new strain of flu', 'end': 19, 'score': 0.7145650685380576,'start': 0}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/codebert-base-finetuned-detect-insecure-code
|
mrm8488
| 2021-05-20T18:19:02Z | 166 | 28 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"dataset:codexglue",
"arxiv:2002.08155",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
datasets:
- codexglue
---
# CodeBERT fine-tuned for Insecure Code Detection 💾⛔
[codebert-base](https://huggingface.co/microsoft/codebert-base) fine-tuned on [CodeXGLUE -- Defect Detection](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) dataset for **Insecure Code Detection** downstream task.
## Details of [CodeBERT](https://arxiv.org/abs/2002.08155)
We present CodeBERT, a bimodal pre-trained model for programming language (PL) and nat-ural language (NL). CodeBERT learns general-purpose representations that support downstream NL-PL applications such as natural language codesearch, code documentation generation, etc. We develop CodeBERT with Transformer-based neural architecture, and train it with a hybrid objective function that incorporates the pre-training task of replaced token detection, which is to detect plausible alternatives sampled from generators. This enables us to utilize both bimodal data of NL-PL pairs and unimodal data, where the former provides input tokens for model training while the latter helps to learn better generators. We evaluate CodeBERT on two NL-PL applications by fine-tuning model parameters. Results show that CodeBERT achieves state-of-the-art performance on both natural language code search and code documentation generation tasks. Furthermore, to investigate what type of knowledge is learned in CodeBERT, we construct a dataset for NL-PL probing, and evaluate in a zero-shot setting where parameters of pre-trained models are fixed. Results show that CodeBERT performs better than previous pre-trained models on NL-PL probing.
## Details of the downstream task (code classification) - Dataset 📚
Given a source code, the task is to identify whether it is an insecure code that may attack software systems, such as resource leaks, use-after-free vulnerabilities and DoS attack. We treat the task as binary classification (0/1), where 1 stands for insecure code and 0 for secure code.
The [dataset](https://github.com/microsoft/CodeXGLUE/tree/main/Code-Code/Defect-detection) used comes from the paper [*Devign*: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks](http://papers.nips.cc/paper/9209-devign-effective-vulnerability-identification-by-learning-comprehensive-program-semantics-via-graph-neural-networks.pdf). All projects are combined and splitted 80%/10%/10% for training/dev/test.
Data statistics of the dataset are shown in the below table:
| | #Examples |
| ----- | :-------: |
| Train | 21,854 |
| Dev | 2,732 |
| Test | 2,732 |
## Test set metrics 🧾
| Methods | ACC |
| -------- | :-------: |
| BiLSTM | 59.37 |
| TextCNN | 60.69 |
| [RoBERTa](https://arxiv.org/pdf/1907.11692.pdf) | 61.05 |
| [CodeBERT](https://arxiv.org/pdf/2002.08155.pdf) | 62.08 |
| [Ours](https://huggingface.co/mrm8488/codebert-base-finetuned-detect-insecure-code) | **65.30** |
## Model in Action 🚀
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
tokenizer = AutoTokenizer.from_pretrained('mrm8488/codebert-base-finetuned-detect-insecure-code')
model = AutoModelForSequenceClassification.from_pretrained('mrm8488/codebert-base-finetuned-detect-insecure-code')
inputs = tokenizer("your code here", return_tensors="pt", truncation=True, padding='max_length')
labels = torch.tensor([1]).unsqueeze(0) # Batch size 1
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
print(np.argmax(logits.detach().numpy()))
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
mrm8488/RuPERTa-base-finetuned-squadv2
|
mrm8488
| 2021-05-20T18:14:42Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"es",
"dataset:squad_v2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- squad_v2
---
|
mrm8488/RuPERTa-base-finetuned-squadv1
|
mrm8488
| 2021-05-20T18:13:28Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"es",
"dataset:squad",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: es
datasets:
- squad
---
|
mrm8488/RoBasquERTa
|
mrm8488
| 2021-05-20T18:05:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: eu
widget:
- text: "Euskara da Euskal Herriko <mask> ofiziala"
- text: "Gaur egun, Euskadik Espainia osoko ekonomia <mask> du"
---
# RoBasquERTa: RoBERTa-like Language model trained on OSCAR Basque corpus
|
julien-c/dummy-unknown
|
julien-c
| 2021-05-20T17:31:14Z | 61,031 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"ci",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- ci
---
## Dummy model used for unit testing and CI
```python
import json
import os
from transformers import RobertaConfig, RobertaForMaskedLM, TFRobertaForMaskedLM
DIRNAME = "./dummy-unknown"
config = RobertaConfig(10, 20, 1, 1, 40)
model = RobertaForMaskedLM(config)
model.save_pretrained(DIRNAME)
tf_model = TFRobertaForMaskedLM.from_pretrained(DIRNAME, from_pt=True)
tf_model.save_pretrained(DIRNAME)
# Tokenizer:
vocab = [
"l",
"o",
"w",
"e",
"r",
"s",
"t",
"i",
"d",
"n",
"\u0120",
"\u0120l",
"\u0120n",
"\u0120lo",
"\u0120low",
"er",
"\u0120lowest",
"\u0120newer",
"\u0120wider",
"<unk>",
]
vocab_tokens = dict(zip(vocab, range(len(vocab))))
merges = ["#version: 0.2", "\u0120 l", "\u0120l o", "\u0120lo w", "e r", ""]
vocab_file = os.path.join(DIRNAME, "vocab.json")
merges_file = os.path.join(DIRNAME, "merges.txt")
with open(vocab_file, "w", encoding="utf-8") as fp:
fp.write(json.dumps(vocab_tokens) + "\n")
with open(merges_file, "w", encoding="utf-8") as fp:
fp.write("\n".join(merges))
```
|
jpcorb20/toxic-detector-distilroberta
|
jpcorb20
| 2021-05-20T17:25:58Z | 88 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# Distilroberta for toxic comment detection
See my GitHub repo [toxic-comment-server](https://github.com/jpcorb20/toxic-comment-server)
The model was trained from [DistilRoberta](https://huggingface.co/distilroberta-base) on [Kaggle Toxic Comments](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) with the BCEWithLogits loss for Multi-Label prediction. Thus, please use the sigmoid activation on the logits (not made to use the softmax output, e.g. like the HF widget).
## Evaluation
F1 scores:
toxic: 0.72
severe_toxic: 0.38
obscene: 0.72
threat: 0.52
insult: 0.69
identity_hate: 0.60
Macro-F1: 0.61
|
idjotherwise/autonlp-reading_prediction-172506
|
idjotherwise
| 2021-05-20T16:57:07Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"autonlp",
"en",
"dataset:idjotherwise/autonlp-data-reading_prediction",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- idjotherwise/autonlp-data-reading_prediction
---
# Model Trained Using AutoNLP
- Problem type: Single Column Regression
- Model ID: 172506
## Validation Metrics
- Loss: 0.03257797285914421
- MSE: 0.03257797285914421
- MAE: 0.14246532320976257
- R2: 0.9693824457290849
- RMSE: 0.18049369752407074
- Explained Variance: 0.9699198007583618
## 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/idjotherwise/autonlp-reading_prediction-172506
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("idjotherwise/autonlp-reading_prediction-172506")
tokenizer = AutoTokenizer.from_pretrained("idjotherwise/autonlp-reading_prediction-172506")
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
iarfmoose/roberta-small-bulgarian
|
iarfmoose
| 2021-05-20T16:54:01Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: bg
---
# RoBERTa-small-bulgarian
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This is a smaller version of [RoBERTa-base-bulgarian](https://huggingface.co/iarfmoose/roberta-small-bulgarian) with only 6 hidden layers, but similar performance.
## Intended uses
This model can be used for cloze tasks (masked language modeling) or finetuned on other tasks in Bulgarian.
## Limitations and bias
The training data is unfiltered text from the internet and may contain all sorts of biases.
## Training data
This model was trained on the following data:
- [bg_dedup from OSCAR](https://oscar-corpus.com/)
- [Newscrawl 1 million sentences 2017 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
- [Wikipedia 1 million sentences 2016 from Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download/bulgarian)
## Training procedure
The model was pretrained using a masked language-modeling objective with dynamic masking as described [here](https://huggingface.co/roberta-base#preprocessing)
It was trained for 160k steps. The batch size was limited to 8 due to GPU memory limitations.
|
iarfmoose/roberta-base-bulgarian-pos
|
iarfmoose
| 2021-05-20T16:49:07Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"bg",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
language: bg
---
# RoBERTa-base-bulgarian-POS
The RoBERTa model was originally introduced in [this paper](https://arxiv.org/abs/1907.11692). This model is a version of [RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian) fine-tuned for part-of-speech tagging.
## Intended uses
The model can be used to predict part-of-speech tags in Bulgarian text. Since the tokenizer uses byte-pair encoding, each word in the text may be split into more than one token. When predicting POS-tags, the last token from each word can be used. Using the last token was found to slightly outperform predictions based on the first token.
An example of this can be found [here](https://github.com/iarfmoose/bulgarian-nlp/blob/master/models/postagger.py).
## Limitations and bias
The pretraining data is unfiltered text from the internet and may contain all sorts of biases.
## Training data
In addition to the pretraining data used in [RoBERTa-base-Bulgarian]([RoBERTa-base-Bulgarian](https://huggingface.co/iarfmoose/roberta-base-bulgarian)), the model was trained on the UPOS tags from [UD_Bulgarian-BTB](https://github.com/UniversalDependencies/UD_Bulgarian-BTB).
## Training procedure
The model was trained for 5 epochs over the training set. The loss was calculated based on label predictions for the last POS-tag for each word. The model achieves 97% on the test set.
|
hashk1/EsperBERTo-malgranda
|
hashk1
| 2021-05-20T16:38:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"eo",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: eo
thumbnail: https://huggingface.co/blog/assets/01_how-to-train/EsperBERTo-thumbnail-v2.png
widget:
- text: "Ĉu vi paloras la <mask> Esperanto?"
---
## EsperBERTo: RoBERTa-like Language model trained on Esperanto
|
giganticode/StackOBERTflow-comments-small-v1
|
giganticode
| 2021-05-20T16:33:56Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# StackOBERTflow-comments-small
StackOBERTflow is a RoBERTa model trained on StackOverflow comments.
A Byte-level BPE tokenizer with dropout was used (using the `tokenizers` package).
The model is *small*, i.e. has only 6-layers and the maximum sequence length was restricted to 256 tokens.
