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
2025-09-11 12:33:28
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 555
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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| card
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|---|---|---|---|---|---|---|---|---|---|
christy/TEST2ppo-LunarLander-v2
|
christy
| 2022-05-04T23:56:23Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T23:55:41Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 164.94 +/- 74.94
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
akkasayaz/ppo-LunarLander-v2
|
akkasayaz
| 2022-05-04T23:44:34Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T23:43:59Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 233.78 +/- 19.45
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
reaprtripr/distillRobertaMLM-csnPython-finetuned-imdb
|
reaprtripr
| 2022-05-04T22:05:12Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-04T18:59:21Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: reaprtripr/distillRobertaMLM-csnPython-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# reaprtripr/distillRobertaMLM-csnPython-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.1703
- Validation Loss: 2.6914
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, '__passive_serialization__': True}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.1703 | 2.6914 | 0 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/MediumInformalToFormalLincoln4
|
BigSalmon
| 2022-05-04T21:12:03Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T21:02:07Z |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/MediumInformalToFormalLincoln4")
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- declining viewership facing the nba.
- does not have to be this way.
- in fact, many solutions exist.
- the four point line would surely draw in eyes.
text: failing to draw in the masses, the nba has ( fallen into / succumb to / bowed to ) disrepair. such does not have to be the case, however. in fact, a myriad of simple, relatively cheap ( solutions / interventions / enhancements ) could revive the league. the addition of the much-hyped four-point line would surely juice viewership.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
|
elotech/ppo-LunarLander-v0
|
elotech
| 2022-05-04T20:57:09Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T20:29:19Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO_v2
results:
- metrics:
- type: mean_reward
value: 275.76 +/- 26.02
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO_v2** Agent playing **LunarLander-v2**
This is a trained model of a **PPO_v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
mmangino/ppo-LunarLander-v2
|
mmangino
| 2022-05-04T20:24:48Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T20:24:21Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 282.72 +/- 23.16
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
utkusaglm/ppo-LunarLander-v1
|
utkusaglm
| 2022-05-04T20:23:28Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T20:17:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 295.94 +/- 13.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
NessrineT/ppo-LunarLander-v2
|
NessrineT
| 2022-05-04T19:55:27Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T18:11:52Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 265.98 +/- 9.31
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
SaiShashank1303/ch-1-ppo-LunarLander-v2
|
SaiShashank1303
| 2022-05-04T19:04:07Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T19:03:25Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: ppo-LunarLander-v2
results:
- metrics:
- type: mean_reward
value: 203.94 +/- 26.92
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **ppo-LunarLander-v2** Agent playing **LunarLander-v2**
This is a trained model of a **ppo-LunarLander-v2** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/kanyewest-usmnt
|
huggingtweets
| 2022-05-04T18:51:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T17:44:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/kanyewest-usmnt/1651690314434/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410587808666955776/mWkKWw1U_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1276461929934942210/cqNhNk6v_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">USMNT & ye</div>
<div style="text-align: center; font-size: 14px;">@kanyewest-usmnt</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from USMNT & ye.
| Data | USMNT | ye |
| --- | --- | --- |
| Tweets downloaded | 3247 | 1858 |
| Retweets | 600 | 188 |
| Short tweets | 215 | 573 |
| Tweets kept | 2432 | 1097 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/12os8ehp/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @kanyewest-usmnt's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2pwtssam) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2pwtssam/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/kanyewest-usmnt')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
CWhy/given-ppo-LunarLander-v2
|
CWhy
| 2022-05-04T18:44:21Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T18:43:43Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 198.92 +/- 36.84
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Harri/2nd_try_ppo-LunarLander-v2
|
Harri
| 2022-05-04T18:27:55Z | 0 | 1 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T18:27:29Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 279.01 +/- 16.25
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
Harri/ppo-LunarLander-v2
|
Harri
| 2022-05-04T17:55:28Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T17:53:11Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 215.15 +/- 49.08
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
KoenBronstring/finetuning-sentiment-model-3000-samples
|
KoenBronstring
| 2022-05-04T17:53:58Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T12:08:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.8758169934640523
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3149
- Accuracy: 0.8733
- F1: 0.8758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
seriy21/ppo-LunarLander-v2_tst2
|
seriy21
| 2022-05-04T17:32:54Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T17:32:28Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 291.14 +/- 17.01
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/usmnt-zacksteffen_
|
huggingtweets
| 2022-05-04T17:19:08Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T17:18:29Z |
---
language: en
thumbnail: http://www.huggingtweets.com/usmnt-zacksteffen_/1651684743123/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410587808666955776/mWkKWw1U_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509644465388105731/dErjQdWT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">USMNT & Zack Steffen</div>
<div style="text-align: center; font-size: 14px;">@usmnt-zacksteffen_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from USMNT & Zack Steffen.
| Data | USMNT | Zack Steffen |
| --- | --- | --- |
| Tweets downloaded | 3250 | 3120 |
| Retweets | 600 | 869 |
| Short tweets | 215 | 523 |
| Tweets kept | 2435 | 1728 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/34uud8si/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @usmnt-zacksteffen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2wiyd3kq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2wiyd3kq/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/usmnt-zacksteffen_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
robertou2/TEST2ppo-LunarLander-v2
|
robertou2
| 2022-05-04T17:05:32Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:47:58Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 286.33 +/- 13.08
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2
|
MartinoMensio
| 2022-05-04T16:28:04Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T17:06:08Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-nonstrict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9680026173591614}, {'label': 'non-racist', 'score': 0.9936750531196594}]
```
For more details, see https://github.com/preyero/neatclass22
|
Guillaume63/ppo-LunarLander-v2
|
Guillaume63
| 2022-05-04T16:27:19Z | 3 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T16:26:48Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PP0
results:
- metrics:
- type: mean_reward
value: 223.27 +/- 26.13
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PP0** Agent playing **LunarLander-v2**
This is a trained model of a **PP0** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
MartinoMensio/racism-models-w-m-vote-strict-epoch-3
|
MartinoMensio
| 2022-05-04T16:26:07Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:57:00Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `w-m-vote-strict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'w-m-vote-strict-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9619585871696472}, {'label': 'non-racist', 'score': 0.9396700859069824}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-regression-w-m-vote-epoch-4
|
MartinoMensio
| 2022-05-04T16:22:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:22:35Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8345461}, {'score': 0.48615143}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8345461}, {'label': 'non-racist', 'score': 0.48615143}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-regression-w-m-vote-epoch-2
|
MartinoMensio
| 2022-05-04T16:20:44Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:18:45Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `regression-w-m-vote-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
from transformers.pipelines import TextClassificationPipeline
class TextRegressionPipeline(TextClassificationPipeline):
"""
Class based on the TextClassificationPipeline from transformers.
The difference is that instead of being based on a classifier, it is based on a regressor.
You can specify the regression threshold when you call the pipeline or when you instantiate the pipeline.
"""
def __init__(self, **kwargs):
"""
Builds a new Pipeline based on regression.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold = kwargs.pop("regression_threshold", None)
super().__init__(**kwargs)
def __call__(self, *args, **kwargs):
"""
You can also specify the regression threshold when you call the pipeline.
regression_threshold: Optional(float). If None, the pipeline will simply output the score. If set to a specific value, the output will be both the score and the label.
"""
self.regression_threshold_call = kwargs.pop("regression_threshold", None)
result = super().__call__(*args, **kwargs)
return result
def postprocess(self, model_outputs, function_to_apply=None, return_all_scores=False):
outputs = model_outputs["logits"][0]
outputs = outputs.numpy()
scores = outputs
score = scores[0]
regression_threshold = self.regression_threshold
# override the specific threshold if it is specified in the call
if self.regression_threshold_call:
regression_threshold = self.regression_threshold_call
if regression_threshold:
return {"label": 'racist' if score > regression_threshold else 'non-racist', "score": score}
else:
return {"score": score}
model_name = 'regression-w-m-vote-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = TextRegressionPipeline(model=model, tokenizer=tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
# just get the score of regression
print(pipe(texts))
# [{'score': 0.8367272}, {'score': 0.4402479}]
# or also specify a threshold to cut racist/non-racist
print(pipe(texts, regression_threshold=0.9))
# [{'label': 'non-racist', 'score': 0.8367272}, {'label': 'non-racist', 'score': 0.4402479}]
```
For more details, see https://github.com/preyero/neatclass22
|
huggingtweets/zacksteffen_
|
huggingtweets
| 2022-05-04T16:16:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T16:15:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/zacksteffen_/1651680987265/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509644465388105731/dErjQdWT_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Zack Steffen</div>
<div style="text-align: center; font-size: 14px;">@zacksteffen_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Zack Steffen.
| Data | Zack Steffen |
| --- | --- |
| Tweets downloaded | 3120 |
| Retweets | 869 |
| Short tweets | 523 |
| Tweets kept | 1728 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1nz1w2dd/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @zacksteffen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lqwnrcja) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lqwnrcja/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/zacksteffen_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
MartinoMensio/racism-models-m-vote-nonstrict-epoch-4
|
MartinoMensio
| 2022-05-04T16:14:06Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:50:19Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-nonstrict-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9791656136512756}, {'label': 'non-racist', 'score': 0.996966540813446}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-nonstrict-epoch-2
|
MartinoMensio
| 2022-05-04T16:12:34Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:46:17Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-nonstrict-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-nonstrict-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.8650100827217102}, {'label': 'non-racist', 'score': 0.9674995541572571}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-strict-epoch-4
|
MartinoMensio
| 2022-05-04T16:10:41Z | 98 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:41:38Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-4`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-4'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9965864419937134}, {'label': 'racist', 'score': 0.6058831214904785}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-strict-epoch-3
|
MartinoMensio
| 2022-05-04T16:09:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:35:22Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-3`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-3'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.9929012656211853}, {'label': 'non-racist', 'score': 0.5616322159767151}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-m-vote-strict-epoch-1
|
MartinoMensio
| 2022-05-04T16:07:46Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:29:06Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `m-vote-strict-epoch-1`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'm-vote-strict-epoch-1'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.6074065566062927}, {'label': 'non-racist', 'score': 0.8047575950622559}]
```
For more details, see https://github.com/preyero/neatclass22
|
MartinoMensio/racism-models-raw-label-epoch-2
|
MartinoMensio
| 2022-05-04T16:04:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-15T16:04:35Z |
---
language: es
license: mit
widget:
- text: "y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!"
---
### Description
This model is a fine-tuned version of [BETO (spanish bert)](https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased) that has been trained on the *Datathon Against Racism* dataset (2022)
We performed several experiments that will be described in the upcoming paper "Estimating Ground Truth in a Low-labelled Data Regime:A Study of Racism Detection in Spanish" (NEATClasS 2022)
We applied 6 different methods ground-truth estimations, and for each one we performed 4 epochs of fine-tuning. The result is made of 24 models:
| method | epoch 1 | epoch 3 | epoch 3 | epoch 4 |
|--- |--- |--- |--- |--- |
| raw-label | [raw-label-epoch-1](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-1) | [raw-label-epoch-2](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-2) | [raw-label-epoch-3](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-3) | [raw-label-epoch-4](https://huggingface.co/MartinoMensio/racism-models-raw-label-epoch-4) |
| m-vote-strict | [m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-1) | [m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-2) | [m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-3) | [m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-strict-epoch-4) |
| m-vote-nonstrict | [m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-1) | [m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-2) | [m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-3) | [m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-m-vote-nonstrict-epoch-4) |
| regression-w-m-vote | [regression-w-m-vote-epoch-1](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-1) | [regression-w-m-vote-epoch-2](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-2) | [regression-w-m-vote-epoch-3](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-3) | [regression-w-m-vote-epoch-4](https://huggingface.co/MartinoMensio/racism-models-regression-w-m-vote-epoch-4) |
| w-m-vote-strict | [w-m-vote-strict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-1) | [w-m-vote-strict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-2) | [w-m-vote-strict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-3) | [w-m-vote-strict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-strict-epoch-4) |
| w-m-vote-nonstrict | [w-m-vote-nonstrict-epoch-1](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-1) | [w-m-vote-nonstrict-epoch-2](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-2) | [w-m-vote-nonstrict-epoch-3](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-3) | [w-m-vote-nonstrict-epoch-4](https://huggingface.co/MartinoMensio/racism-models-w-m-vote-nonstrict-epoch-4) |
This model is `raw-label-epoch-2`
### Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, pipeline
model_name = 'raw-label-epoch-2'
tokenizer = AutoTokenizer.from_pretrained("dccuchile/bert-base-spanish-wwm-uncased")
full_model_path = f'MartinoMensio/racism-models-{model_name}'
model = AutoModelForSequenceClassification.from_pretrained(full_model_path)
pipe = pipeline("text-classification", model = model, tokenizer = tokenizer)
texts = [
'y porqué es lo que hay que hacer con los menas y con los adultos también!!!! NO a los inmigrantes ilegales!!!!',
'Es que los judíos controlan el mundo'
]
print(pipe(texts))
# [{'label': 'racist', 'score': 0.8982619643211365}, {'label': 'non-racist', 'score': 0.6703745126724243}]
```
For more details, see https://github.com/preyero/neatclass22
|
Phaneo/ppo-LunarLander-v2
|
Phaneo
| 2022-05-04T16:03:23Z | 4 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T14:58:54Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 275.14 +/- 17.29
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
huggingtweets/cpulisic_10-usmnt-zacksteffen_
|
huggingtweets
| 2022-05-04T16:00:44Z | 3 | 1 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-04T16:00:36Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1410587808666955776/mWkKWw1U_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509644465388105731/dErjQdWT_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1511457717281607680/SuAprf1T_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">USMNT & Zack Steffen & Christian Pulisic</div>
<div style="text-align: center; font-size: 14px;">@cpulisic_10-usmnt-zacksteffen_</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from USMNT & Zack Steffen & Christian Pulisic.
| Data | USMNT | Zack Steffen | Christian Pulisic |
| --- | --- | --- | --- |
| Tweets downloaded | 3243 | 3120 | 1159 |
| Retweets | 599 | 869 | 629 |
| Short tweets | 215 | 523 | 93 |
| Tweets kept | 2429 | 1728 | 437 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/395einau/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @cpulisic_10-usmnt-zacksteffen_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1x9olwhx) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1x9olwhx/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/cpulisic_10-usmnt-zacksteffen_')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
LidarRL/TEST2ppo-LunarLander-v2
|
LidarRL
| 2022-05-04T15:10:24Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-05-04T14:20:45Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 204.65 +/- 31.76
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2** using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
|
dbmdz/flair-hipe-2022-ajmc-all
|
dbmdz
| 2022-05-04T13:43:34Z | 10 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"multilingual",
"license:mit",
"region:us"
] |
token-classification
| 2022-04-29T07:26:42Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: multilingual
widget:
- text: "In editing the Fragments , I have availed myself of Mr . R . Ellis ’ acute remarks on them in the Cambridge Journal of Philology , Vol . IV , and that I am largely indebted , as every editor must now be , to the edition of the Tragic Fragments by A . Nauck , Leipzig , 1856 ."
- text: "459 . Skyros klang dem Athener etwa wie Pholegandros und Sikinos bei Solon Eleg . 1 , 4 , dem Römer Ulubrae , Butunti ."
- text: "Celles d ’ Ajax et des siens occupaient l ' extrême aile gauche , vers le promontoire Rhétée , et confinaient tout à la fois au retranchement et à la mer ( // . XIT1 , 681 ; Heynce , excursns cité ) ,"
license: mit
---
|
thuannc/vi-distilled-msmarco-MiniLM-L12-cos-v5
|
thuannc
| 2022-05-04T12:52:08Z | 6 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2004.09813",
"license:mit",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-04T10:10:10Z |
---
license: mit
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a Vietnamese [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like questions answering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
The thesis will be available on [https://github.com/ncthuan/uet-qa](https://github.com/ncthuan/uet-qa) with evaluation results in chapter 4.
paraphrase-multilingual-minilm: 75 recall@10, 49 MRR@10
this model: 85 recall@10, 58 MRR@10
## Training
It was distilled using English-Vietnamese parallel data with this [training script](https://github.com/ncthuan/uet-qa/blob/main/scripts/train/make_multilingual.py)
that follows the work of [Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation](https://www.sbert.net/examples/training/multilingual/README.html)
teacher: msmarco-MiniLM-L12-cos-v5
student: paraphrase-multilingual-minilm-L12-v2
Data: PhoMT, MKQA, MLQA, XQuAD
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 40148 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 2000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"correct_bias": false,
"eps": 1e-06,
"lr": 1e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 2000,
"weight_decay": 0.005
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
```
@inproceedings{reimers-2020-multilingual-sentence-bert,
title = "Making Monolingual Sentence Embeddings Multilingual using Knowledge Distillation",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2020",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/2004.09813",
}
@article{thuan2022-uetqa,
title={{Extractive question answering system on regulations for University of Engineering and Technology}},
author={Nguyen, Thuan},
journal={Undergraduate Thesis, University of Engineering and Technology, Vietnam National University Hanoi},
year={2022}
}
```
|
guhuawuli/distilbert-imdb
|
guhuawuli
| 2022-05-04T10:57:23Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-04T10:35:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 391 | 0.1846 | 0.9288 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0a0+3fd9dcf
- Datasets 2.1.0
- Tokenizers 0.12.1
|
jonfrank/xlm-roberta-base-finetuned-panx-de
|
jonfrank
| 2022-05-04T10:13:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-05-04T09:39:55Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8654425558524246
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1334
- F1: 0.8654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2541 | 1.0 | 525 | 0.1596 | 0.8242 |
| 0.1284 | 2.0 | 1050 | 0.1360 | 0.8499 |
| 0.0827 | 3.0 | 1575 | 0.1334 | 0.8654 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Nijana/gpt-neo-1.3B-climate_change_tweets
|
Nijana
| 2022-05-04T10:12:52Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-05-02T11:35:45Z |
# A fine-tuned GPT-Neo Model for Tweet Generation
This model is a fine-tuned version of the 1.3B-parameter GPT-Neo model developed by EleutherAI. As the default GPT-Neo model did not receive any social media data during its pre-training, we fine-tuned it with tweets collected from Twitter from October to November 2021 related to climate change hashtags. The model received data in the format `<username> - <tweet>` We used an 80/20 train/test split, and to differentiate distinct tweets, we added a start-of-tweet and an end-of-tweet token to the training dataset.
To guide you in using this model, please consult the `gpt_neo_1.3B_twitter.ipynb` Jupyter Notebook file from this repository.
---
license: cc-by-3.0
---
|
nbhimte/tiny-bert-mnli-distilled
|
nbhimte
| 2022-05-04T07:14:17Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-17T03:40:10Z |
---
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: tiny-bert-mnli-distilled
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.5818644931227712
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# tiny-bert-mnli-distilled
It achieves the following results on the evaluation set:
- Loss: 1.5018
- Accuracy: 0.5819
- F1 score: 0.5782
- Precision score: 0.6036
- Metric recall: 0.5819
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 64
- eval_batch_size: 32
- seed: 33
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 score | Precision score | Metric recall |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:---------------:|:-------------:|
| 1.4475 | 1.0 | 614 | 1.4296 | 0.4521 | 0.4070 | 0.5621 | 0.4521 |
| 1.3354 | 2.0 | 1228 | 1.4320 | 0.4805 | 0.4579 | 0.5276 | 0.4805 |
| 1.2244 | 3.0 | 1842 | 1.4786 | 0.5699 | 0.5602 | 0.5865 | 0.5699 |
| 1.1416 | 4.0 | 2456 | 1.5018 | 0.5819 | 0.5782 | 0.6036 | 0.5819 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.9.1
- Datasets 2.1.0
- Tokenizers 0.11.6
|
ybkim95/lp-bert-model
|
ybkim95
| 2022-05-04T06:26:12Z | 1 | 1 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-05-04T06:26:02Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# ybkim95/lp-bert-model
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('ybkim95/lp-bert-model')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('ybkim95/lp-bert-model')
model = AutoModel.from_pretrained('ybkim95/lp-bert-model')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=ybkim95/lp-bert-model)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 46 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
snunlp/KR-ELECTRA-discriminator
|
snunlp
| 2022-05-04T06:22:51Z | 150 | 11 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"pretraining",
"ko",
"endpoints_compatible",
"region:us"
] | null | 2022-03-02T23:29:05Z |
---
language:
- "ko"
---
## KoRean based ELECTRA (KR-ELECTRA)
This is a release of a Korean-specific ELECTRA model with comparable or better performances developed by the Computational Linguistics Lab at Seoul National University. Our model shows remarkable performances on tasks related to informal texts such as review documents, while still showing comparable results on other kinds of tasks.
### Released Model
We pre-trained our KR-ELECTRA model following a base-scale model of [ELECTRA](https://github.com/google-research/electra). We trained the model based on Tensorflow-v1 using a v3-8 TPU of Google Cloud Platform.
#### Model Details
We followed the training parameters of the base-scale model of [ELECTRA](https://github.com/google-research/electra).
##### Hyperparameters
| model | # of layers | embedding size | hidden size | # of heads |
| ------: | ----------: | -------------: | ----------: | ---------: |
| Discriminator | 12 | 768 | 768 | 12 |
| Generator | 12 | 768 | 256 | 4 |
##### Pretraining
| batch size | train steps | learning rates | max sequence length | generator size |
| ---------: | ----------: | -------------: | ------------------: | -------------: |
| 256 | 700000 | 2e-4 | 128 | 0.33333 |
#### Training Dataset
34GB Korean texts including Wikipedia documents, news articles, legal texts, news comments, product reviews, and so on. These texts are balanced, consisting of the same ratios of written and spoken data.
#### Vocabulary
vocab size 30,000
We used morpheme-based unit tokens for our vocabulary based on the [Mecab-Ko](https://bitbucket.org/eunjeon/mecab-ko-dic/src/master/) morpheme analyzer.
#### Download Link
* Tensorflow-v1 model ([download](https://drive.google.com/file/d/1L_yKEDaXM_yDLwHm5QrXAncQZiMN3BBU/view?usp=sharing))
* PyTorch models on HuggingFace
```python
from transformers import ElectraModel, ElectraTokenizer
model = ElectraModel.from_pretrained("snunlp/KR-ELECTRA-discriminator")
tokenizer = ElectraTokenizer.from_pretrained("snunlp/KR-ELECTRA-discriminator")
```
### Finetuning
We used and slightly edited the finetuning codes from [KoELECTRA](https://github.com/monologg/KoELECTRA), with additionally adjusted hyperparameters. You can download the codes and config files that we used for our model from our [github](https://github.com/snunlp/KR-ELECTRA).