The model was trained for 6 epochs on several GBs of comments from the StackOverflow corpus.
## Quick start: masked language modeling prediction
```python
from transformers import pipeline
from pprint import pprint
COMMENT = "You really should not do it this way, I would use <mask> instead."
fill_mask = pipeline(
"fill-mask",
model="giganticode/StackOBERTflow-comments-small-v1",
tokenizer="giganticode/StackOBERTflow-comments-small-v1"
)
pprint(fill_mask(COMMENT))
# [{'score': 0.019997311756014824,
# 'sequence': '<s> You really should not do it this way, I would use jQuery instead.</s>',
# 'token': 1738},
# {'score': 0.01693696901202202,
# 'sequence': '<s> You really should not do it this way, I would use arrays instead.</s>',
# 'token': 2844},
# {'score': 0.013411642983555794,
# 'sequence': '<s> You really should not do it this way, I would use CSS instead.</s>',
# 'token': 2254},
# {'score': 0.013224546797573566,
# 'sequence': '<s> You really should not do it this way, I would use it instead.</s>',
# 'token': 300},
# {'score': 0.011984303593635559,
# 'sequence': '<s> You really should not do it this way, I would use classes instead.</s>',
# 'token': 1779}]
```
|
dbernsohn/roberta-php
|
dbernsohn
| 2021-05-20T15:56:10Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# roberta-php
---
language: php
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **php** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-php")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/roberta-php")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
```
You can then use this model to fill masked words in a Java code.
```python
code = """
$people = array(
array('name' => 'Kalle', 'salt' => 856412),
array('name' => 'Pierre', 'salt' => 215863)
);
for($i = 0; $i < count($<mask>); ++$i) {
$people[$i]['salt'] = mt_rand(000000, 999999);
}
""".lstrip()
pred = {x["token_str"].replace("Ġ", ""): x["score"] for x in fill_mask(code)}
sorted(pred.items(), key=lambda kv: kv[1], reverse=True)
# [('people', 0.785636842250824),
# ('parts', 0.006270722020417452),
# ('id', 0.0035842324141412973),
# ('data', 0.0025512021966278553),
# ('config', 0.002258970635011792)]
```
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/CodeMLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
dbernsohn/roberta-go
|
dbernsohn
| 2021-05-20T15:53:19Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
# roberta-go
---
language: Go
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **Golang** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-go")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/roberta-go")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
```
You can then use this model to fill masked words in a Java code.
```python
code = """
package main
import (
"fmt"
"runtime"
)
func main() {
fmt.Print("Go runs on ")
switch os := runtime.<mask>; os {
case "darwin":
fmt.Println("OS X.")
case "linux":
fmt.Println("Linux.")
default:
// freebsd, openbsd,
// plan9, windows...
fmt.Printf("%s.\n", os)
}
}
""".lstrip()
pred = {x["token_str"].replace("Ġ", ""): x["score"] for x in fill_mask(code)}
sorted(pred.items(), key=lambda kv: kv[1], reverse=True)
[('GOOS', 0.11810332536697388),
('FileInfo', 0.04276798665523529),
('Stdout', 0.03572738170623779),
('Getenv', 0.025064032524824142),
('FileMode', 0.01462600938975811)]
```
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/CodeMLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/)
|
clue/roberta_chinese_base
|
clue
| 2021-05-20T15:23:58Z | 317 | 7 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"zh",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: zh
---
## roberta_chinese_base
### Overview
**Language model:** roberta-base
**Model size:** 392M
**Language:** Chinese
**Training data:** [CLUECorpusSmall](https://github.com/CLUEbenchmark/CLUECorpus2020)
**Eval data:** [CLUE dataset](https://github.com/CLUEbenchmark/CLUE)
### Results
For results on downstream tasks like text classification, please refer to [this repository](https://github.com/CLUEbenchmark/CLUE).
### Usage
**NOTE:** You have to call **BertTokenizer** instead of RobertaTokenizer !!!
```
import torch
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained("clue/roberta_chinese_base")
roberta = BertModel.from_pretrained("clue/roberta_chinese_base")
```
### About CLUE benchmark
Organization of Language Understanding Evaluation benchmark for Chinese: tasks & datasets, baselines, pre-trained Chinese models, corpus and leaderboard.
Github: https://github.com/CLUEbenchmark
Website: https://www.cluebenchmarks.com/
|
castorini/ance-msmarco-doc-maxp
|
castorini
| 2021-05-20T15:17:50Z | 11 | 2 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
|
castorini/ance-msmarco-doc-firstp
|
castorini
| 2021-05-20T15:17:20Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"arxiv:2007.00808",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
This model is converted from the original ANCE [repo](https://github.com/microsoft/ANCE) and fitted into Pyserini:
> Lee Xiong, Chenyan Xiong, Ye Li, Kwok-Fung Tang, Jialin Liu, Paul Bennett, Junaid Ahmed, Arnold Overwijk. [Approximate Nearest Neighbor Negative Contrastive Learning for Dense Text Retrieval](https://arxiv.org/pdf/2007.00808.pdf)
For more details on how to use it, check our experiments in [Pyserini](https://github.com/castorini/pyserini/blob/master/docs/experiments-ance.md)
|
aravind-812/roberta-train-json
|
aravind-812
| 2021-05-20T14:12:53Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
datasets:
- squad
widget:
- text: "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."
- text: "How many square kilometers of rainforest is covered in the basin?"
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."
|
pchanda/pretrained-smiles-pubchem10m
|
pchanda
| 2021-05-20T13:01:15Z | 729 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
model pretrained on 10m smiles from pubchem.
|
MoseliMotsoehli/zuBERTa
|
MoseliMotsoehli
| 2021-05-20T12:14:07Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"fill-mask",
"zu",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:04Z |
---
language: zu
---
# zuBERTa
zuBERTa is a RoBERTa style transformer language model trained on zulu text.
## Intended uses & limitations
The model can be used for getting embeddings to use on a down-stream task such as question answering.
#### How to use
```python
>>> from transformers import pipeline
>>> from transformers import AutoTokenizer, AutoModelWithLMHead
>>> tokenizer = AutoTokenizer.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> model = AutoModelWithLMHead.from_pretrained("MoseliMotsoehli/zuBERTa")
>>> unmasker = pipeline('fill-mask', model=model, tokenizer=tokenizer)
>>> unmasker("Abafika eNkandla bafika sebeholwa <mask> uMpongo kaZingelwayo.")
[
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa khona uMpongo kaZingelwayo.</s>",
"score": 0.050459690392017365,
"token": 555,
"token_str": "Ġkhona"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa inkosi uMpongo kaZingelwayo.</s>",
"score": 0.03668094798922539,
"token": 2321,
"token_str": "Ġinkosi"
},
{
"sequence": "<s>Abafika eNkandla bafika sebeholwa ubukhosi uMpongo kaZingelwayo.</s>",
"score": 0.028774697333574295,
"token": 5101,
"token_str": "Ġubukhosi"
}
]
```
## Training data
1. 30k sentences of text, came from the [Leipzig Corpora Collection](https://wortschatz.uni-leipzig.de/en/download) of zulu 2018. These were collected from news articles and creative writtings.
2. ~7500 articles of human generated translations were scraped from the zulu [wikipedia](https://zu.wikipedia.org/wiki/Special:AllPages).
### BibTeX entry and citation info
```bibtex
@inproceedings{author = {Moseli Motsoehli},
title = {Towards transformation of Southern African language models through transformers.},
year={2020}
}
```
|
MoritzLaurer/covid-policy-roberta-21
|
MoritzLaurer
| 2021-05-20T12:11:07Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"roberta",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:04Z |
---
language:
- en
tags:
- text-classification
metrics:
- accuracy (balanced)
- F1 (weighted)
widget:
- text: "All non-essential work activity will stop in Spain from tomorrow until 9 April but there is some confusion as to which jobs can continue under the new lockdown restrictions"
---
# Covid-Policy-RoBERTa-21
This model is currently in development at the Centre for European Policy Studies (CEPS).
The model is not yet recommended for use. A more detailed description will follow.
If you are interested in using deep learning to identify 20 different types policy measures against COVID-19 in text (NPIs, "non-pharmaceutical interventions") don't hesitate to [contact me](https://www.ceps.eu/ceps-staff/moritz-laurer/).
|
HooshvareLab/roberta-fa-zwnj-base-ner
|
HooshvareLab
| 2021-05-20T11:55:34Z | 113 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"roberta",
"token-classification",
"fa",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:04Z |
---
language: fa
---
# RobertaNER
This model fine-tuned for the Named Entity Recognition (NER) task on a mixed NER dataset collected from [ARMAN](https://github.com/HaniehP/PersianNER), [PEYMA](http://nsurl.org/2019-2/tasks/task-7-named-entity-recognition-ner-for-farsi/), and [WikiANN](https://elisa-ie.github.io/wikiann/) that covered ten types of entities:
- Date (DAT)
- Event (EVE)
- Facility (FAC)
- Location (LOC)
- Money (MON)
- Organization (ORG)
- Percent (PCT)
- Person (PER)
- Product (PRO)
- Time (TIM)
## Dataset Information
| | Records | B-DAT | B-EVE | B-FAC | B-LOC | B-MON | B-ORG | B-PCT | B-PER | B-PRO | B-TIM | I-DAT | I-EVE | I-FAC | I-LOC | I-MON | I-ORG | I-PCT | I-PER | I-PRO | I-TIM |
|:------|----------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|--------:|
| Train | 29133 | 1423 | 1487 | 1400 | 13919 | 417 | 15926 | 355 | 12347 | 1855 | 150 | 1947 | 5018 | 2421 | 4118 | 1059 | 19579 | 573 | 7699 | 1914 | 332 |
| Valid | 5142 | 267 | 253 | 250 | 2362 | 100 | 2651 | 64 | 2173 | 317 | 19 | 373 | 799 | 387 | 717 | 270 | 3260 | 101 | 1382 | 303 | 35 |
| Test | 6049 | 407 | 256 | 248 | 2886 | 98 | 3216 | 94 | 2646 | 318 | 43 | 568 | 888 | 408 | 858 | 263 | 3967 | 141 | 1707 | 296 | 78 |
## Evaluation
The following tables summarize the scores obtained by model overall and per each class.