#### Experimental Results
| | **NSMC**<br/>(acc) | **Naver NER**<br/>(F1) | **PAWS**<br/>(acc) | **KorNLI**<br/>(acc) | **KorSTS**<br/>(spearman) | **Question Pair**<br/>(acc) | **KorQuaD (Dev)**<br/>(EM/F1) | **Korean-Hate-Speech (Dev)**<br/>(F1) |
| :-------------------- | :----------------: | :--------------------: | :----------------: | :------------------: | :-----------------------: | :-------------------------: | :---------------------------: | :-----------------------------------: |
| KoBERT | 89.59 | 87.92 | 81.25 | 79.62 | 81.59 | 94.85 | 51.75 / 79.15 | 66.21 |
| XLM-Roberta-Base | 89.03 | 86.65 | 82.80 | 80.23 | 78.45 | 93.80 | 64.70 / 88.94 | 64.06 |
| HanBERT | 90.06 | 87.70 | 82.95 | 80.32 | 82.73 | 94.72 | 78.74 / 92.02 | 68.32 |
| KoELECTRA-Base | 90.33 | 87.18 | 81.70 | 80.64 | 82.00 | 93.54 | 60.86 / 89.28 | 66.09 |
| KoELECTRA-Base-v2 | 89.56 | 87.16 | 80.70 | 80.72 | 82.30 | 94.85 | 84.01 / 92.40 | 67.45 |
| KoELECTRA-Base-v3 | 90.63 | **88.11** | **84.45** | 82.24 | **85.53** | 95.25 | 84.83 / **93.45** | 67.61 |
| **KR-ELECTRA (ours)** | **91.168** | 87.90 | 82.05 | **82.51** | 85.41 | **95.51** | **84.93** / 93.04 | **74.50** |
The baseline results are brought from [KoELECTRA](https://github.com/monologg/KoELECTRA)'s.
### Citation
```bibtex
@misc{kr-electra,
author = {Lee, Sangah and Hyopil Shin},
title = {KR-ELECTRA: a KoRean-based ELECTRA model},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/snunlp/KR-ELECTRA}}
}
```
|
LiYouYou/bert_finetuning_cn
|
LiYouYou
| 2022-05-04T05:36:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"en",
"dataset:glue",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-04T05:21:07Z |
---
language:
- en
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: bert_finetuning_cn
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE SST2
type: glue
args: sst2
metrics:
- name: Accuracy
type: accuracy
value: 0.8314220183486238
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_finetuning_cn
This model is a fine-tuned version of [bert-base-chinese](https://huggingface.co/bert-base-chinese) on the GLUE SST2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5440
- Accuracy: 0.8314
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
abhi1nandy2/EManuals_RoBERTa
|
abhi1nandy2
| 2022-05-04T04:57:53Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"feature-extraction",
"EManuals",
"customer support",
"QA",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
feature-extraction
| 2022-03-02T23:29:05Z |
---
language:
- English
tags:
- EManuals
- customer support
- QA
- roberta
---
Refer to https://aclanthology.org/2021.findings-emnlp.392/ for the paper and https://sites.google.com/view/emanualqa/home for the project website
## Citation
Please cite the work if you would like to use it.
```
@inproceedings{nandy-etal-2021-question-answering,
title = "Question Answering over Electronic Devices: A New Benchmark Dataset and a Multi-Task Learning based {QA} Framework",
author = "Nandy, Abhilash and
Sharma, Soumya and
Maddhashiya, Shubham and
Sachdeva, Kapil and
Goyal, Pawan and
Ganguly, NIloy",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2021",
month = nov,
year = "2021",
address = "Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.findings-emnlp.392",
doi = "10.18653/v1/2021.findings-emnlp.392",
pages = "4600--4609",
abstract = "Answering questions asked from instructional corpora such as E-manuals, recipe books, etc., has been far less studied than open-domain factoid context-based question answering. This can be primarily attributed to the absence of standard benchmark datasets. In this paper, we meticulously create a large amount of data connected with E-manuals and develop a suitable algorithm to exploit it. We collect E-Manual Corpus, a huge corpus of 307,957 E-manuals, and pretrain RoBERTa on this large corpus. We create various benchmark QA datasets which include question answer pairs curated by experts based upon two E-manuals, real user questions from Community Question Answering Forum pertaining to E-manuals etc. We introduce EMQAP (E-Manual Question Answering Pipeline) that answers questions pertaining to electronics devices. Built upon the pretrained RoBERTa, it harbors a supervised multi-task learning framework which efficiently performs the dual tasks of identifying the section in the E-manual where the answer can be found and the exact answer span within that section. For E-Manual annotated question-answer pairs, we show an improvement of about 40{\%} in ROUGE-L F1 scores over most competitive baseline. We perform a detailed ablation study and establish the versatility of EMQAP across different circumstances. The code and datasets are shared at https://github.com/abhi1nandy2/EMNLP-2021-Findings, and the corresponding project website is https://sites.google.com/view/emanualqa/home.",
}
```
|
clevo570/Nissan_Project
|
clevo570
| 2022-05-03T21:54:07Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-26T04:47:11Z |
# Nissan Project
---
license: mit
---
## Overview
This model is based on [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) model and [roberta-base-squad2 ](https://huggingface.co/deepset/roberta-base-squad2) model. Bart-large-mnli model is a zero-shot pre-trained model so we don't need to train the model. We just input comments and features we want to classify. Roberta-base-squad2 is a Question Answering model, which helps us to filter which comment mentions the feature.
## Text-image matching
### Model Input
```python
import pandas as pd
from transformers import pipeline
QA_input = {
'question': 'Does it mention dependable?',
'context': input("Enter your sentence:")
}
```
### Model Process
```python
from transformers import AutoModelForQuestionAnswering, AutoTokenizer, pipeline
model_name = "deepset/roberta-base-squad2"
# a) Get predictions
nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
res = nlp(QA_input)
if res['score'] > 0.1:
sentence = QA_input['context']
classifier = pipeline("zero-shot-classification",
model="facebook/bart-large-mnli", device=0)
sequence_to_classify = sentence
candidate_labels = ['dependable', 'not dependable']
res_2 = classifier(sequence_to_classify, candidate_labels, multi_label=False)
score = res_2.get('scores')[0]*2-1
else:
score = 0
print(score)
```
## Result
If the score is zero, it means it doesn't mention the feature. Others, it gets the score of the comment with the feature we select.
### Demo code (Python Notebook)
https://github.com/vanderbilt-data-science/nissan/blob/main/30-ModelFilter/question-answering.ipynb
https://github.com/vanderbilt-data-science/nissan/blob/main/31-ModelWalkthrough/label_after_filtering.ipynb
|
espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3
|
espnet
| 2022-05-03T21:48:45Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"en",
"dataset:chime6",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-03T20:52:40Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: en
datasets:
- chime6
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3`
This model was trained by simpleoier using chime6 recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout b757b89d45d5574cebf44e225cbe32e3e9e4f522
pip install -e .
cd egs2/chime6/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/simpleoier_chime6_asr_transformer_wavlm_lr1e-3
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Tue May 3 16:47:10 EDT 2022`
- python version: `3.9.12 (main, Apr 5 2022, 06:56:58) [GCC 7.5.0]`
- espnet version: `espnet 202204`
- pytorch version: `pytorch 1.10.1`
- Git hash: `b757b89d45d5574cebf44e225cbe32e3e9e4f522`
- Commit date: `Mon May 2 09:21:08 2022 -0400`
## asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_bpe1000_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|58881|66.5|21.3|12.2|8.8|42.3|77.4|
|decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|58881|68.6|20.7|10.6|8.4|39.8|77.5|
|decode_asr_transformer_asr_model_3epoch/dev_gss_multiarray|7437|58881|67.5|20.3|12.2|8.0|40.5|76.5|
|decode_asr_transformer_asr_model_5epoch/dev_gss_multiarray|7437|58881|67.7|21.4|10.9|8.6|40.9|77.9|
|decode_asr_transformer_asr_model_7epoch/dev_gss_multiarray|7437|58881|66.6|20.9|12.5|8.2|41.6|77.8|
|decode_asr_transformer_asr_model_valid.acc.ave/dev_gss_multiarray|0|0|0.0|0.0|0.0|0.0|0.0|0.0|
|decode_asr_transformer_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|58881|69.4|20.2|10.4|8.6|39.1|75.8|
|decode_asr_transformer_lw0.5_lm_lm_train_lm_en_bpe1000_valid.loss.ave_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|58881|65.7|20.2|14.1|7.5|41.8|77.8|
|decode_asr_transformer_lw0.5_ngram_ngram_3gram_asr_model_valid.acc.ave/dev_gss_multiarray|7437|58881|65.7|19.0|15.3|6.2|40.6|78.8|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|280767|78.1|7.7|14.1|9.1|31.0|77.9|
|decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|280767|80.0|7.6|12.5|8.7|28.8|78.1|
|decode_asr_transformer_asr_model_3epoch/dev_gss_multiarray|7437|280767|78.6|7.3|14.1|8.1|29.5|77.5|
|decode_asr_transformer_asr_model_5epoch/dev_gss_multiarray|7437|280767|79.5|7.7|12.8|9.1|29.6|78.8|
|decode_asr_transformer_asr_model_7epoch/dev_gss_multiarray|7437|280767|77.9|7.6|14.5|8.3|30.3|78.6|
|decode_asr_transformer_asr_model_valid.acc.ave/dev_gss_multiarray|0|0|0.0|0.0|0.0|0.0|0.0|0.0|
|decode_asr_transformer_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|280767|80.6|7.4|12.0|8.9|28.3|76.6|
|decode_asr_transformer_lw0.5_lm_lm_train_lm_en_bpe1000_valid.loss.ave_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|280767|76.5|7.4|16.1|7.7|31.2|78.5|
|decode_asr_transformer_lw0.5_ngram_ngram_3gram_asr_model_valid.acc.ave/dev_gss_multiarray|7437|280767|77.0|7.6|15.4|7.2|30.2|79.8|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_asr_transformer_asr_model_1epoch/dev_gss_multiarray|7437|92680|65.8|18.8|15.4|8.7|42.9|78.0|
|decode_asr_transformer_asr_model_2epoch/dev_gss_multiarray|7437|92680|67.9|18.1|13.9|8.2|40.3|78.2|
|decode_asr_transformer_asr_model_3epoch/dev_gss_multiarray|7437|92680|66.9|17.8|15.2|8.0|41.1|77.7|
|decode_asr_transformer_asr_model_5epoch/dev_gss_multiarray|7437|92680|67.2|18.5|14.3|8.2|40.9|78.9|
|decode_asr_transformer_asr_model_7epoch/dev_gss_multiarray|7437|92680|66.1|18.2|15.7|7.8|41.7|78.6|
|decode_asr_transformer_asr_model_valid.acc.ave/dev_gss_multiarray|0|0|0.0|0.0|0.0|0.0|0.0|0.0|
|decode_asr_transformer_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|92680|68.9|17.7|13.4|8.2|39.3|76.6|
|decode_asr_transformer_lw0.5_lm_lm_train_lm_en_bpe1000_valid.loss.ave_asr_model_valid.acc.ave_5best/dev_gss_multiarray|7437|92680|66.1|19.1|14.8|10.2|44.1|78.6|
|decode_asr_transformer_lw0.5_ngram_ngram_3gram_asr_model_valid.acc.ave/dev_gss_multiarray|7437|92680|66.0|19.9|14.1|9.5|43.6|79.8|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_transformer_wavlm_lr1e-3_specaug_accum1_preenc128_warmup20k_raw_en_bpe1000_sp
ngpu: 0
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: null
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: true
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 8
patience: null
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - valid
- acc
- max
keep_nbest_models: 5
nbest_averaging_interval: 0
grad_clip: 5
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param:
- frontend.upstream
num_iters_per_epoch: null
batch_size: 48
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_en_bpe1000_sp/train/speech_shape
- exp/asr_stats_raw_en_bpe1000_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_en_bpe1000_sp/valid/speech_shape
- exp/asr_stats_raw_en_bpe1000_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_worn_simu_u400k_cleaned_sp/wav.scp
- speech
- kaldi_ark
- - dump/raw/train_worn_simu_u400k_cleaned_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_gss_multiarray/wav.scp
- speech
- kaldi_ark
- - dump/raw/dev_gss_multiarray/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adam
optim_conf:
lr: 0.001
scheduler: warmuplr
scheduler_conf:
warmup_steps: 20000
token_list:
- <blank>
- <unk>
- '[inaudible]'
- '[laughs]'
- '[noise]'
- ▁
- s
- ''''
- ▁i
- ▁it
- t
- ▁you
- ▁the
- ▁yeah
- ▁a
- ▁like
- ▁that
- ▁and
- ▁to
- m
- ▁oh
- ▁so
- '-'
- e
- re
- a
- ▁just
- ▁no
- d
- ▁we
- n
- ▁in
- ing
- i
- ▁of
- ▁do
- ▁is
- ▁have
- ▁what
- ▁was
- ▁this
- ▁can
- o
- ▁one
- r
- ▁but
- er
- y
- ▁they
- ed
- ▁uh
- ▁for
- ▁okay
- ▁there
- ▁be
- ▁he
- ▁don
- g
- ll
- ▁right
- p
- ▁not
- u
- ▁on
- c
- ▁then
- ▁know
- ▁my
- ▁or
- ▁get
- ▁are
- ▁all
- ▁um
- ▁me
- ▁if
- ▁go
- ▁good
- ▁with
- ▁really
- b
- ▁gonna
- ▁think
- ▁cuz
- in
- ▁your
- k
- ve
- le
- w
- an
- ▁she
- l
- ▁well
- en
- f
- ▁up
- al
- ▁two
- h
- ar
- ▁how
- ▁mhm
- v
- ▁here
- ly
- ▁put
- ▁out
- ▁would
- ▁at
- ▁need
- ▁did
- ▁f
- ▁want
- ▁mm
- ▁more
- ch
- ri
- ▁now
- or
- ▁when
- ▁k
- ▁p
- ▁see
- ▁got
- ▁too
- ▁thing
- ▁time
- 'on'
- ▁actually
- ▁where
- ne
- ▁guys
- ▁some
- ▁had
- ▁why
- ic
- ▁them
- ▁st
- ro
- ▁make
- ur
- ▁three
- ▁b
- ▁mean
- ▁wanna
- ▁should
- at
- ▁from
- th
- ▁didn
- ▁about
- ▁yes
- ▁because
- ▁yep
- ▁people
- ▁co
- ▁could
- ▁were
- ▁take
- ▁has
- ▁something
- ce
- ▁w
- ▁c
- ▁sure
- ▁who
- ▁other
- ▁sh
- ▁say
- ▁an
- ▁her
- ▁g
- ▁work
- il
- es
- ▁little
- el
- ▁much
- ▁eat
- ▁still
- ▁wait
- ▁ma
- ▁four
- ▁de
- ▁only
- ▁down
- ▁though
- ▁way
- ▁lot
- ▁use
- ▁over
- ▁let
- ▁pretty
- ▁these
- ▁bo
- ▁any
- ▁off
- ▁ba
- ▁di
- ▁d
- ▁back
- ▁sorry
- ▁those
- ▁very
- ▁bit
- ▁even
- li
- ▁stuff
- ke
- ate
- z
- ▁probably
- ▁nice
- ▁turn
- ▁doesn
- ▁first
- ▁does
- ▁hmm
- ▁look
- ▁going
- ▁play
- ▁ho
- pe
- ▁maybe
- ▁come
- ▁fine
- ▁cut
- ▁man
- ▁bu
- ▁ca
- ▁mo
- ▁th
- lo
- ▁never
- ry
- ▁po
- ▁h
- ▁will
- us
- x
- ge
- ▁five
- ▁start
- ▁him
- ▁long
- ▁give
- ▁se
- ting
- ▁sp
- ▁ra
- ▁done
- ▁con
- ▁big
- ▁his
- ▁y
- ▁which
- ▁been
- ▁dunno
- est
- ion
- ▁fa
- ▁than
- me
- ▁our
- ▁also
- ▁six
- ▁kinda
- co
- ▁cool
- ty
- ▁game
- ▁thought
- ▁fi
- ▁after
- ▁day
- ▁doing
- ment
- ▁said
- ▁whatever
- ap
- ▁place
- ▁anything
- ▁j
- ▁guess
- em
- ▁always
- ▁things
- ▁card
- ▁li
- ▁thank
- ▁last
- ▁before
- ▁many
- ▁watch
- ▁pa
- ▁year
- ▁ah
- ▁hot
- ▁into
- ▁ten
- ▁keep
- ▁bad
- tion
- ▁us
- ▁cr
- ▁part
- ▁cook
- ▁o
- ▁cards
- ▁everything
- ▁la
- ▁ha
- ▁by
- ▁wow
- ▁their
- ies
- ▁hey
- ▁same
- ▁went
- ▁pick
- ▁might
- ▁sc
- ▁ex
- ie
- ▁wood
- ight
- ▁another
- ▁better
- ▁try
- ard
- ▁seven
- ▁guy
- ▁point
- up
- op
- ▁twenty
- ▁hand
- ▁wh
- ▁food
- ▁tra
- ation
- ▁buy
- ▁kind
- ist
- ▁whole
- ive
- is
- ▁half
- able
- ▁pro
- ▁win
- ▁different
- ▁cl
- age
- ▁already
- ▁gotta
- ack
- ▁ti
- ▁lo
- ▁every
- ▁super
- ▁again
- ▁new
- ▁remember
- ers
- ▁dude
- um
- ▁feel
- ▁roll
- ▁cheese
- ▁na
- ▁sit
- ▁sa
- way
- ▁hard
- ▁enough
- 'no'
- ▁eight
- ity
- ▁friend
- ▁un
- ul
- ▁love
- ▁salt
- ▁mi
- ▁steak
- ▁nine
- ▁else
- ▁looks
- ▁pu
- ▁fl
- ▁build
- ▁pre
- ▁end
- ▁ta
- ▁salad
- ▁high
- ▁find
- ▁water
- ▁usually
- ▁small
- ▁around
- ▁butter
- ▁car
- ▁made
- ▁wash
- ▁move
- ▁plate
- ▁true
- ▁pan
- ain
- cu
- ▁nope
- ▁ooh
- ▁sauce
- ▁help
- ▁wa
- ▁left
- ▁person
- uck
- ▁top
- ▁side
- ▁cha
- ▁god
- ▁leave
- ▁goes
- ▁weird
- ▁each
- ▁r
- ▁basically
- ▁chicken
- ted
- ▁oil
- ▁trying
- ▁fun
- ▁close
- ▁taste
- ▁old
- ▁show
- ble
- ▁next
- ▁name
- ▁used
- ▁mine
- ous
- ▁great
- ▁pot
- ally
- ▁burn
- ▁huh
- ▁minutes
- ▁once
- ▁phone
- ▁bowl
- tic
- ▁tell
- ound
- ▁ask
- ▁mu
- ▁thirty
- ▁someone
- ▁piece
- ▁saying
- ▁vi
- ish
- ▁ja
- ▁comp
- ▁called
- ▁through
- ▁gr
- ize
- ▁everyone
- ▁funny
- ▁getting
- ▁won
- ▁bl
- ▁away
- ▁pi
- ▁chi
- ▁totally
- ▁red
- ▁word
- ▁hundred
- ▁open
- ▁dollar
- ▁stone
- ▁yet
- ade
- ▁du
- ▁mmm
- ▁sound
- ▁both
- ▁mar
- ant
- ▁potatoes
- ▁garlic
- fi
- ▁hear
- ▁pass
- ▁saw
- ▁kill
- ▁second
- ▁girl
- ▁shit
- ▁throw
- ▁bought
- ▁please
- ▁che
- ▁da
- ▁hit
- ▁tea
- ▁hold
- ▁shoot
- ▁most
- ▁clean
- ▁wanted
- ▁pepper
- ▁happen
- ▁aw
- ▁home
- ▁drink
- ance
- ▁yo
- ▁sheep
- ▁while
- ▁ro
- ▁house
- ▁call
- ▁meat
- ▁face
- ▁fuck
- ▁talking
- ▁green
- ries
- side
- ▁set
- ▁exactly
- huh
- ▁hour
- ▁ready
- ▁played
- ▁finish
- ▁add
- ▁susie
- q
- ▁stop
- ▁almost
- ▁bring
- ▁rice
- ▁ear
- ▁sweet
- ▁hi
- ▁pizza
- ake
- ▁wi
- ▁gra
- ▁free
- ▁night
- ▁pay
- ▁rick
- ▁full
- ▁wheat
- ▁count
- ▁white
- ful
- ▁light
- ▁plan
- ▁supposed
- ▁either
- ▁bacon
- ▁sim
- ▁sense
- ▁blue
- ▁team
- ▁interesting
- ▁care
- ▁room
- nut
- ward
- ▁real
- ▁week
- ▁heard
- ▁told
- ▁mind
- ▁table
- ▁head
- ash
- ▁looking
- ▁ever
- ▁check
- ▁together
- ▁ju
- ▁app
- ▁grab
- ▁brown
- ▁eh
- book
- ▁stick
- ▁later
- ▁pea
- ▁talk
- ▁awesome
- ▁cream
- ling
- ▁fifty
- ▁color
- ▁qu
- ▁round
- ▁nothing
- ▁power
- ▁deal
- ▁matter
- ▁player
- ▁draw
- ▁having
- ▁kid
- ▁fish
- ▁damn
- ▁own
- ▁crazy
- ▁dad
- ▁took
- ▁perfect
- ▁idea
- ▁couple
- ▁live
- ▁job
- ▁smell
- ▁number
- ▁reason
- ▁best
- ▁forty
- ▁making
- ▁dinner
- ▁change
- ▁playing
- ▁sometimes
- ▁fridge
- ▁miss
- j
- ▁woah
- ▁chancey
- ▁bucks
- ▁brick
- ▁rec
- ▁run
- ▁far
- ball
- ▁bread
- ▁fast
- ▁knife
- ▁black
- ▁break
- ▁mix
- ▁today
- ▁cheap
- ▁mike
- ▁expensive
- out
- ▁normal
- ▁under
- ▁using
- ▁double
- ▁gold
- ▁life
- ▁oven
- ▁less
- ▁space
- ▁wine
- ence
- land
- ▁sea
- ▁corn
- ▁cooking
- ▁stay
- ▁line
- ▁may
- ▁bar
- ▁block
- ▁late
- ▁yourself
- ▁quite
- ▁apple
- ▁extra
- ▁wedding
- ▁happened
- ▁kitchen
- ▁coming
- ▁zero
- ▁definitely
- ▁connect
- ▁read
- ▁crab
- ▁easier
- ▁mkay
- ▁egg
- ▁came
- ▁money
- ▁anyone
- ▁save
- ▁problem
- ▁club
- ▁tried
- ▁wrong
- ▁spot
- ▁low
- ▁amazing
- ▁milk
- ▁jeff
- ▁flip
- ▁text
- ▁bottle
- jo
- ▁without
- ▁parents
- ▁anymore
- ▁course
- ship
- ▁month
- ▁chinese
- ▁must
- ▁movie
- ▁wonder
- ▁bunch
- ▁family
- ▁season
- ▁quick
- ▁past
- ▁paul
- ▁rid
- ▁tennis
- town
- ▁cold
- ▁serious
- ▁drive
- ▁boil
- ▁screw
- ▁least
- ▁everybody
- ▁sort
- ▁thomas
- ▁rest
- ▁suck
- ▁road
- ▁fair
- ▁forgot
- ▁order
- ▁middle
- ▁babe
- ▁bang
- ▁dress
- ▁sleep
- ▁question
- ▁until
- ▁sheriff
- ▁chop
- ▁restaurant
- ▁outside
- ▁learn
- ▁stand
- ▁walk
- ▁attack
- ▁trade
- ▁phil
- ▁few
- ▁strong
- ▁school
- ▁world
- ▁company
- ▁easy
- ▁hockey
- ▁somebody
- ▁short
- ▁figure
- ▁spice
- ▁apparently
- ▁since
- ▁serve
- ▁huge
- ▁saboteur
- ▁fifteen
- ▁myself
- ▁such
- ▁port
- ▁literally
- ▁lose
- ▁crap
- ught
- ▁gosh
- ▁unless
- ▁joke
- ▁store
- ▁bigger
- ▁spell
- ▁ago
- ▁hang
- ▁depend
- ▁ginger
- ▁slow
- ▁medium
- ▁record
- acti
- ▁kenny
- ▁picture
- old
- ▁thousand
- ▁cover
- ▁tree
- ▁obvious
- ▁glass
- ▁taking
- ▁letter
- ▁eleven
- ▁skin
- ▁market
- ▁anybody
- ▁ahead
- ▁morning
- ▁brand
- ▁paper
- ▁lemon
- ▁onions
- ▁juice
- ▁jimmy
- ▁living
- ▁front
- ▁bottom
- ▁dark
- ▁oops
- ▁arjan
- ▁shot
- ▁rule
- ▁hun
- ▁flavor
- ▁speak
- ▁gun
- ▁potato
- ▁worry
- ▁twelve
- ▁sandwich
- ▁plus
- ▁believe
- ▁knew
- ▁realize
- ▁sugar
- ▁happy
- ▁sister
- ▁entire
- ▁master
- ▁eye
- ▁touch
- ▁wenny
- ▁drop
- ▁price
- ▁slice
- ▁sword
- ▁spicy
- ▁listen
- ▁outlaw
- que
- ▁percent
- ▁yesterday
- ▁mushroom
- ▁worth
- ▁proper
- ▁story
- ▁megan
- ▁character
- ▁hair
- ▁straight
- ▁discard
- ▁spoon
- ▁understand
- ▁computer
- ▁type
- ▁nikki
- ▁tomorrow
- ▁trump
- ▁third
- ▁bennet
- ▁nobody
- ▁somewhere
- ▁amount
- ▁split
- ▁accent
- ▁group
- ▁trip
- ▁lunch
- ▁racket
- ▁level
- ▁difference
- ▁orange
- ▁gave
- ▁dessert
- ▁single
- ▁chocolate
- ▁junette
- ▁camera
- ▁regular
- ▁video
- ▁gross
- ▁notice
- ▁actual
- ▁between
- ▁surprise
- ▁smart
- ▁east
- ▁craft
- ▁rock
- ▁certain
- ▁rather
- ▁lobster
- ▁photo
- ▁favorite
- ▁behind
- ▁across
- ▁steal
- ▁spend
- ▁weekend
- ▁special
- ▁sign
- ▁wrap
- ▁except
- ▁john
- ▁conversation
- ▁asian
- ▁grand
- ▁online
- ▁explain
- ▁dishes
- ▁magic
- ▁decide
- ▁fancy
- ▁random
- ▁tunnel
- ▁switch
- ▁transcribe
- ▁english
- ▁giant
- ▁kick
- ▁claire
- ▁laugh
- ▁yellow
- ▁delicious
- ▁freeze
- ▁drunk
- ▁general
- ▁gimme
- ▁damage
- ▁breakfast
- ▁roast
- ▁josh
- ▁choose
- ▁email
- ▁direct
- ▁tomatoes
- ▁fruit
- ▁apart
- ▁chopstick
- ▁vancouver
- ▁kept
- tract
- ▁chunk
- ▁girlfriend
- ▁shuffle
- ▁terrible
- ▁diamond
- ▁sausage
- ▁sweat
- ▁iphone
- ▁pineapple
- ▁summer
- ▁french
- ▁fresh
- ▁heavy
- ▁million
- ▁instead
- ▁ridiculous
- ▁tough
- ▁friday
- ▁whenever
- ▁coffee
- ▁hilarious
- ▁worried
- ▁especially
- ▁shrimp
- ▁avocado
- '&'
- ä
- '#'
- ǎ
- î
- ü
- ǐ
- ñ
- â
- ç
- ']'
- é
- <sos/eos>
init: xavier_uniform
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
use_preprocessor: true
token_type: bpe
bpemodel: data/en_token_list/bpe_unigram1000/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: s3prl
frontend_conf:
frontend_conf:
upstream: wavlm_large
download_dir: ./hub
multilayer_feature: true
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 100
num_freq_mask: 4
apply_time_mask: true
time_mask_width_range:
- 0
- 40
num_time_mask: 2
normalize: utterance_mvn
normalize_conf: {}
model: espnet
model_conf:
ctc_weight: 0.3
lsm_weight: 0.1
length_normalized_loss: false
extract_feats_in_collect_stats: false
preencoder: linear
preencoder_conf:
input_size: 1024
output_size: 128
encoder: transformer
encoder_conf:
output_size: 256
attention_heads: 4
linear_units: 2048
num_blocks: 12
dropout_rate: 0.1
attention_dropout_rate: 0.0
input_layer: conv2d2
normalize_before: true
postencoder: null
postencoder_conf: {}
decoder: transformer
decoder_conf:
input_layer: embed
attention_heads: 4
linear_units: 2048
num_blocks: 6
dropout_rate: 0.1
positional_dropout_rate: 0.0
self_attention_dropout_rate: 0.0
src_attention_dropout_rate: 0.0
required:
- output_dir
- token_list
version: '202204'
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
theojolliffe/bart-large-cnn-finetuned-roundup-32
|
theojolliffe
| 2022-05-03T21:24:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T19:23:27Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-32
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-32
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2324
- Rouge1: 46.462
- Rouge2: 25.9506
- Rougel: 29.4584
- Rougelsum: 44.1863
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 32
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 132 | 1.3139 | 48.8247 | 29.2173 | 31.7628 | 45.8992 | 142.0 |
| No log | 2.0 | 264 | 1.2287 | 47.9398 | 29.4061 | 30.9133 | 44.9142 | 140.9 |
| No log | 3.0 | 396 | 1.2676 | 49.2743 | 30.4469 | 32.8893 | 46.6208 | 142.0 |
| 0.9578 | 4.0 | 528 | 1.3218 | 47.315 | 26.7303 | 30.5007 | 44.7654 | 142.0 |
| 0.9578 | 5.0 | 660 | 1.3173 | 47.1476 | 25.9408 | 29.4257 | 44.4956 | 142.0 |
| 0.9578 | 6.0 | 792 | 1.4283 | 47.5836 | 27.1572 | 29.8553 | 44.8858 | 142.0 |
| 0.9578 | 7.0 | 924 | 1.5005 | 46.6839 | 26.2214 | 30.1895 | 43.8753 | 140.75 |
| 0.3306 | 8.0 | 1056 | 1.5316 | 47.7611 | 27.1105 | 30.8142 | 44.7598 | 142.0 |
| 0.3306 | 9.0 | 1188 | 1.6295 | 48.4416 | 27.6912 | 30.3409 | 45.317 | 142.0 |
| 0.3306 | 10.0 | 1320 | 1.6564 | 46.5751 | 27.2306 | 29.7265 | 43.7327 | 142.0 |
| 0.3306 | 11.0 | 1452 | 1.7471 | 47.9684 | 27.5739 | 30.7018 | 44.6852 | 141.75 |
| 0.145 | 12.0 | 1584 | 1.7700 | 47.9274 | 28.5129 | 31.129 | 45.1009 | 142.0 |
| 0.145 | 13.0 | 1716 | 1.8391 | 49.8091 | 30.1597 | 33.6004 | 47.2007 | 141.95 |
| 0.145 | 14.0 | 1848 | 1.9212 | 45.2195 | 25.033 | 27.4181 | 42.6161 | 142.0 |
| 0.145 | 15.0 | 1980 | 1.9267 | 48.4959 | 28.1 | 31.2796 | 46.2758 | 142.0 |
| 0.0723 | 16.0 | 2112 | 1.9130 | 47.0765 | 27.4929 | 30.6862 | 44.1458 | 142.0 |
| 0.0723 | 17.0 | 2244 | 1.9514 | 48.5354 | 28.4909 | 31.8966 | 45.7116 | 142.0 |
| 0.0723 | 18.0 | 2376 | 2.0064 | 47.9339 | 28.6862 | 32.4472 | 45.3704 | 142.0 |
| 0.042 | 19.0 | 2508 | 2.0210 | 48.3169 | 28.1579 | 30.2681 | 45.3831 | 141.3 |
| 0.042 | 20.0 | 2640 | 2.0377 | 46.8156 | 26.0122 | 28.817 | 43.9383 | 142.0 |
| 0.042 | 21.0 | 2772 | 2.0587 | 46.3813 | 27.3555 | 29.875 | 43.6605 | 142.0 |
| 0.042 | 22.0 | 2904 | 2.0695 | 45.6728 | 26.0639 | 29.5653 | 42.3772 | 142.0 |
| 0.025 | 23.0 | 3036 | 2.1617 | 46.7283 | 26.2082 | 28.52 | 43.3304 | 142.0 |
| 0.025 | 24.0 | 3168 | 2.1375 | 48.1347 | 28.3444 | 31.7509 | 45.4907 | 142.0 |
| 0.025 | 25.0 | 3300 | 2.1911 | 47.3358 | 27.1479 | 29.4923 | 44.0087 | 142.0 |
| 0.025 | 26.0 | 3432 | 2.1806 | 47.2218 | 26.8421 | 30.03 | 44.2417 | 142.0 |
| 0.0153 | 27.0 | 3564 | 2.1890 | 46.3745 | 27.0095 | 29.7274 | 43.3372 | 142.0 |
| 0.0153 | 28.0 | 3696 | 2.2235 | 50.1274 | 30.8817 | 32.8766 | 46.7486 | 141.5 |
| 0.0153 | 29.0 | 3828 | 2.2236 | 50.1785 | 30.8079 | 32.8886 | 46.9888 | 142.0 |
| 0.0153 | 30.0 | 3960 | 2.2312 | 46.7468 | 26.4272 | 30.1175 | 43.9132 | 142.0 |
| 0.0096 | 31.0 | 4092 | 2.2287 | 47.558 | 26.3933 | 29.9122 | 44.5752 | 142.0 |
| 0.0096 | 32.0 | 4224 | 2.2324 | 46.462 | 25.9506 | 29.4584 | 44.1863 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
ShreyaR/finetuned-distil-bert-depression
|
ShreyaR
| 2022-05-03T20:44:08Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-16T13:54:51Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuned-distil-bert-depression
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuned-distil-bert-depression
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1695
- Accuracy: 0.9445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0243 | 1.0 | 625 | 0.2303 | 0.9205 |
| 0.0341 | 2.0 | 1250 | 0.1541 | 0.933 |
| 0.0244 | 3.0 | 1875 | 0.1495 | 0.9445 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
SebastianS/distilbert-base-uncased-finetuned-imdb
|
SebastianS
| 2022-05-03T20:42:53Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-03T19:56:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.0122
- eval_runtime: 27.9861
- eval_samples_per_second: 35.732
- eval_steps_per_second: 0.572
- epoch: 2.13
- step: 334
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
hbruce11216/distilbert-base-uncased-finetuned-OTTO
|
hbruce11216
| 2022-05-03T18:51:50Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-26T14:54:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-OTTO
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-OTTO
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.7687 | 1.0 | 17 | 3.3507 |
| 3.5069 | 2.0 | 34 | 3.2786 |
| 3.4126 | 3.0 | 51 | 3.2575 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
huggingtweets/wojespn
|
huggingtweets
| 2022-05-03T18:45:11Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/wojespn/1651603295184/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1509990164415893517/qIuzsMq6_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Adrian Wojnarowski</div>
<div style="text-align: center; font-size: 14px;">@wojespn</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Adrian Wojnarowski.