**Overall**
| Model | accuracy | precision | recall | f1 |
|:----------:|:--------:|:---------:|:--------:|:--------:|
| Roberta | 0.994849 | 0.949816 | 0.960235 | 0.954997 |
**Per entities**
| | number | precision | recall | f1 |
|:---: |:------: |:---------: |:--------: |:--------: |
| DAT | 407 | 0.844869 | 0.869779 | 0.857143 |
| EVE | 256 | 0.948148 | 1.000000 | 0.973384 |
| FAC | 248 | 0.957529 | 1.000000 | 0.978304 |
| LOC | 2884 | 0.965422 | 0.968100 | 0.966759 |
| MON | 98 | 0.937500 | 0.918367 | 0.927835 |
| ORG | 3216 | 0.943662 | 0.958333 | 0.950941 |
| PCT | 94 | 1.000000 | 0.968085 | 0.983784 |
| PER | 2646 | 0.957030 | 0.959562 | 0.958294 |
| PRO | 318 | 0.963636 | 1.000000 | 0.981481 |
| TIM | 43 | 0.739130 | 0.790698 | 0.764045 |
## How To Use
You use this model with Transformers pipeline for NER.
### Installing requirements
```bash
pip install transformers
```
### How to predict using pipeline
```python
from transformers import AutoTokenizer
from transformers import AutoModelForTokenClassification # for pytorch
from transformers import TFAutoModelForTokenClassification # for tensorflow
from transformers import pipeline
model_name_or_path = "HooshvareLab/roberta-fa-zwnj-base-ner"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForTokenClassification.from_pretrained(model_name_or_path) # Pytorch
# model = TFAutoModelForTokenClassification.from_pretrained(model_name_or_path) # Tensorflow
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "در سال ۲۰۱۳ درگذشت و آندرتیکر و کین برای او مراسم یادبود گرفتند."
ner_results = nlp(example)
print(ner_results)
```
## Questions?
Post a Github issue on the [ParsNER Issues](https://github.com/hooshvare/parsner/issues) repo.
|
zanelim/singbert
|
zanelim
| 2021-05-20T09:38:41Z | 6 | 4 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-base-uncased",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-base-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---
# Model name
SingBert - Bert for Singlish (SG) and Manglish (MY).
## Model description
[BERT base uncased](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## Intended uses & limitations
#### How to use
```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert')
>>> nlp("kopi c siew [MASK]")
[{'sequence': '[CLS] kopi c siew dai [SEP]',
'score': 0.5092713236808777,
'token': 18765,
'token_str': 'dai'},
{'sequence': '[CLS] kopi c siew mai [SEP]',
'score': 0.3515934646129608,
'token': 14736,
'token_str': 'mai'},
{'sequence': '[CLS] kopi c siew bao [SEP]',
'score': 0.05576375499367714,
'token': 25945,
'token_str': 'bao'},
{'sequence': '[CLS] kopi c siew. [SEP]',
'score': 0.006019321270287037,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] kopi c siew sai [SEP]',
'score': 0.0038361591286957264,
'token': 18952,
'token_str': 'sai'}]
>>> nlp("one teh c siew dai, and one kopi [MASK].")
[{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
'score': 0.6176503300666809,
'token': 1039,
'token_str': 'c'},
{'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
'score': 0.21094971895217896,
'token': 1051,
'token_str': 'o'},
{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
'score': 0.13027705252170563,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] one teh c siew dai, and one kopi! [SEP]',
'score': 0.004680239595472813,
'token': 999,
'token_str': '!'},
{'sequence': '[CLS] one teh c siew dai, and one kopi w [SEP]',
'score': 0.002034128177911043,
'token': 1059,
'token_str': 'w'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh [SEP]',
'score': 0.9281464219093323,
'token': 2377,
'token_str': 'play'},
{'sequence': '[CLS] dont play politics leh [SEP]',
'score': 0.010990909300744534,
'token': 4331,
'token_str': 'politics'},
{'sequence': '[CLS] dont play punk leh [SEP]',
'score': 0.005583590362221003,
'token': 7196,
'token_str': 'punk'},
{'sequence': '[CLS] dont play dirty leh [SEP]',
'score': 0.0025784350000321865,
'token': 6530,
'token_str': 'dirty'},
{'sequence': '[CLS] dont play cheat leh [SEP]',
'score': 0.0025066907983273268,
'token': 21910,
'token_str': 'cheat'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball [SEP]',
'score': 0.7922210693359375,
'token': 3608,
'token_str': 'ball'},
{'sequence': '[CLS] catch no balls [SEP]',
'score': 0.20503675937652588,
'token': 7395,
'token_str': 'balls'},
{'sequence': '[CLS] catch no tail [SEP]',
'score': 0.0006608376861549914,
'token': 5725,
'token_str': 'tail'},
{'sequence': '[CLS] catch no talent [SEP]',
'score': 0.0002158183924620971,
'token': 5848,
'token_str': 'talent'},
{'sequence': '[CLS] catch no prisoners [SEP]',
'score': 5.3481446229852736e-05,
'token': 5895,
'token_str': 'prisoners'}]
>>> nlp("confirm plus [MASK]")
[{'sequence': '[CLS] confirm plus chop [SEP]',
'score': 0.992355227470398,
'token': 24494,
'token_str': 'chop'},
{'sequence': '[CLS] confirm plus one [SEP]',
'score': 0.0037301010452210903,
'token': 2028,
'token_str': 'one'},
{'sequence': '[CLS] confirm plus minus [SEP]',
'score': 0.0014284878270700574,
'token': 15718,
'token_str': 'minus'},
{'sequence': '[CLS] confirm plus 1 [SEP]',
'score': 0.0011354683665558696,
'token': 1015,
'token_str': '1'},
{'sequence': '[CLS] confirm plus chopped [SEP]',
'score': 0.0003804611915256828,
'token': 24881,
'token_str': 'chopped'}]
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try [SEP]',
'score': 0.9552758932113647,
'token': 3280,
'token_str': 'die'},
{'sequence': '[CLS] die also must try [SEP]',
'score': 0.03644804656505585,
'token': 2036,
'token_str': 'also'},
{'sequence': '[CLS] die liao must try [SEP]',
'score': 0.003282855963334441,
'token': 727,
'token_str': 'liao'},
{'sequence': '[CLS] die already must try [SEP]',
'score': 0.0004937972989864647,
'token': 2525,
'token_str': 'already'},
{'sequence': '[CLS] die hard must try [SEP]',
'score': 0.0003659659414552152,
'token': 2524,
'token_str': 'hard'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert')
model = BertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert")
model = TFBertModel.from_pretrained("zanelim/singbert")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
## Training procedure
Initialized with [bert base uncased](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.
Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8
|
ykacer/bert-base-cased-imdb-sequence-classification
|
ykacer
| 2021-05-20T09:31:37Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sequence",
"classification",
"en",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
thumbnail: https://raw.githubusercontent.com/JetRunner/BERT-of-Theseus/master/bert-of-theseus.png
tags:
- sequence
- classification
license: apache-2.0
datasets:
- imdb
metrics:
- accuracy
---
|
twmkn9/bert-base-uncased-squad2
|
twmkn9
| 2021-05-20T08:21:23Z | 245 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
This model is [BERT base uncased](https://huggingface.co/bert-base-uncased) trained on SQuAD v2 as:
```
export SQUAD_DIR=../../squad2
python3 run_squad.py
--model_type bert
--model_name_or_path bert-base-uncased
--do_train
--do_eval
--overwrite_cache
--do_lower_case
--version_2_with_negative
--save_steps 100000
--train_file $SQUAD_DIR/train-v2.0.json
--predict_file $SQUAD_DIR/dev-v2.0.json
--per_gpu_train_batch_size 8
--num_train_epochs 3
--learning_rate 3e-5
--max_seq_length 384
--doc_stride 128
--output_dir ./tmp/bert_fine_tuned/
```
Performance on a dev subset is close to the original paper:
```
Results:
{
'exact': 72.35932872655479,
'f1': 75.75355132564763,
'total': 6078,
'HasAns_exact': 74.29553264604812,
'HasAns_f1': 81.38490892002987,
'HasAns_total': 2910,
'NoAns_exact': 70.58080808080808,
'NoAns_f1': 70.58080808080808,
'NoAns_total': 3168,
'best_exact': 72.35932872655479,
'best_exact_thresh': 0.0,
'best_f1': 75.75355132564766,
'best_f1_thresh': 0.0
}
```
We are hopeful this might save you time, energy, and compute. Cheers!
|
trueto/medbert-kd-chinese
|
trueto
| 2021-05-20T08:10:57Z | 9 | 10 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CEMRNER | 965 | 138 | 276 | 命名实体识别 | 医渡云 |
| CMTNER | 14000 | 2000 | 4000 | 命名实体识别 | CHIP2020 |
| CMedQQ | 14000 | 2000 | 4000 | 句对识别 | 平安医疗 |
| CCTC | 26837 | 3834 | 7669 | 句子分类 | CHIP2019 |
## 开源模型
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
## 性能表现
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
| **模型** | **CEMRNER** | **CMTNER** | **CMedQQ** | **CCTC** |
| :---- | :----: | :----: | :----: | :----: |
| [BERT](https://huggingface.co/bert-base-chinese) | 81.17% | 65.67% | 87.77% | 81.62% |
| [MC-BERT](https://github.com/alibaba-research/ChineseBLUE) | 80.93% | 66.15% | 89.04% | 80.65% |
| [PCL-BERT](https://code.ihub.org.cn/projects/1775) | 81.58% | 67.02% | 88.81% | 80.27% |
| MedBERT | 82.29% | 66.49% | 88.32% | **81.77%** |
|MedBERT-wwm| **82.60%** | 67.11% | 88.02% | 81.72% |
|MedBERT-kd | 82.58% | **67.27%** | **89.34%** | 80.73% |
|- | - | - | - | - |
| [Albert](https://huggingface.co/voidful/albert_chinese_base) | 79.98% | 62.42% | 86.81% | 79.83% |
| MedAlbert | 81.03% | 63.81% | 87.56% | 80.05% |
|MedAlbert-wwm| **81.28%** | **64.12%** | **87.71%** | **80.46%** |
## 引用格式
```
杨飞洪,王序文,李姣.BERT模型在中文临床自然语言处理中的应用探索与研究[EB/OL].https://github.com/trueto/medbert, 2021-03.