| Data | Adrian Wojnarowski |
| --- | --- |
| Tweets downloaded | 3250 |
| Retweets | 699 |
| Short tweets | 46 |
| Tweets kept | 2505 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kc1af3t/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @wojespn's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3d9r0f0h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3d9r0f0h/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/wojespn')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
theojolliffe/bart-large-cnn-finetuned-roundup-8
|
theojolliffe
| 2022-05-03T18:12:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T17:16:58Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-8
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-8
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4519
- Rouge1: 49.5671
- Rouge2: 27.0118
- Rougel: 30.8538
- Rougelsum: 45.5503
- Gen Len: 141.75
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 132 | 1.3159 | 48.5275 | 28.0817 | 30.6646 | 45.5024 | 142.0 |
| No log | 2.0 | 264 | 1.2377 | 47.0791 | 27.4386 | 28.9458 | 44.1536 | 142.0 |
| No log | 3.0 | 396 | 1.2474 | 49.3567 | 29.5904 | 30.8029 | 46.6083 | 142.0 |
| 0.9623 | 4.0 | 528 | 1.2914 | 47.8795 | 27.0611 | 29.8538 | 44.4494 | 142.0 |
| 0.9623 | 5.0 | 660 | 1.2982 | 49.9921 | 28.4839 | 31.5688 | 46.9734 | 142.0 |
| 0.9623 | 6.0 | 792 | 1.3521 | 46.7269 | 25.8672 | 29.7325 | 43.8279 | 142.0 |
| 0.9623 | 7.0 | 924 | 1.4102 | 47.4995 | 26.0066 | 29.4342 | 44.1102 | 141.8 |
| 0.3734 | 8.0 | 1056 | 1.4519 | 49.5671 | 27.0118 | 30.8538 | 45.5503 | 141.75 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
laituan245/molt5-base-smiles2caption
|
laituan245
| 2022-05-03T18:07:57Z | 617 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T17:12:55Z |
---
license: apache-2.0
---
This model can be used to generate an input caption from a SMILES string.
## Example Usage
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained("laituan245/molt5-base-smiles2caption", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-base-smiles2caption')
input_text = 'C1=CC2=C(C(=C1)[O-])NC(=CC2=O)C(=O)O'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, num_beams=5, max_length=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
laituan245/molt5-small
|
laituan245
| 2022-05-03T18:07:24Z | 178 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2204.11817",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T17:45:46Z |
---
license: apache-2.0
---
## Example Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("laituan245/molt5-small", model_max_length=512)
model = T5ForConditionalGeneration.from_pretrained('laituan245/molt5-small')
```
## Paper
For more information, please take a look at our paper.
Paper: [Translation between Molecules and Natural Language](https://arxiv.org/abs/2204.11817)
Authors: *Carl Edwards\*, Tuan Lai\*, Kevin Ros, Garrett Honke, Heng Ji*
|
TehranNLP-org/electra-base-mnli
|
TehranNLP-org
| 2022-05-03T17:01:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-30T12:50:13Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: MNLI
type: ''
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.8879266428935303
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SEED0042
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4265
- Accuracy: 0.8879
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3762 | 1.0 | 12272 | 0.3312 | 0.8794 |
| 0.2542 | 2.0 | 24544 | 0.3467 | 0.8843 |
| 0.1503 | 3.0 | 36816 | 0.4265 | 0.8879 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
TehranNLP-org/electra-base-hateXplain
|
TehranNLP-org
| 2022-05-03T17:00:31Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-30T12:51:26Z |
---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: SEED0042
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: HATEXPLAIN
type: ''
args: hatexplain
metrics:
- name: Accuracy
type: accuracy
value: 0.4162330905306972
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# SEED0042
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the HATEXPLAIN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7667
- Accuracy: 0.4162
- Accuracy 0: 0.8145
- Accuracy 1: 0.1895
- Accuracy 2: 0.3084
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 150
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Accuracy 0 | Accuracy 1 | Accuracy 2 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:----------:|:----------:|:----------:|
| No log | 1.0 | 481 | 0.7431 | 0.4152 | 0.7707 | 0.1805 | 0.3650 |
| No log | 2.0 | 962 | 0.7346 | 0.4152 | 0.8010 | 0.2190 | 0.2774 |
| No log | 3.0 | 1443 | 0.7667 | 0.4162 | 0.8145 | 0.1895 | 0.3084 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu113
- Datasets 2.1.0
- Tokenizers 0.11.6
|
theojolliffe/bart-large-cnn-finetuned-roundup-4
|
theojolliffe
| 2022-05-03T16:58:47Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T16:09:59Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-large-cnn-finetuned-roundup-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-cnn-finetuned-roundup-4
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2573
- Rouge1: 49.0193
- Rouge2: 28.6311
- Rougel: 31.3363
- Rougelsum: 46.1408
- Gen Len: 142.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 132 | 1.3178 | 48.4526 | 28.6361 | 30.2875 | 45.4822 | 142.0 |
| No log | 2.0 | 264 | 1.2404 | 48.139 | 28.2459 | 29.3584 | 45.0785 | 142.0 |
| No log | 3.0 | 396 | 1.2389 | 49.74 | 29.7834 | 33.143 | 46.8147 | 142.0 |
| 0.9855 | 4.0 | 528 | 1.2573 | 49.0193 | 28.6311 | 31.3363 | 46.1408 | 142.0 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
mrm8488/data2vec-text-base-finetuned-stsb
|
mrm8488
| 2022-05-03T16:28:24Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"data2vec-text",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T15:51:59Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- spearmanr
model-index:
- name: data2vec-text-base-finetuned-stsb
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
args: stsb
metrics:
- name: Spearmanr
type: spearmanr
value: 0.8716633516590501
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# data2vec-text-base-finetuned-stsb
This model is a fine-tuned version of [facebook/data2vec-text-base](https://huggingface.co/facebook/data2vec-text-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5530
- Pearson: 0.8732
- Spearmanr: 0.8717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.725353773731373e-05
- train_batch_size: 32
- eval_batch_size: 16
- seed: 5
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson | Spearmanr |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:---------:|
| No log | 1.0 | 180 | 1.0650 | 0.8102 | 0.8380 |
| No log | 2.0 | 360 | 0.6211 | 0.8524 | 0.8497 |
| 0.9312 | 3.0 | 540 | 0.5917 | 0.8640 | 0.8642 |
| 0.9312 | 4.0 | 720 | 0.5672 | 0.8695 | 0.8686 |
| 0.9312 | 5.0 | 900 | 0.5530 | 0.8732 | 0.8717 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
pietrolesci/t5v1_1-base-mnli
|
pietrolesci
| 2022-05-03T14:53:23Z | 4 | 1 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-05-03T14:50:42Z |
## Overview
T5-Base v1.1 model trained to generate hypotheses given a premise and a label. Below the settings used to train it
```yaml
Experiment configurations
├── datasets
│ └── mnli_train:
│ dataset_name: multi_nli
│ dataset_config_name: null
│ cache_dir: null
│ input_fields:
│ - premise
│ - hypothesis
│ target_field: label
│ train_subset_names: null
│ val_subset_names: validation_matched
│ test_subset_names: none
│ train_val_split: null
│ limit_train_samples: null
│ limit_val_samples: null
│ limit_test_samples: null
│ sampling_kwargs:
│ sampling_strategy: random
│ seed: 42
│ replace: false
│ align_labels_with_mapping: null
│ avoid_consistency_check: false
│ predict_label_mapping: null
│ mnli:
│ dataset_name: multi_nli
│ dataset_config_name: null
│ cache_dir: null
│ input_fields:
│ - premise
│ - hypothesis
│ target_field: label
│ train_subset_names: none
│ val_subset_names: none
│ test_subset_names: validation_mismatched
│ train_val_split: null
│ limit_train_samples: null
│ limit_val_samples: null
│ limit_test_samples: null
│ sampling_kwargs:
│ sampling_strategy: random
│ seed: 42
│ replace: false
│ align_labels_with_mapping: null
│ avoid_consistency_check: false
│ predict_label_mapping: null
│
├── data
│ └── _target_: src.task.nli.data.NLIGenerationData.from_config
│ main_dataset_name: null
│ use_additional_as_test: null
│ dataloader:
│ batch_size: 64
│ eval_batch_size: 100
│ num_workers: 16
│ pin_memory: true
│ drop_last: false
│ persistent_workers: false
│ shuffle: true
│ seed_dataloader: 42
│ replacement: false
│ processing:
│ preprocessing_num_workers: 16
│ preprocessing_batch_size: 1000
│ load_from_cache_file: true
│ padding: longest
│ truncation: longest_first
│ max_source_length: 128
│ max_target_length: 128
│ template: 'premise: $premise $label hypothesis: '
│ tokenizer:
│ _target_: transformers.AutoTokenizer.from_pretrained
│ pretrained_model_name_or_path: google/t5-v1_1-base
│ use_fast: true
│
├── task
│ └── optimizer:
│ name: Adafactor
│ lr: 0.001
│ weight_decay: 0.0
│ no_decay:
│ - bias
│ - LayerNorm.weight
│ decay_rate: -0.8
│ clip_threshold: 1.0
│ relative_step: false
│ scale_parameter: false
│ warmup_init: false
│ scheduler:
│ name: constant_schedule
│ model:
│ model_name_or_path: google/t5-v1_1-base
│ checkpoint_path: null
│ freeze: false
│ seed_init_weight: 42
│ _target_: src.task.nli.NLIGenerationTask.from_config
│ generation:
│ max_length: 128
│ min_length: 3
│ do_sample: true
│ early_stopping: false
│ num_beams: 1
│ temperature: 1.0
│ top_k: 50
│ top_p: 0.95
│ repetition_penalty: null
│ length_penalty: null
│ no_repeat_ngram_size: null
│ encoder_no_repeat_ngram_size: null
│ num_return_sequences: 1
│ max_time: null
│ max_new_tokens: null
│ decoder_start_token_id: null
│ use_cache: null
│ num_beam_groups: null
│ diversity_penalty: null
│
├── trainer
│ └── _target_: pytorch_lightning.Trainer
│ callbacks:
│ lr_monitor:
│ _target_: pytorch_lightning.callbacks.LearningRateMonitor
│ logging_interval: step
│ log_momentum: false
│ model_checkpoint:
│ _target_: pytorch_lightning.callbacks.ModelCheckpoint
│ dirpath: ./checkpoints/
│ filename: nli_generator_mnli-epoch={epoch:02d}-val_loss={val/aggregated_loss:.2f}
│ monitor: val/aggregated_loss
│ mode: min
│ verbose: false
│ save_last: true
│ save_top_k: 1
│ auto_insert_metric_name: false
│ save_on_train_epoch_end: false
│ rich_model_summary:
│ _target_: pytorch_lightning.callbacks.RichModelSummary
│ max_depth: 1
│ log_grad_norm:
│ _target_: src.core.callbacks.LogGradNorm
│ norm_type: 2
│ group_separator: /
│ only_total: true
│ on_step: true
│ on_epoch: false
│ prog_bar: true
│ log_generated_text:
│ _target_: src.core.callbacks.GenerateAndLogText
│ dirpath: ./generated_text
│ type: generated_text
│ pop_keys_after_logging: true
│ on_train: false
│ on_validation: false
│ on_test: true
│ log_to_wandb: true
│ wandb_log_dataset_sizes:
│ _target_: src.core.callbacks.WandbLogDatasetSizes
│ logger:
│ wandb:
│ _target_: pytorch_lightning.loggers.WandbLogger
│ project: nli_debiasing
│ entity: team_brushino
│ name: nli_generator_mnli
│ save_dir: ./
│ offline: false
│ log_model: false
│ group: mnli
│ job_type: generator
│ tags:
│ - nli_generator_mnli
│ - seed=42
│ - seed_dataloader=42
│ notes: nli_generator_mnli_time=02-24-53
│ enable_checkpointing: true
│ enable_progress_bar: true
│ enable_model_summary: true
│ gradient_clip_val: 0.0
│ gradient_clip_algorithm: null
│ accelerator: gpu
│ devices: auto
│ gpus: null
│ auto_select_gpus: true
│ accumulate_grad_batches: 1
│ max_epochs: 3
│ min_epochs: 1
│ max_steps: -1
│ min_steps: null
│ max_time: null
│ num_sanity_val_steps: 2
│ overfit_batches: 0.0
│ fast_dev_run: false
│ limit_train_batches: 1.0
│ limit_val_batches: 1.0
│ limit_test_batches: 1.0
│ profiler: null
│ detect_anomaly: false
│ deterministic: false
│ check_val_every_n_epoch: 1
│ val_check_interval: 0.1
│ log_every_n_steps: 10
│ move_metrics_to_cpu: false
│
└── training
└── run_val_before_fit: false
run_val_after_fit: false
run_test_before_fit: false
run_test_after_fit: true
lr: 0.001
seed: 42
show_batch: false
batch_size: 64
eval_batch_size: 100
num_workers: 16
pin_memory: true
drop_last: false
persistent_workers: false
shuffle: true
seed_dataloader: 42
ignore_warnings: true
experiment_name: nli_generator_mnli
```
|
UWB-AIR/Czert-B-base-cased-long-zero-shot
|
UWB-AIR
| 2022-05-03T13:49:35Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"longformer",
"feature-extraction",
"cs",
"fill-mask",
"arxiv:2103.13031",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
tags:
- cs
- fill-mask
---
# CZERT
This repository keeps trained Czert-B-base-cased-long-zero-shot model for the paper [Czert – Czech BERT-like Model for Language Representation
](https://arxiv.org/abs/2103.13031)
For more information, see the paper
This is long version of Czert-B-base-cased created without any finetunning on long documents. Positional embedings were created by simply repeating the positional embeddings of the original Czert-B model. For tokenization, please use BertTokenizer. Cannot be used with AutoTokenizer.