```
|
trueto/medbert-base-wwm-chinese
|
trueto
| 2021-05-20T08:09:44Z | 8 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CEMRNER | 965 | 138 | 276 | 命名实体识别 | 医渡云 |
| CMTNER | 14000 | 2000 | 4000 | 命名实体识别 | CHIP2020 |
| CMedQQ | 14000 | 2000 | 4000 | 句对识别 | 平安医疗 |
| CCTC | 26837 | 3834 | 7669 | 句子分类 | CHIP2019 |
## 开源模型
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
## 性能表现
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
| **模型** | **CEMRNER** | **CMTNER** | **CMedQQ** | **CCTC** |
| :---- | :----: | :----: | :----: | :----: |
| [BERT](https://huggingface.co/bert-base-chinese) | 81.17% | 65.67% | 87.77% | 81.62% |
| [MC-BERT](https://github.com/alibaba-research/ChineseBLUE) | 80.93% | 66.15% | 89.04% | 80.65% |
| [PCL-BERT](https://code.ihub.org.cn/projects/1775) | 81.58% | 67.02% | 88.81% | 80.27% |
| MedBERT | 82.29% | 66.49% | 88.32% | **81.77%** |
|MedBERT-wwm| **82.60%** | 67.11% | 88.02% | 81.72% |
|MedBERT-kd | 82.58% | **67.27%** | **89.34%** | 80.73% |
|- | - | - | - | - |
| [Albert](https://huggingface.co/voidful/albert_chinese_base) | 79.98% | 62.42% | 86.81% | 79.83% |
| MedAlbert | 81.03% | 63.81% | 87.56% | 80.05% |
|MedAlbert-wwm| **81.28%** | **64.12%** | **87.71%** | **80.46%** |
## 引用格式
```
杨飞洪,王序文,李姣.BERT模型在中文临床自然语言处理中的应用探索与研究[EB/OL].https://github.com/trueto/medbert, 2021-03.
```
|
trueto/medbert-base-chinese
|
trueto
| 2021-05-20T08:08:47Z | 276 | 13 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"pretraining",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
# [medbert](https://github.com/trueto/medbert)
本项目开源硕士毕业论文“BERT模型在中文临床自然语言处理中的应用探索与研究”相关模型
## 评估基准
构建了中文电子病历命名实体识别数据集（CEMRNER）、中文医学文本命名实体识别数据集（CMTNER）、
中文医学问句-问句识别数据集（CMedQQ）和中文临床文本分类数据集（CCTC）。
| **数据集** | **训练集** | **验证集** | **测试集** | **任务类型** | **语料来源** |
| ---- | ---- | ---- |---- |---- |:----:|
| CEMRNER | 965 | 138 | 276 | 命名实体识别 | 医渡云 |
| CMTNER | 14000 | 2000 | 4000 | 命名实体识别 | CHIP2020 |
| CMedQQ | 14000 | 2000 | 4000 | 句对识别 | 平安医疗 |
| CCTC | 26837 | 3834 | 7669 | 句子分类 | CHIP2019 |
## 开源模型
在6.5亿字符中文临床自然语言文本语料上基于BERT模型和Albert模型预训练获得了MedBERT和MedAlbert模型。
## 性能表现
在同等实验环境，相同训练参数和脚本下，各模型的性能表现
| **模型** | **CEMRNER** | **CMTNER** | **CMedQQ** | **CCTC** |
| :---- | :----: | :----: | :----: | :----: |
| [BERT](https://huggingface.co/bert-base-chinese) | 81.17% | 65.67% | 87.77% | 81.62% |
| [MC-BERT](https://github.com/alibaba-research/ChineseBLUE) | 80.93% | 66.15% | 89.04% | 80.65% |
| [PCL-BERT](https://code.ihub.org.cn/projects/1775) | 81.58% | 67.02% | 88.81% | 80.27% |
| MedBERT | 82.29% | 66.49% | 88.32% | **81.77%** |
|MedBERT-wwm| **82.60%** | 67.11% | 88.02% | 81.72% |
|MedBERT-kd | 82.58% | **67.27%** | **89.34%** | 80.73% |
|- | - | - | - | - |
| [Albert](https://huggingface.co/voidful/albert_chinese_base) | 79.98% | 62.42% | 86.81% | 79.83% |
| MedAlbert | 81.03% | 63.81% | 87.56% | 80.05% |
|MedAlbert-wwm| **81.28%** | **64.12%** | **87.71%** | **80.46%** |
## 引用格式
```
杨飞洪,王序文,李姣.BERT模型在中文临床自然语言处理中的应用探索与研究[EB/OL].https://github.com/trueto/medbert, 2021-03.
```
|
trtd56/autonlp-wrime_joy_only-117396
|
trtd56
| 2021-05-20T08:07:48Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autonlp",
"ja",
"dataset:trtd56/autonlp-data-wrime_joy_only",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
tags: autonlp
language: ja
widget:
- text: "I love AutoNLP 🤗"
datasets:
- trtd56/autonlp-data-wrime_joy_only
---
# Model Trained Using AutoNLP
- Problem type: Binary Classification
- Model ID: 117396
## Validation Metrics
- Loss: 0.4094310998916626
- Accuracy: 0.8201678240740741
- Precision: 0.6750303520841765
- Recall: 0.7912713472485768
- AUC: 0.8927167943538512
- F1: 0.728543350076436
## 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/trtd56/autonlp-wrime_joy_only-117396
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("trtd56/autonlp-wrime_joy_only-117396", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("trtd56/autonlp-wrime_joy_only-117396", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
```
|
trituenhantaoio/bert-base-vietnamese-diacritics-uncased
|
trituenhantaoio
| 2021-05-20T08:05:47Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
## Usage
```python
from transformers import BertForSequenceClassification
from transformers import BertTokenizer
model = BertForSequenceClassification.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-uncased")
tokenizer = BertTokenizer.from_pretrained("trituenhantaoio/bert-base-vietnamese-diacritics-uncased")
```
### References
```
@article{ttnt2020bertdiacritics,
title={Vietnamese BERT Diacritics: Pretrained on News and Wiki},
author={trituenhantao.io},
year = {2020},
publisher = {Hugging Face},
journal = {Hugging Face repository}
}
```
[trituenhantao.io](https://trituenhantao.io)
|
textattack/bert-base-uncased-yelp-polarity
|
textattack
| 2021-05-20T07:49:07Z | 16,043 | 4 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the yelp_polarity dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9699473684210527, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-uncased-imdb
|
textattack
| 2021-05-20T07:42:02Z | 17,464 | 6 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the imdb dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.89088, as measured by the
eval set accuracy, found after 4 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-uncased-ag-news
|
textattack
| 2021-05-20T07:40:21Z | 2,911 | 4 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model CardThis `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the ag_news dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 3e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.9514473684210526, as measured by the
eval set accuracy, found after 3 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-uncased-WNLI
|
textattack
| 2021-05-20T07:39:22Z | 44 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 64, a learning
rate of 5e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.5633802816901409, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-uncased-RTE
|
textattack
| 2021-05-20T07:36:18Z | 81 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 8, a learning
rate of 2e-05, and a maximum sequence length of 128.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.7256317689530686, as measured by the
eval set accuracy, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-uncased-MRPC
|
textattack
| 2021-05-20T07:32:52Z | 199 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-uncased` model was fine-tuned for sequence classification using TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 5 epochs with a batch size of 16, a learning
rate of 2e-05, and a maximum sequence length of 256.
Since this was a classification task, the model was trained with a cross-entropy loss function.
The best score the model achieved on this task was 0.8774509803921569, as measured by the
eval set accuracy, found after 1 epoch.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
textattack/bert-base-cased-STS-B
|
textattack
| 2021-05-20T07:30:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
## TextAttack Model Card
This `bert-base-cased` model was fine-tuned for sequence classificationusing TextAttack
and the glue dataset loaded using the `nlp` library. The model was fine-tuned
for 3 epochs with a batch size of 128, a learning
rate of 1e-05, and a maximum sequence length of 128.
Since this was a regression task, the model was trained with a mean squared error loss function.
The best score the model achieved on this task was 0.8244429996636282, as measured by the
eval set pearson correlation, found after 2 epochs.
For more information, check out [TextAttack on Github](https://github.com/QData/TextAttack).
|
tennessejoyce/titlewave-bert-base-uncased
|
tennessejoyce
| 2021-05-20T07:29:09Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: en
license: cc-by-4.0
widget:
- text: "[Gmail API] How can I extract plain text from an email sent to me?"
---
# Titlewave: bert-base-uncased
## Model description
Titlewave is a Chrome extension that helps you choose better titles for your Stack Overflow questions. See the [github repository](https://github.com/tennessejoyce/TitleWave) for more information.
This is one of two NLP models used in the Titlewave project, and its purpose is to classify whether question will be answered or not just based on the title. The [companion model](https://huggingface.co/tennessejoyce/titlewave-t5-small) suggests a new title based on on the body of the question.
## Intended use
Try out different titles for your Stack Overflow post, and see which one gives you the best chance of receiving an answer.
You can use the model through the API on this page (hosted by HuggingFace) or install the Chrome extension by following the instructions on the [github repository](https://github.com/tennessejoyce/TitleWave), which integrates the tool directly into the Stack Overflow website.
You can also run the model locally in Python like this (which automatically downloads the model to your machine):
```python
>>> from transformers import pipeline
>>> classifier = pipeline('sentiment-analysis', model='tennessejoyce/titlewave-bert-base-uncased')
>>> classifier('[Gmail API] How can I extract plain text from an email sent to me?')
[{'label': 'Answered', 'score': 0.8053370714187622}]
```
The 'score' in the output represents the probability of getting an answer with this title: 80.5%.
## Training data
The weights were initialized from the [BERT base model](https://huggingface.co/bert-base-uncased), which was trained on BookCorpus and English Wikipedia.
Then the model was fine-tuned on the dataset of previous Stack Overflow post titles, which is publicly available [here](https://archive.org/details/stackexchange).
Specifically I used three years of posts from 2017-2019, filtered out posts which were closed (e.g., duplicates, off-topic), and selected 5% of the remaining posts at random to use in the training set, and the same amount for validation and test sets (278,155 posts each).
## Training procedure
The model was fine-tuned for two epochs with a batch size of 32 (17,384 steps total) using 16-bit mixed precision.
After some hyperparameter tuning, I found that the following two-phase training procedure yields the best performance (ROC-AUC score) on the validation set:
* In the first epoch, all layers were frozen except for the last two (pooling layer and classification layer) and a learning rate of 3e-4 was used.