## Available Models
You can download **MLM & NSP only** pretrained models
~~[CZERT-A-v1](https://air.kiv.zcu.cz/public/CZERT-A-czert-albert-base-uncased.zip)
[CZERT-B-v1](https://air.kiv.zcu.cz/public/CZERT-B-czert-bert-base-cased.zip)~~
After some additional experiments, we found out that the tokenizers config was exported wrongly. In Czert-B-v1, the tokenizer parameter "do_lower_case" was wrongly set to true. In Czert-A-v1 the parameter "strip_accents" was incorrectly set to true.
Both mistakes are repaired in v2.
[CZERT-A-v2](https://air.kiv.zcu.cz/public/CZERT-A-v2-czert-albert-base-uncased.zip)
[CZERT-B-v2](https://air.kiv.zcu.cz/public/CZERT-B-v2-czert-bert-base-cased.zip)
or choose from one of **Finetuned Models**
| | Models |
| - | - |
| Sentiment Classification<br> (Facebook or CSFD) | [CZERT-A-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-A_fb.zip) <br> [CZERT-B-sentiment-FB](https://air.kiv.zcu.cz/public/CZERT-B_fb.zip) <br> [CZERT-A-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-A_csfd.zip) <br> [CZERT-B-sentiment-CSFD](https://air.kiv.zcu.cz/public/CZERT-B_csfd.zip) | Semantic Text Similarity <br> (Czech News Agency) | [CZERT-A-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-A-sts-CNA.zip) <br> [CZERT-B-sts-CNA](https://air.kiv.zcu.cz/public/CZERT-B-sts-CNA.zip)
| Named Entity Recognition | [CZERT-A-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-A-ner-CNEC-cased.zip) <br> [CZERT-B-ner-CNEC](https://air.kiv.zcu.cz/public/CZERT-B-ner-CNEC-cased.zip) <br>[PAV-ner-CNEC](https://air.kiv.zcu.cz/public/PAV-ner-CNEC-cased.zip) <br> [CZERT-A-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-A-ner-BSNLP-cased.zip)<br>[CZERT-B-ner-BSNLP](https://air.kiv.zcu.cz/public/CZERT-B-ner-BSNLP-cased.zip) <br>[PAV-ner-BSNLP](https://air.kiv.zcu.cz/public/PAV-ner-BSNLP-cased.zip) |
| Morphological Tagging<br> | [CZERT-A-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-A-morphtag-126k-cased.zip)<br>[CZERT-B-morphtag-126k](https://air.kiv.zcu.cz/public/CZERT-B-morphtag-126k-cased.zip) |
| Semantic Role Labelling |[CZERT-A-srl](https://air.kiv.zcu.cz/public/CZERT-A-srl-cased.zip)<br> [CZERT-B-srl](https://air.kiv.zcu.cz/public/CZERT-B-srl-cased.zip) |
## How to Use CZERT?
### Sentence Level Tasks
We evaluate our model on two sentence level tasks:
* Sentiment Classification,
* Semantic Text Similarity.
<!-- tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
model = TFAlbertForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, num_labels=1)
or
self.tokenizer = BertTokenizerFast.from_pretrained(CZERT_MODEL_PATH, strip_accents=False)
self.model_encoder = AutoModelForSequenceClassification.from_pretrained(CZERT_MODEL_PATH, from_tf=True)
-->
### Document Level Tasks
We evaluate our model on one document level task
* Multi-label Document Classification.
### Token Level Tasks
We evaluate our model on three token level tasks:
* Named Entity Recognition,
* Morphological Tagging,
* Semantic Role Labelling.
## Downstream Tasks Fine-tuning Results
### Sentiment Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:----:|:------------------------:|:------------------------:|:------------------------:|:-----------------------:|:--------------------------------:|
| FB | 71.72 ± 0.91 | 73.87 ± 0.50 | 59.50 ± 0.47 | 72.47 ± 0.72 | **76.55** ± **0.14** |
| CSFD | 82.80 ± 0.14 | 82.51 ± 0.14 | 75.40 ± 0.18 | 79.58 ± 0.46 | **84.79** ± **0.26** |
Average F1 results for the Sentiment Classification task. For more information, see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Text Similarity
| | **mBERT** | **Pavlov** | **Albert-random** | **Czert-A** | **Czert-B** |
|:-------------|:--------------:|:--------------:|:-----------------:|:--------------:|:----------------------:|
| STA-CNA | 83.335 ± 0.063 | 83.593 ± 0.050 | 43.184 ± 0.125 | 82.942 ± 0.106 | **84.345** ± **0.028** |
| STS-SVOB-img | 79.367 ± 0.486 | 79.900 ± 0.810 | 15.739 ± 2.992 | 79.444 ± 0.338 | **83.744** ± **0.395** |
| STS-SVOB-hl | 78.833 ± 0.296 | 76.996 ± 0.305 | 33.949 ± 1.807 | 75.089 ± 0.806 | **79.827 ± 0.469** |
Comparison of Pearson correlation achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on semantic text similarity. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Multi-label Document Classification
| | mBERT | SlavicBERT | ALBERT-r | Czert-A | Czert-B |
|:-----:|:------------:|:------------:|:------------:|:------------:|:-------------------:|
| AUROC | 97.62 ± 0.08 | 97.80 ± 0.06 | 94.35 ± 0.13 | 97.49 ± 0.07 | **98.00** ± **0.04** |
| F1 | 83.04 ± 0.16 | 84.08 ± 0.14 | 72.44 ± 0.22 | 82.27 ± 0.17 | **85.06** ± **0.11** |
Comparison of F1 and AUROC score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on multi-label document classification. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Morphological Tagging
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------------------|:---------------|:---------------|:---------------|:---------------|:---------------|
| Universal Dependencies | 99.176 ± 0.006 | 99.211 ± 0.008 | 96.590 ± 0.096 | 98.713 ± 0.008 | **99.300 ± 0.009** |
Comparison of F1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on morphological tagging task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
### Semantic Role Labelling
<div id="tab:SRL">
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B | dep-based | gold-dep |
|:------:|:----------:|:----------:|:-------------:|:----------:|:----------:|:---------:|:--------:|
| span | 78.547 ± 0.110 | 79.333 ± 0.080 | 51.365 ± 0.423 | 72.254 ± 0.172 | **81.861 ± 0.102** | \- | \- |
| syntax | 90.226 ± 0.224 | 90.492 ± 0.040 | 80.747 ± 0.131 | 80.319 ± 0.054 | **91.462 ± 0.062** | 85.19 | 89.52 |
SRL results – dep columns are evaluate with labelled F1 from CoNLL 2009 evaluation script, other columns are evaluated with span F1 score same as it was used for NER evaluation. For more information see [the paper](https://arxiv.org/abs/2103.13031).
</div>
### Named Entity Recognition
| | mBERT | Pavlov | Albert-random | Czert-A | Czert-B |
|:-----------|:---------------|:---------------|:---------------|:---------------|:---------------|
| CNEC | **86.225 ± 0.208** | **86.565 ± 0.198** | 34.635 ± 0.343 | 72.945 ± 0.227 | 86.274 ± 0.116 |
| BSNLP 2019 | 84.006 ± 1.248 | **86.699 ± 0.370** | 19.773 ± 0.938 | 48.859 ± 0.605 | **86.729 ± 0.344** |
Comparison of f1 score achieved using pre-trained CZERT-A, CZERT-B, mBERT, Pavlov and randomly initialised Albert on named entity recognition task. For more information see [the paper](https://arxiv.org/abs/2103.13031).
## Licence
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. http://creativecommons.org/licenses/by-nc-sa/4.0/
## How should I cite CZERT?
For now, please cite [the Arxiv paper](https://arxiv.org/abs/2103.13031):
```
@article{sido2021czert,
title={Czert -- Czech BERT-like Model for Language Representation},
author={Jakub Sido and Ondřej Pražák and Pavel Přibáň and Jan Pašek and Michal Seják and Miloslav Konopík},
year={2021},
eprint={2103.13031},
archivePrefix={arXiv},
primaryClass={cs.CL},
journal={arXiv preprint arXiv:2103.13031},
}
```
|
PoloHuggingface/French_grammar_error_corrector
|
PoloHuggingface
| 2022-05-03T13:32:40Z | 102 | 6 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"text2text generation",
"fr",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-04-02T15:45:49Z |
---
content:
language:
- fr
tags:
- text2text generation
widget:
- text: "improve grammar: Elle ne peux jamais aller au cinéma avec son amis"
example_title: "Grammar correction"
---
# Finetuned T5 on the french part of Lang-8 to automatically correct sentences.
Since the Lang-8 dataset contains really short sentences, the model does not generalize well with sentences larger than 10 words.