* In the second epoch all layers were unfrozen, and the learning rate was decreased by a factor of 10 to 3e-5.
Otherwise, all parameters we set to the defaults listed [here](https://huggingface.co/transformers/main_classes/trainer.html#transformers.TrainingArguments),
including the AdamW optimizer and a linearly decreasing learning schedule (both of which were reset between the two epochs). See the [github repository](https://github.com/tennessejoyce/TitleWave) for the scripts that were used to train the model.
## Evaluation
See [this notebook](https://github.com/tennessejoyce/TitleWave/blob/master/model_training/test_classifier.ipynb) for the performance of the title classification model on the test set.
|
techthiyanes/Bert_Bahasa_Sentiment
|
techthiyanes
| 2021-05-20T07:26:52Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained(model_checkpoint)
model = AutoModelForSequenceClassification.from_pretrained('techthiyanes/Bert_Bahasa_Sentiment')
inputs = tokenizer("saya tidak", return_tensors="pt")
labels = torch.tensor([1]).unsqueeze(0)
outputs = model(**inputs, labels=labels)
loss = outputs.loss
logits = outputs.logits
outputs
hello
|
socialmediaie/TRAC2020_HIN_C_bert-base-multilingual-uncased
|
socialmediaie
| 2021-05-20T07:01:31Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: https://databank.illinois.edu/datasets/IDB-8882752#
We also make a few of our models available in HuggingFace's models repository at https://huggingface.co/socialmediaie/, these models can be further fine-tuned on your dataset of choice.
Our approach is described in our paper titled:
> Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020
NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.
If you plan to use the dataset please cite the following resources:
* Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
* Mishra, Shubhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Trained Models for Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8882752_V1.
```
@inproceedings{Mishra2020TRAC,
author = {Mishra, Sudhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020)},
title = {{Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
year = {2020}
}
@data{illinoisdatabankIDB-8882752,
author = {Mishra, Shubhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
doi = {10.13012/B2IDB-8882752_V1},
publisher = {University of Illinois at Urbana-Champaign},
title = {{Trained models for Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
url = {https://doi.org/10.13012/B2IDB-8882752{\_}V1},
year = {2020}
}
```
## Usage
The models can be used via the following code:
```python
from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
import torch
from pathlib import Path
from scipy.special import softmax
import numpy as np
import pandas as pd
TASK_LABEL_IDS = {
"Sub-task A": ["OAG", "NAG", "CAG"],
"Sub-task B": ["GEN", "NGEN"],
"Sub-task C": ["OAG-GEN", "OAG-NGEN", "NAG-GEN", "NAG-NGEN", "CAG-GEN", "CAG-NGEN"]
}
model_version="databank" # other option is hugging face library
if model_version == "databank":
# Make sure you have downloaded the required model file from https://databank.illinois.edu/datasets/IDB-8882752
# Unzip the file at some model_path (we are using: "databank_model")
model_path = next(Path("databank_model").glob("./*/output/*/model"))
# Assuming you get the following type of structure inside "databank_model"
# 'databank_model/ALL/Sub-task C/output/bert-base-multilingual-uncased/model'
lang, task, _, base_model, _ = model_path.parts
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
else:
lang, task, base_model = "ALL", "Sub-task C", "bert-base-multilingual-uncased"
base_model = f"socialmediaie/TRAC2020_{lang}_{lang.split()[-1]}_{base_model}"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(base_model)
# For doing inference set model in eval mode
model.eval()
# If you want to further fine-tune the model you can reset the model to model.train()
task_labels = TASK_LABEL_IDS[task]
sentence = "This is a good cat and this is a bad dog."
processed_sentence = f"{tokenizer.cls_token} {sentence}"
tokens = tokenizer.tokenize(sentence)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokens)
tokens_tensor = torch.tensor([indexed_tokens])
with torch.no_grad():
logits, = model(tokens_tensor, labels=None)
logits
preds = logits.detach().cpu().numpy()
preds_probs = softmax(preds, axis=1)
preds = np.argmax(preds_probs, axis=1)
preds_labels = np.array(task_labels)[preds]
print(dict(zip(task_labels, preds_probs[0])), preds_labels)
"""You should get an output as follows:
({'CAG-GEN': 0.06762535,
'CAG-NGEN': 0.03244293,
'NAG-GEN': 0.6897794,
'NAG-NGEN': 0.15498641,
'OAG-GEN': 0.034373745,
'OAG-NGEN': 0.020792078},
array(['NAG-GEN'], dtype='<U8'))
"""
```
|
socialmediaie/TRAC2020_ALL_B_bert-base-multilingual-uncased
|
socialmediaie
| 2021-05-20T06:53:23Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020
Models and predictions for submission to TRAC - 2020 Second Workshop on Trolling, Aggression and Cyberbullying.
Our trained models as well as evaluation metrics during traing are available at: https://databank.illinois.edu/datasets/IDB-8882752#
We also make a few of our models available in HuggingFace's models repository at https://huggingface.co/socialmediaie/, these models can be further fine-tuned on your dataset of choice.
Our approach is described in our paper titled:
> Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
The source code for training this model and more details can be found on our code repository: https://github.com/socialmediaie/TRAC2020
NOTE: These models are retrained for uploading here after our submission so the evaluation measures may be slightly different from the ones reported in the paper.
If you plan to use the dataset please cite the following resources:
* Mishra, Sudhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. "Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020." In Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020).
* Mishra, Shubhanshu, Shivangi Prasad, and Shubhanshu Mishra. 2020. “Trained Models for Multilingual Joint Fine-Tuning of Transformer Models for Identifying Trolling, Aggression and Cyberbullying at TRAC 2020.” University of Illinois at Urbana-Champaign. https://doi.org/10.13012/B2IDB-8882752_V1.
```
@inproceedings{Mishra2020TRAC,
author = {Mishra, Sudhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
booktitle = {Proceedings of the Second Workshop on Trolling, Aggression and Cyberbullying (TRAC-2020)},
title = {{Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
year = {2020}
}
@data{illinoisdatabankIDB-8882752,
author = {Mishra, Shubhanshu and Prasad, Shivangi and Mishra, Shubhanshu},
doi = {10.13012/B2IDB-8882752_V1},
publisher = {University of Illinois at Urbana-Champaign},
title = {{Trained models for Multilingual Joint Fine-tuning of Transformer models for identifying Trolling, Aggression and Cyberbullying at TRAC 2020}},
url = {https://doi.org/10.13012/B2IDB-8882752{\_}V1},
year = {2020}
}
```
## Usage
The models can be used via the following code:
```python
from transformers import AutoModel, AutoTokenizer, AutoModelForSequenceClassification
import torch
from pathlib import Path
from scipy.special import softmax
import numpy as np
import pandas as pd
TASK_LABEL_IDS = {
"Sub-task A": ["OAG", "NAG", "CAG"],
"Sub-task B": ["GEN", "NGEN"],
"Sub-task C": ["OAG-GEN", "OAG-NGEN", "NAG-GEN", "NAG-NGEN", "CAG-GEN", "CAG-NGEN"]
}
model_version="databank" # other option is hugging face library
if model_version == "databank":
# Make sure you have downloaded the required model file from https://databank.illinois.edu/datasets/IDB-8882752
# Unzip the file at some model_path (we are using: "databank_model")
model_path = next(Path("databank_model").glob("./*/output/*/model"))
# Assuming you get the following type of structure inside "databank_model"
# 'databank_model/ALL/Sub-task C/output/bert-base-multilingual-uncased/model'
lang, task, _, base_model, _ = model_path.parts
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(model_path)
else:
lang, task, base_model = "ALL", "Sub-task C", "bert-base-multilingual-uncased"
base_model = f"socialmediaie/TRAC2020_{lang}_{lang.split()[-1]}_{base_model}"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForSequenceClassification.from_pretrained(base_model)
# For doing inference set model in eval mode
model.eval()
# If you want to further fine-tune the model you can reset the model to model.train()
task_labels = TASK_LABEL_IDS[task]
sentence = "This is a good cat and this is a bad dog."
processed_sentence = f"{tokenizer.cls_token} {sentence}"
tokens = tokenizer.tokenize(sentence)
indexed_tokens = tokenizer.convert_tokens_to_ids(tokens)
tokens_tensor = torch.tensor([indexed_tokens])
with torch.no_grad():
logits, = model(tokens_tensor, labels=None)
logits
preds = logits.detach().cpu().numpy()
preds_probs = softmax(preds, axis=1)
preds = np.argmax(preds_probs, axis=1)
preds_labels = np.array(task_labels)[preds]
print(dict(zip(task_labels, preds_probs[0])), preds_labels)
"""You should get an output as follows:
({'CAG-GEN': 0.06762535,
'CAG-NGEN': 0.03244293,
'NAG-GEN': 0.6897794,
'NAG-NGEN': 0.15498641,
'OAG-GEN': 0.034373745,
'OAG-NGEN': 0.020792078},
array(['NAG-GEN'], dtype='<U8'))
"""
```
|
smeylan/childes-bert
|
smeylan
| 2021-05-20T06:49:00Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"language-modeling",
"en",
"dataset:childes",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- language-modeling
license: "cc-by-sa-4.0"
datasets:
- childes
---
|
sismetanin/sbert-ru-sentiment-rusentiment
|
sismetanin
| 2021-05-20T06:38:36Z | 367 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## SBERT-Large-Base-ru-sentiment-RuSentiment
SBERT-Large-ru-sentiment-RuSentiment is a [SBERT-Large](https://huggingface.co/sberbank-ai/sbert_large_nlu_ru) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@inproceedings{rogers2018rusentiment,
title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
booktitle={Proceedings of the 27th international conference on computational linguistics},
pages={755--763},
year={2018}
}
```
|
sismetanin/sbert-ru-sentiment-krnd
|
sismetanin
| 2021-05-20T06:27:51Z | 16 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"SBERT-Large",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
- SBERT-Large
---
## SBERT-Large on Kaggle Russian News Dataset
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F<sub>1</sub></td>
<td>macro F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>micro F<sub>1</sub></td>
<td>macro F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>wighted F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>F<sub>1</sub></td>
<td>F<sub>1</sub></td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
|
sismetanin/rubert_conversational-ru-sentiment-rusentiment
|
sismetanin
| 2021-05-20T06:22:35Z | 23 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## RuBERT-Conversational-ru-sentiment-RuSentiment
RuBERT-Conversational-ru-sentiment-RuSentiment is a [RuBERT-Conversational](https://huggingface.co/DeepPavlov/rubert-base-cased-conversational) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@inproceedings{rogers2018rusentiment,
title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
booktitle={Proceedings of the 27th international conference on computational linguistics},
pages={755--763},
year={2018}
}
```
|
sismetanin/rubert-toxic-pikabu-2ch
|
sismetanin
| 2021-05-20T06:16:03Z | 305 | 8 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"toxic comments classification",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- toxic comments classification
---
## RuBERT-Toxic
RuBERT-Toxic is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments). You can find a detailed description of the data used and the fine-tuning process in [this article](http://doi.org/10.28995/2075-7182-2020-19-1149-1159). You can also find this information at [GitHub](https://github.com/sismetanin/toxic-comments-detection-in-russian).
| System | P | R | F<sub>1</sub> |
| ------------- | ------------- | ------------- | ------------- |
| MNB-Toxic | 87.01% | 81.22% | 83.21% |
| M-BERT<sub>Base</sub>-Toxic | 91.19% | 91.10% | 91.15% |
| <b>RuBERT-Toxic</b> | <b>91.91%</b> | <b>92.51%</b> | <b>92.20%</b> |
| M-USE<sub>CNN</sub>-Toxic | 89.69% | 90.14% | 89.91% |
| M-USE<sub>Trans</sub>-Toxic | 90.85% | 91.92% | 91.35% |
We fine-tuned two versions of Multilingual Universal Sentence Encoder (M-USE), Multilingual Bidirectional Encoder Representations from Transformers (M-BERT) and RuBERT for toxic comments detection in Russian. Fine-tuned RuBERT-Toxic achieved F<sub>1</sub> = 92.20%, demonstrating the best classification score.
## Toxic Comments Dataset
[Kaggle Russian Language Toxic Comments Dataset](https://www.kaggle.com/blackmoon/russian-language-toxic-comments) is the collection of Russian-language annotated comments from [2ch](https://2ch.hk/) and [Pikabu](https://pikabu.ru/), which was published on Kaggle in 2019. It consists of 14412 comments, where 4826 texts were labelled as toxic, and 9586 were labelled as non-toxic. The average length of comments is ~175 characters; the minimum length is 21, and the maximum is 7403.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@INPROCEEDINGS{Smetanin2020Toxic,
author={Sergey Smetanin},
booktitle={Computational Linguistics and Intellectual Technologies: Proceedings of the International Conference “Dialogue 2020”},
title={Toxic Comments Detection in Russian},
year={2020},
doi={10.28995/2075-7182-2020-19-1149-1159}
}
```
|
sismetanin/rubert-ru-sentiment-rusentiment
|
sismetanin
| 2021-05-20T06:11:34Z | 416 | 6 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"sentiment analysis",
"Russian",
"ru",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- ru
tags:
- sentiment analysis
- Russian
---
## RuBERT-Base-ru-sentiment-RuSentiment
RuBERT-ru-sentiment-RuSentiment is a [RuBERT](https://huggingface.co/DeepPavlov/rubert-base-cased) model fine-tuned on [RuSentiment dataset](https://github.com/text-machine-lab/rusentiment) of general-domain Russian-language posts from the largest Russian social network, VKontakte.
<table>
<thead>
<tr>
<th rowspan="4">Model</th>
<th rowspan="4">Score<br></th>
<th rowspan="4">Rank</th>
<th colspan="12">Dataset</th>
</tr>
<tr>
<td colspan="6">SentiRuEval-2016<br></td>
<td colspan="2" rowspan="2">RuSentiment</td>
<td rowspan="2">KRND</td>
<td rowspan="2">LINIS Crowd</td>
<td rowspan="2">RuTweetCorp</td>
<td rowspan="2">RuReviews</td>
</tr>
<tr>
<td colspan="3">TC</td>
<td colspan="3">Banks</td>
</tr>
<tr>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>micro F1</td>
<td>macro F1</td>
<td>F1</td>
<td>wighted</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
<td>F1</td>
</tr>
</thead>
<tbody>
<tr>
<td>SOTA</td>
<td>n/s</td>
<td></td>
<td>76.71</td>
<td>66.40</td>
<td>70.68</td>
<td>67.51</td>
<td>69.53</td>
<td>74.06</td>
<td>78.50</td>
<td>n/s</td>
<td>73.63</td>
<td>60.51</td>
<td>83.68</td>
<td>77.44</td>
</tr>
<tr>
<td>XLM-RoBERTa-Large</td>
<td>76.37</td>
<td>1</td>
<td>82.26</td>
<td>76.36</td>
<td>79.42</td>
<td>76.35</td>
<td>76.08</td>
<td>80.89</td>
<td>78.31</td>
<td>75.27</td>
<td>75.17</td>
<td>60.03</td>
<td>88.91</td>
<td>78.81</td>
</tr>
<tr>
<td>SBERT-Large</td>
<td>75.43</td>
<td>2</td>
<td>78.40</td>
<td>71.36</td>
<td>75.14</td>
<td>72.39</td>
<td>71.87</td>
<td>77.72</td>
<td>78.58</td>
<td>75.85</td>
<td>74.20</td>
<td>60.64</td>
<td>88.66</td>
<td>77.41</td>
</tr>
<tr>
<td>MBARTRuSumGazeta</td>
<td>74.70</td>
<td>3</td>
<td>76.06</td>
<td>68.95</td>
<td>73.04</td>
<td>72.34</td>
<td>71.93</td>
<td>77.83</td>
<td>76.71</td>
<td>73.56</td>
<td>74.18</td>
<td>60.54</td>
<td>87.22</td>
<td>77.51</td>
</tr>
<tr>
<td>Conversational RuBERT</td>
<td>74.44</td>
<td>4</td>
<td>76.69</td>
<td>69.09</td>
<td>73.11</td>
<td>69.44</td>
<td>68.68</td>
<td>75.56</td>
<td>77.31</td>
<td>74.40</td>
<td>73.10</td>
<td>59.95</td>
<td>87.86</td>
<td>77.78</td>
</tr>
<tr>
<td>LaBSE</td>
<td>74.11</td>
<td>5</td>
<td>77.00</td>
<td>69.19</td>
<td>73.55</td>
<td>70.34</td>
<td>69.83</td>
<td>76.38</td>
<td>74.94</td>
<td>70.84</td>
<td>73.20</td>
<td>59.52</td>
<td>87.89</td>
<td>78.47</td>
</tr>
<tr>
<td>XLM-RoBERTa-Base</td>
<td>73.60</td>
<td>6</td>
<td>76.35</td>
<td>69.37</td>
<td>73.42</td>
<td>68.45</td>
<td>67.45</td>
<td>74.05</td>
<td>74.26</td>
<td>70.44</td>
<td>71.40</td>
<td>60.19</td>
<td>87.90</td>
<td>78.28</td>
</tr>
<tr>
<td>RuBERT</td>
<td>73.45</td>
<td>7</td>
<td>74.03</td>
<td>66.14</td>
<td>70.75</td>
<td>66.46</td>
<td>66.40</td>
<td>73.37</td>
<td>75.49</td>
<td>71.86</td>
<td>72.15</td>
<td>60.55</td>
<td>86.99</td>
<td>77.41</td>
</tr>
<tr>
<td>MBART-50-Large-Many-to-Many</td>
<td>73.15</td>
<td>8</td>
<td>75.38</td>
<td>67.81</td>
<td>72.26</td>
<td>67.13</td>
<td>66.97</td>
<td>73.85</td>
<td>74.78</td>
<td>70.98</td>
<td>71.98</td>
<td>59.20</td>
<td>87.05</td>
<td>77.24</td>
</tr>
<tr>
<td>SlavicBERT</td>
<td>71.96</td>
<td>9</td>
<td>71.45</td>
<td>63.03</td>
<td>68.44</td>
<td>64.32</td>
<td>63.99</td>
<td>71.31</td>
<td>72.13</td>
<td>67.57</td>
<td>72.54</td>
<td>58.70</td>
<td>86.43</td>
<td>77.16</td>
</tr>
<tr>
<td>EnRuDR-BERT</td>
<td>71.51</td>
<td>10</td>
<td>72.56</td>
<td>64.74</td>
<td>69.07</td>
<td>61.44</td>
<td>60.21</td>
<td>68.34</td>
<td>74.19</td>
<td>69.94</td>
<td>69.33</td>
<td>56.55</td>
<td>87.12</td>
<td>77.95</td>
</tr>
<tr>
<td>RuDR-BERT</td>
<td>71.14</td>
<td>11</td>
<td>72.79</td>
<td>64.23</td>
<td>68.36</td>
<td>61.86</td>
<td>60.92</td>
<td>68.48</td>
<td>74.65</td>
<td>70.63</td>
<td>68.74</td>
<td>54.45</td>
<td>87.04</td>
<td>77.91</td>
</tr>
<tr>
<td>MBART-50-Large</td>
<td>69.46</td>
<td>12</td>
<td>70.91</td>
<td>62.67</td>
<td>67.24</td>
<td>61.12</td>
<td>60.25</td>
<td>68.41</td>
<td>72.88</td>
<td>68.63</td>
<td>70.52</td>
<td>46.39</td>
<td>86.48</td>
<td>77.52</td>
</tr>
</tbody>
</table>
The table shows per-task scores and a macro-average of those scores to determine a models’s position on the leaderboard. For datasets with multiple evaluation metrics (e.g., macro F1 and weighted F1 for RuSentiment), we use an unweighted average of the metrics as the score for the task when computing the overall macro-average. The same strategy for comparing models’ results was applied in the GLUE benchmark.
## Citation
If you find this repository helpful, feel free to cite our publication:
```
@article{Smetanin2021Deep,
author = {Sergey Smetanin and Mikhail Komarov},
title = {Deep transfer learning baselines for sentiment analysis in Russian},
journal = {Information Processing & Management},
volume = {58},
number = {3},
pages = {102484},
year = {2021},
issn = {0306-4573},
doi = {0.1016/j.ipm.2020.102484}
}
```
Dataset:
```
@inproceedings{rogers2018rusentiment,
title={RuSentiment: An enriched sentiment analysis dataset for social media in Russian},
author={Rogers, Anna and Romanov, Alexey and Rumshisky, Anna and Volkova, Svitlana and Gronas, Mikhail and Gribov, Alex},
booktitle={Proceedings of the 27th international conference on computational linguistics},
pages={755--763},
year={2018}
}
```
|
shoarora/electra-small-owt
|
shoarora
| 2021-05-20T05:54:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# ELECTRA-small-OWT
This is an unnoficial implementation of an
[ELECTRA](https://openreview.net/forum?id=r1xMH1BtvB) small model, trained on the
[OpenWebText corpus](https://skylion007.github.io/OpenWebTextCorpus/).
Differences from official ELECTRA models:
- we use a `BertForMaskedLM` as the generator and `BertForTokenClassification` as the discriminator
- they use an embedding projection layer, but Bert doesn't have one
## Pretraining ttask
(figure from [Clark et al. 2020](https://openreview.net/pdf?id=r1xMH1BtvB))
ELECTRA uses discriminative LM / replaced-token-detection for pretraining.
This involves a generator (a Masked LM model) creating examples for a discriminator
to classify as original or replaced for each token.
## Usage
```python
from transformers import BertForSequenceClassification, BertTokenizer
tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
electra = BertForSequenceClassification.from_pretrained('shoarora/electra-small-owt')
```
## Code
The pytorch module that implements this task is available [here](https://github.com/shoarora/lmtuners/blob/master/lmtuners/lightning_modules/discriminative_lm.py).
Further implementation information [here](https://github.com/shoarora/lmtuners/tree/master/experiments/disc_lm_small),
and [here](https://github.com/shoarora/lmtuners/blob/master/experiments/disc_lm_small/train_electra_small.py) is the script that created this model.
This specific model was trained with the following params:
- `batch_size: 512`
- `training_steps: 5e5`
- `warmup_steps: 4e4`
- `learning_rate: 2e-3`
## Downstream tasks
#### GLUE Dev results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| ELECTRA-Small++ | 14M | 57.0 | 91. | 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT | 14M | 56.8 | 88.3| 87.4 | 86.8 | 88.3 | 78.9 | 87.9 | 68.5|
| ELECTRA-Small-OWT (ours) | 17M | 56.3 | 88.4| 75.0 | 86.1 | 89.1 | 77.9 | 83.0 | 67.1|
| ALECTRA-Small-OWT (ours) | 4M | 50.6 | 89.1| 86.3 | 87.2 | 89.1 | 78.2 | 85.9 | 69.6|
- Table initialized from [ELECTRA github repo](https://github.com/google-research/electra)
#### GLUE Test results
| Model | # Params | CoLA | SST | MRPC | STS | QQP | MNLI | QNLI | RTE |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| BERT-Base | 110M | 52.1 | 93.5| 84.8 | 85.9 | 89.2 | 84.6 | 90.5 | 66.4|
| GPT | 117M | 45.4 | 91.3| 75.7 | 80.0 | 88.5 | 82.1 | 88.1 | 56.0|
| ELECTRA-Small++ | 14M | 57.0 | 91.2| 88.0 | 87.5 | 89.0 | 81.3 | 88.4 | 66.7|
| ELECTRA-Small-OWT (ours) | 17M | 57.4 | 89.3| 76.2 | 81.9 | 87.5 | 78.1 | 82.4 | 68.1|
| ALECTRA-Small-OWT (ours) | 4M | 43.9 | 87.9| 82.1 | 82.0 | 87.6 | 77.9 | 85.8 | 67.5|
|
junnyu/bert_chinese_mc_base
|
junnyu
| 2021-05-20T05:28:56Z | 8 | 3 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
https://github.com/alibaba-research/ChineseBLUE
|
sello-ralethe/bert-base-frozen-generics-mlm
|
sello-ralethe
| 2021-05-20T05:11:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
BERT model finetuned for masked language modeling on generics dataset by freezing all the weights of pretrained BERT except the last layer. The aim is to investigate if the model will overgeneralize generics and treat quantified statements such as 'All ducks lay eggs', 'All tigers have stripes' as if these are generics.
|
sadakmed/dpr-passage_encoder-spanish
|
sadakmed
| 2021-05-20T04:37:11Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"dpr",
"es",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language: es
tags:
- dpr
---
This is a DPR passage_encoder model, finetuned with `dpr-question_encoder-spanish` on Spanish question answering data.
|
rohanrajpal/bert-base-codemixed-uncased-sentiment
|
rohanrajpal
| 2021-05-20T04:32:54Z | 18 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"hi",
"en",
"codemix",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- hi
- en
tags:
- hi
- en
- codemix
datasets:
- SAIL 2017
---
# Model name
## Model description
I took a bert-base-multilingual-cased model from huggingface and finetuned it on SAIL 2017 dataset.
## Intended uses & limitations
#### How to use
```python
# You can include sample code which will be formatted
#Coming soon!
```
#### Limitations and bias
Provide examples of latent issues and potential remediations.
## Training data
I trained on the SAIL 2017 dataset [link](http://amitavadas.com/SAIL/Data/SAIL_2017.zip) on this [pretrained model](https://huggingface.co/bert-base-multilingual-cased).
## Training procedure
No preprocessing.
## Eval results
### BibTeX entry and citation info
```bibtex
@inproceedings{khanuja-etal-2020-gluecos,
title = "{GLUEC}o{S}: An Evaluation Benchmark for Code-Switched {NLP}",
author = "Khanuja, Simran and
Dandapat, Sandipan and
Srinivasan, Anirudh and
Sitaram, Sunayana and
Choudhury, Monojit",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2020.acl-main.329",
pages = "3575--3585"
}
```
|
ahmedabdelali/bert-base-qarib60_1970k
|
ahmedabdelali
| 2021-05-20T03:46:19Z | 14 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tf",
"qarib",
"qarib60_1790k",
"ar",
"dataset:arabic_billion_words",
"dataset:open_subtitles",
"dataset:twitter",
"arxiv:2102.10684",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ar
tags:
- pytorch
- tf
- qarib
- qarib60_1790k
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."
---
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
### bert-base-qarib60_1970k
- Data size: 60Gb
- Number of Iterations: 1970k
- Loss: 1.5708898
## Training QARiB
The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
See more details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)
## Using QARiB
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 downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")
>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]
>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")
[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
```
## Training procedure
The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
## Eval results
We evaluated QARiB models on five NLP downstream task:
- Sentiment Analysis
- Emotion Detection
- Named-Entity Recognition (NER)
- Offensive Language Detection
- Dialect Identification
The results obtained from QARiB models outperforms multilingual BERT/AraBERT/ArabicBERT.
## Model Weights and Vocab Download
From Huggingface site: https://huggingface.co/qarib/qarib/bert-base-qarib60_1970k
## Contacts
Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
## Reference
```
@article{abdelali2021pretraining,
title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
year={2021},
eprint={2102.10684},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ahmedabdelali/bert-base-qarib60_1790k
|
ahmedabdelali
| 2021-05-20T03:44:18Z | 49 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tf",
"qarib",
"qarib60_1790k",
"ar",
"dataset:arabic_billion_words",
"dataset:open_subtitles",
"dataset:twitter",
"arxiv:2102.10684",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ar
tags:
- pytorch
- tf
- qarib
- qarib60_1790k
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."
---
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For Tweets, the data was collected using twitter API and using language filter. `lang:ar`. For Text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
### bert-base-qarib60_1790k
- Data size: 60Gb
- Number of Iterations: 1790k
- Loss: 1.8764963
## Training QARiB
The training of the model has been performed using Google’s original Tensorflow code on Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
See more details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)
## Using QARiB
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 downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")
>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]
>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")
[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
```
## Training procedure
The training of the model has been performed using Google’s original Tensorflow code on eight core Google Cloud TPU v2.
We used a Google Cloud Storage bucket, for persistent storage of training data and models.
## Eval results
We evaluated QARiB models on five NLP downstream task:
- Sentiment Analysis
- Emotion Detection
- Named-Entity Recognition (NER)
- Offensive Language Detection
- Dialect Identification
The results obtained from QARiB models outperforms multilingual BERT/AraBERT/ArabicBERT.
## Model Weights and Vocab Download
From Huggingface site: https://huggingface.co/qarib/qarib/bert-base-qarib60_1790k
## Contacts
Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
## Reference
```
@article{abdelali2021pretraining,
title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
year={2021},
eprint={2102.10684},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
ahmedabdelali/bert-base-qarib
|
ahmedabdelali
| 2021-05-20T03:42:19Z | 1,216 | 9 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"tf",
"QARiB",
"qarib",
"ar",
"dataset:arabic_billion_words",
"dataset:open_subtitles",
"dataset:twitter",
"arxiv:2102.10684",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ar
tags:
- pytorch
- tf
- QARiB
- qarib
datasets:
- arabic_billion_words
- open_subtitles
- twitter
metrics:
- f1
widget:
- text: " شو عندكم يا [MASK] ."
---
# QARiB: QCRI Arabic and Dialectal BERT
## About QARiB
QCRI Arabic and Dialectal BERT (QARiB) model, was trained on a collection of ~ 420 Million tweets and ~ 180 Million sentences of text.
For the tweets, the data was collected using twitter API and using language filter. `lang:ar`. For the text data, it was a combination from
[Arabic GigaWord](url), [Abulkhair Arabic Corpus]() and [OPUS](http://opus.nlpl.eu/).
QARiB: Is the Arabic name for "Boat".
## Model and Parameters:
- Data size: 14B tokens
- Vocabulary: 64k
- Iterations: 10M
- Number of Layers: 12
## Training QARiB
See details in [Training QARiB](https://github.com/qcri/QARIB/Training_QARiB.md)
## Using QARiB
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 downstream task. See the model hub to look for fine-tuned versions on a task that interests you. For more details, see [Using QARiB](https://github.com/qcri/QARIB/Using_QARiB.md)
### How to use
You can use this model directly with a pipeline for masked language modeling:
```python
>>>from transformers import pipeline
>>>fill_mask = pipeline("fill-mask", model="./models/data60gb_86k")
>>> fill_mask("شو عندكم يا [MASK]")
[{'sequence': '[CLS] شو عندكم يا عرب [SEP]', 'score': 0.0990147516131401, 'token': 2355, 'token_str': 'عرب'},
{'sequence': '[CLS] شو عندكم يا جماعة [SEP]', 'score': 0.051633741706609726, 'token': 2308, 'token_str': 'جماعة'},
{'sequence': '[CLS] شو عندكم يا شباب [SEP]', 'score': 0.046871256083250046, 'token': 939, 'token_str': 'شباب'},
{'sequence': '[CLS] شو عندكم يا رفاق [SEP]', 'score': 0.03598872944712639, 'token': 7664, 'token_str': 'رفاق'},
{'sequence': '[CLS] شو عندكم يا ناس [SEP]', 'score': 0.031996358186006546, 'token': 271, 'token_str': 'ناس'}
]
>>> fill_mask("وقام المدير [MASK]")
[
{'sequence': '[CLS] وقام المدير بالعمل [SEP]', 'score': 0.0678194984793663, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقام المدير بذلك [SEP]', 'score': 0.05191086605191231, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقام المدير بالاتصال [SEP]', 'score': 0.045264165848493576, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقام المدير بعمله [SEP]', 'score': 0.03732728958129883, 'token': 40486, 'token_str': 'بعمله'},
{'sequence': '[CLS] وقام المدير بالامر [SEP]', 'score': 0.0246378555893898, 'token': 29124, 'token_str': 'بالامر'}
]
>>> fill_mask("وقامت المديرة [MASK]")
[{'sequence': '[CLS] وقامت المديرة بذلك [SEP]', 'score': 0.23992691934108734, 'token': 984, 'token_str': 'بذلك'},
{'sequence': '[CLS] وقامت المديرة بالامر [SEP]', 'score': 0.108805812895298, 'token': 29124, 'token_str': 'بالامر'},
{'sequence': '[CLS] وقامت المديرة بالعمل [SEP]', 'score': 0.06639821827411652, 'token': 4230, 'token_str': 'بالعمل'},
{'sequence': '[CLS] وقامت المديرة بالاتصال [SEP]', 'score': 0.05613093823194504, 'token': 26096, 'token_str': 'بالاتصال'},
{'sequence': '[CLS] وقامت المديرة المديرة [SEP]', 'score': 0.021778125315904617, 'token': 41635, 'token_str': 'المديرة'}]
>>> fill_mask("قللي وشفيييك يرحم [MASK]")
[{'sequence': '[CLS] قللي وشفيييك يرحم والديك [SEP]', 'score': 0.4152909517288208, 'token': 9650, 'token_str': 'والديك'},
{'sequence': '[CLS] قللي وشفيييك يرحملي [SEP]', 'score': 0.07663793861865997, 'token': 294, 'token_str': '##لي'},
{'sequence': '[CLS] قللي وشفيييك يرحم حالك [SEP]', 'score': 0.0453166700899601, 'token': 2663, 'token_str': 'حالك'},
{'sequence': '[CLS] قللي وشفيييك يرحم امك [SEP]', 'score': 0.04390475153923035, 'token': 1942, 'token_str': 'امك'},
{'sequence': '[CLS] قللي وشفيييك يرحمونك [SEP]', 'score': 0.027349254116415977, 'token': 3283, 'token_str': '##ونك'}]
```
## Evaluations:
|**Experiment** |**mBERT**|**AraBERT0.1**|**AraBERT1.0**|**ArabicBERT**|**QARiB**|
|---------------|---------|--------------|--------------|--------------|---------|
|Dialect Identification | 6.06% | 59.92% | 59.85% | 61.70% | **65.21%** |
|Emotion Detection | 27.90% | 43.89% | 42.37% | 41.65% | **44.35%** |
|Named-Entity Recognition (NER) | 49.38% | 64.97% | **66.63%** | 64.04% | 61.62% |
|Offensive Language Detection | 83.14% | 88.07% | 88.97% | 88.19% | **91.94%** |
|Sentiment Analysis | 86.61% | 90.80% | **93.58%** | 83.27% | 93.31% |
## Model Weights and Vocab Download
From Huggingface site: https://huggingface.co/qarib/bert-base-qarib
## Contacts
Ahmed Abdelali, Sabit Hassan, Hamdy Mubarak, Kareem Darwish and Younes Samih
## Reference
```
@article{abdelali2021pretraining,
title={Pre-Training BERT on Arabic Tweets: Practical Considerations},
author={Ahmed Abdelali and Sabit Hassan and Hamdy Mubarak and Kareem Darwish and Younes Samih},
year={2021},
eprint={2102.10684},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
pin/analytical
|
pin
| 2021-05-20T02:44:25Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"danish",
"sentiment",
"analytical",
"da",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: da
tags:
- danish
- bert
- sentiment
- analytical
license: cc-by-4.0
widget:
- text: "Jeg synes, det er en elendig film"
---
# Danish BERT fine-tuned for Detecting 'Analytical'
This model detects if a Danish text is 'subjective' or 'objective'.
It is trained and tested on Tweets and texts transcribed from the European Parliament annotated by [Alexandra Institute](https://github.com/alexandrainst). The model is trained with the [`senda`](https://github.com/ebanalyse/senda) package.
Here is an example of how to load the model in PyTorch using the [🤗Transformers](https://github.com/huggingface/transformers) library:
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("pin/analytical")
model = AutoModelForSequenceClassification.from_pretrained("pin/analytical")
# create 'senda' sentiment analysis pipeline
analytical_pipeline = pipeline('sentiment-analysis', model=model, tokenizer=tokenizer)
text = "Jeg synes, det er en elendig film"
# in English: 'I think, it is a terrible movie'
analytical_pipeline(text)
```
## Performance
The `senda` model achieves an accuracy of 0.89 and a macro-averaged F1-score of 0.78 on a small test data set, that [Alexandra Institute](https://github.com/alexandrainst/danlp/blob/master/docs/docs/datasets.md#twitter-sentiment) provides. The model can most certainly be improved, and we encourage all NLP-enthusiasts to give it their best shot - you can use the [`senda`](https://github.com/ebanalyse/senda) package to do this.
#### Contact
Feel free to contact author Lars Kjeldgaard on [lars.kjeldgaard@eb.dk](mailto:lars.kjeldgaard@eb.dk).
|
phiyodr/bert-large-finetuned-squad2
|
phiyodr
| 2021-05-20T02:36:12Z | 20,952 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"question-answering",
"en",
"dataset:squad2",
"arxiv:1810.04805",
"arxiv:1806.03822",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- pytorch
- question-answering
datasets:
- squad2
metrics:
- exact
- f1
widget:
- text: "What discipline did Winkelmann create?"
context: "Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the difference between Greek, Greco-Roman and Roman art. The prophet and founding hero of modern archaeology, Winckelmann was one of the founders of scientific archaeology and first applied the categories of style on a large, systematic basis to the history of art."
---
# bert-large-finetuned-squad2
## Model description
This model is based on **[bert-large-uncased](https://huggingface.co/bert-large-uncased)** and was finetuned on **[SQuAD2.0](https://rajpurkar.github.io/SQuAD-explorer/)**. The corresponding papers you can found [here (model)](https://arxiv.org/abs/1810.04805) and [here (data)](https://arxiv.org/abs/1806.03822).
## How to use
```python
from transformers.pipelines import pipeline
model_name = "phiyodr/bert-large-finetuned-squad2"
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
inputs = {
'question': 'What discipline did Winkelmann create?',
'context': 'Johann Joachim Winckelmann was a German art historian and archaeologist. He was a pioneering Hellenist who first articulated the difference between Greek, Greco-Roman and Roman art. "The prophet and founding hero of modern archaeology", Winckelmann was one of the founders of scientific archaeology and first applied the categories of style on a large, systematic basis to the history of art. '
}
nlp(inputs)
```
## Training procedure
```
{
"base_model": "bert-large-uncased",
"do_lower_case": True,
"learning_rate": 3e-5,
"num_train_epochs": 4,
"max_seq_length": 384,
"doc_stride": 128,
"max_query_length": 64,
"batch_size": 96
}
```
## Eval results
- Data: [dev-v2.0.json](https://rajpurkar.github.io/SQuAD-explorer/dataset/dev-v2.0.json)
- Script: [evaluate-v2.0.py](https://worksheets.codalab.org/rest/bundles/0x6b567e1cf2e041ec80d7098f031c5c9e/contents/blob/) (original script from [here](https://github.com/huggingface/transformers/blob/master/examples/question-answering/README.md))
```
{
"exact": 76.22336393497852,
"f1": 79.72527570261339,
"total": 11873,
"HasAns_exact": 76.19770580296895,
"HasAns_f1": 83.21157193271408,
"HasAns_total": 5928,
"NoAns_exact": 76.24894869638352,
"NoAns_f1": 76.24894869638352,
"NoAns_total": 5945
}
```
|
nreimers/BERT-Small-L-4_H-512_A-8
|
nreimers
| 2021-05-20T02:03:04Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"feature-extraction",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
# BERT-Small-L-4_H-512_A-8
This is a port of the [BERT-Small model](https://github.com/google-research/bert) to Pytorch. It uses 4 layers, a hidden size of 512 and 8 attention heads.
|
nimaafshar/parsbert-fa-sentiment-twitter
|
nimaafshar
| 2021-05-20T01:50:49Z | 14 | 1 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
ParsBERT digikala sentiment analysis model fine-tuned on around 600,000 Persian tweets.
# How to use
at least you need 650 megabytes of ram and disk in order to load the model.
tensorflow, transformers and numpy library
## Loading model
```python
import numpy as np
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification
#loading model
tokenizer = AutoTokenizer.from_pretrained("nimaafshar/parsbert-fa-sentiment-twitter")
model = TFAutoModelForSequenceClassification.from_pretrained("nimaafshar/parsbert-fa-sentiment-twitter")
classes = ["negative","neutral","positive"]
```
## Using Model
```python
#using model
sequences = [".غذا خیلی افتضاح بود متاسفم برای مدیریت رستورن خیلی بد بود.",
"خیلی خوشمزده و عالی بود عالی",
"میتونم اسمتونو بپرسم؟"
]
for sequence in sequences:
inputs = tokenizer(sequence, return_tensors="tf")
classification_logits = model(inputs)[0]
results = tf.nn.softmax(classification_logits, axis=1).numpy()[0]
print(classes[np.argmax(results)])
percentages = np.around(results*100)
print(percentages)
```
note that this model is trained on persian corpus and is meant to be used on persian texts too.
|
nikunjbjj/jd-resume-model
|
nikunjbjj
| 2021-05-20T01:50:03Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
# Sentiment Analysis in Spanish
## beto-sentiment-analysis
Repository: [https://github.com/finiteautomata/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with TASS 2020 corpus (around ~5k tweets) of several dialects of Spanish. Base model is [BETO](https://github.com/dccuchile/beto), a BERT model trained in Spanish.
Uses `POS`, `NEG`, `NEU` labels.
**Coming soon**: a brief paper describing the model and training.
Enjoy! 🤗
|
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