I'll upload soon the cleaned dataset that I've used for training.
|
sanchit-gandhi/flax-wav2vec2-2-bart-large-960h
|
sanchit-gandhi
| 2022-05-03T12:24:52Z | 3 | 0 |
transformers
|
[
"transformers",
"jax",
"speech-encoder-decoder",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-03T12:07:42Z |
2.5% WER on dev.clean: https://wandb.ai/sanchit-gandhi/flax-wav2vec2-2-bart-large-960h/runs/2lhazd5v
|
Tobias/bert-base-uncased_English_Hotel_classification
|
Tobias
| 2022-05-03T11:46:32Z | 7 | 1 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"eng",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T11:33:24Z |
---
language: eng
tags:
- bert
license: apache-2.0
widget:
- text: "The hotel is very nicely located"
example_title: "Example 1"
- text: "The reception staff were extremely helpful and very welcoming"
example_title: "Example 2"
- text: "There is no balcony in the rooms on the mountain side"
example_title: "Example 3"
- text: "A bit pricey"
example_title: "Example 4"
---
# German Hotel Review Sentiment Classification
A model trained on English Hotel Reviews from Switzerland. The base model is the [bert-base-uncased](https://huggingface.co/bert-base-uncased). The last hidden layer of the base model was extracted and a classification layer was added. The entire model was then trained for 5 epochs on our dataset.
# Model Performance
| Classes | Precision | Recall | F1 Score |
| :--- | :---: | :---: |:---: |
| Room | 77.78% | 77.78% | 77.78% |
| Location | 95.45% | 95.45% | 95.45% |
| Staff | 75.00% | 93.75% | 83.33% |
| Unknown | 71.43% | 50.00% | 58.82% |
| HotelOrganisation | 27.27% | 30.00% | 28.57% |
| Food | 87.50% | 87.50% | 87.50% |
| ReasonForStay | 63.64% | 58.33% | 60.87%|
| GeneralUtility | 66.67% | 50.00% | 66.67% |
| Accuracy | | | 74.00% |
| Macro Average | 70.59%| 67.85% | 68.68% |
| Weighted Average | 74.17% | 74.00% | 73.66% |
## Confusion Matrix
|
lucaordronneau/twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
|
lucaordronneau
| 2022-05-03T11:29:22Z | 11 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-13T12:29:56Z |
---
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# twitter-roberta-base-sentiment-latest-finetuned-FG-SINGLE_SENTENCE-NEWS
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment-latest](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment-latest) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2822
- Accuracy: 0.6305
- F1: 0.6250
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 321 | 0.9646 | 0.5624 | 0.4048 |
| 0.9537 | 2.0 | 642 | 0.9474 | 0.5644 | 0.4176 |
| 0.9537 | 3.0 | 963 | 0.9008 | 0.5903 | 0.5240 |
| 0.858 | 4.0 | 1284 | 0.9939 | 0.5999 | 0.5846 |
| 0.5908 | 5.0 | 1605 | 1.0947 | 0.6108 | 0.6026 |
| 0.5908 | 6.0 | 1926 | 1.2507 | 0.5740 | 0.5823 |
| 0.3676 | 7.0 | 2247 | 1.4717 | 0.6128 | 0.6017 |
| 0.2246 | 8.0 | 2568 | 1.6726 | 0.5965 | 0.6003 |
| 0.2246 | 9.0 | 2889 | 1.8041 | 0.6380 | 0.6298 |
| 0.1468 | 10.0 | 3210 | 1.9796 | 0.6053 | 0.6026 |
| 0.1161 | 11.0 | 3531 | 2.0988 | 0.6237 | 0.6202 |
| 0.1161 | 12.0 | 3852 | 2.4171 | 0.5944 | 0.5989 |
| 0.0916 | 13.0 | 4173 | 2.3326 | 0.6374 | 0.6288 |
| 0.0916 | 14.0 | 4494 | 2.5472 | 0.6360 | 0.6245 |
| 0.0661 | 15.0 | 4815 | 2.9127 | 0.6176 | 0.6187 |
| 0.0454 | 16.0 | 5136 | 2.9133 | 0.6326 | 0.6276 |
| 0.0454 | 17.0 | 5457 | 3.1299 | 0.6210 | 0.6162 |
| 0.0337 | 18.0 | 5778 | 3.1828 | 0.6224 | 0.6188 |
| 0.0223 | 19.0 | 6099 | 3.2655 | 0.6299 | 0.6223 |
| 0.0223 | 20.0 | 6420 | 3.2822 | 0.6305 | 0.6250 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.9.1
- Datasets 1.18.4
- Tokenizers 0.11.6
|
anton-l/xtreme_s_xlsr_300m_voxpopuli_en
|
anton-l
| 2022-05-03T09:55:15Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"voxpopuli",
"google/xtreme_s",
"generated_from_trainer",
"en",
"dataset:google/xtreme_s",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-04-29T18:58:24Z |
---
language:
- en
license: apache-2.0
tags:
- voxpopuli
- google/xtreme_s
- generated_from_trainer
datasets:
- google/xtreme_s
model-index:
- name: xtreme_s_xlsr_300m_voxpopuli_en
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xtreme_s_xlsr_300m_voxpopuli_en
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the GOOGLE/XTREME_S - VOXPOPULI.EN dataset.
It achieves the following results on the evaluation set:
- Cer: 0.0966
- Loss: 0.3127
- Wer: 0.1549
- Predict Samples: 1842
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.4221 | 0.19 | 500 | 1.3325 | 0.8224 | 0.3432 |
| 0.8429 | 0.38 | 1000 | 0.7087 | 0.5028 | 0.2023 |
| 0.7377 | 0.57 | 1500 | 0.4900 | 0.2778 | 0.1339 |
| 0.5641 | 0.77 | 2000 | 0.4460 | 0.2540 | 0.1284 |
| 0.5787 | 0.96 | 2500 | 0.4242 | 0.2148 | 0.1167 |
| 0.3465 | 1.15 | 3000 | 0.4210 | 0.2087 | 0.1154 |
| 0.2787 | 1.34 | 3500 | 0.3954 | 0.2090 | 0.1155 |
| 0.2775 | 1.53 | 4000 | 0.3938 | 0.1992 | 0.1133 |
| 0.262 | 1.72 | 4500 | 0.3748 | 0.2104 | 0.1151 |
| 0.3138 | 1.92 | 5000 | 0.3825 | 0.1993 | 0.1134 |
| 0.4331 | 2.11 | 5500 | 0.3648 | 0.1935 | 0.1104 |
| 0.3802 | 2.3 | 6000 | 0.3966 | 0.1910 | 0.1109 |
| 0.3928 | 2.49 | 6500 | 0.3995 | 0.1898 | 0.1100 |
| 0.3441 | 2.68 | 7000 | 0.3764 | 0.1887 | 0.1103 |
| 0.3673 | 2.87 | 7500 | 0.3800 | 0.1843 | 0.1086 |
| 0.3422 | 3.07 | 8000 | 0.3932 | 0.1830 | 0.1092 |
| 0.2933 | 3.26 | 8500 | 0.3672 | 0.1915 | 0.1104 |
| 0.1785 | 3.45 | 9000 | 0.3820 | 0.1796 | 0.1072 |
| 0.321 | 3.64 | 9500 | 0.3533 | 0.1994 | 0.1126 |
| 0.1673 | 3.83 | 10000 | 0.3683 | 0.1856 | 0.1084 |
| 0.1757 | 4.02 | 10500 | 0.3365 | 0.1925 | 0.1102 |
| 0.1881 | 4.22 | 11000 | 0.3528 | 0.1775 | 0.1066 |
| 0.3106 | 4.41 | 11500 | 0.3909 | 0.1754 | 0.1063 |
| 0.25 | 4.6 | 12000 | 0.3734 | 0.1723 | 0.1052 |
| 0.2005 | 4.79 | 12500 | 0.3358 | 0.1900 | 0.1092 |
| 0.2982 | 4.98 | 13000 | 0.3513 | 0.1766 | 0.1060 |
| 0.1552 | 5.17 | 13500 | 0.3720 | 0.1729 | 0.1059 |
| 0.1645 | 5.37 | 14000 | 0.3569 | 0.1713 | 0.1044 |
| 0.2065 | 5.56 | 14500 | 0.3639 | 0.1720 | 0.1048 |
| 0.1898 | 5.75 | 15000 | 0.3660 | 0.1726 | 0.1050 |
| 0.1397 | 5.94 | 15500 | 0.3731 | 0.1670 | 0.1033 |
| 0.2056 | 6.13 | 16000 | 0.3782 | 0.1650 | 0.1030 |
| 0.1859 | 6.32 | 16500 | 0.3903 | 0.1667 | 0.1033 |
| 0.1374 | 6.52 | 17000 | 0.3721 | 0.1736 | 0.1048 |
| 0.2482 | 6.71 | 17500 | 0.3899 | 0.1643 | 0.1023 |
| 0.159 | 6.9 | 18000 | 0.3847 | 0.1687 | 0.1032 |
| 0.1487 | 7.09 | 18500 | 0.3817 | 0.1671 | 0.1030 |
| 0.1942 | 7.28 | 19000 | 0.4120 | 0.1616 | 0.1018 |
| 0.1517 | 7.47 | 19500 | 0.3856 | 0.1635 | 0.1020 |
| 0.0946 | 7.67 | 20000 | 0.3838 | 0.1621 | 0.1016 |
| 0.1455 | 7.86 | 20500 | 0.3749 | 0.1652 | 0.1020 |
| 0.1303 | 8.05 | 21000 | 0.4074 | 0.1615 | 0.1011 |
| 0.1207 | 8.24 | 21500 | 0.4121 | 0.1606 | 0.1008 |
| 0.0727 | 8.43 | 22000 | 0.3948 | 0.1607 | 0.1009 |
| 0.1123 | 8.62 | 22500 | 0.4025 | 0.1603 | 0.1009 |
| 0.1606 | 8.82 | 23000 | 0.3963 | 0.1580 | 0.1004 |
| 0.1458 | 9.01 | 23500 | 0.3991 | 0.1574 | 0.1002 |
| 0.2286 | 9.2 | 24000 | 0.4149 | 0.1596 | 0.1009 |
| 0.1284 | 9.39 | 24500 | 0.4251 | 0.1572 | 0.1002 |
| 0.1141 | 9.58 | 25000 | 0.4264 | 0.1579 | 0.1002 |
| 0.1823 | 9.77 | 25500 | 0.4230 | 0.1562 | 0.0999 |
| 0.2514 | 9.97 | 26000 | 0.4242 | 0.1564 | 0.0999 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.1+cu111
- Datasets 1.18.4.dev0
- Tokenizers 0.11.6
|
jerryKakooza/language-detection-fine-tuned-on-xlm-roberta-base
|
jerryKakooza
| 2022-05-03T09:31:18Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"dataset:common_language",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T16:45:16Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- common_language
metrics:
- accuracy
model-index:
- name: language-detection-fine-tuned-on-xlm-roberta-base
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: common_language
type: common_language
args: full
metrics:
- name: Accuracy
type: accuracy
value: 0.9760187824920342
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# language-detection-fine-tuned-on-xlm-roberta-base
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the common_language dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- Accuracy: 0.9760
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0725 | 1.0 | 22194 | 0.1642 | 0.9760 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
agi-css/distilroberta-base-mrl
|
agi-css
| 2022-05-03T09:27:53Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-23T06:28:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilroberta-base-mrl
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-mrl
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0170
- Accuracy: 0.9967
- F1: 0.9967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.1821851463909416e-05
- train_batch_size: 400
- eval_batch_size: 400
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 9
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 48 | 0.0265 | 0.9946 | 0.9946 |
| No log | 2.0 | 96 | 0.0180 | 0.9962 | 0.9962 |
| No log | 3.0 | 144 | 0.0163 | 0.9962 | 0.9962 |
| No log | 4.0 | 192 | 0.0194 | 0.9946 | 0.9946 |
| No log | 5.0 | 240 | 0.0193 | 0.9942 | 0.9942 |
| No log | 6.0 | 288 | 0.0172 | 0.9967 | 0.9967 |
| No log | 7.0 | 336 | 0.0206 | 0.9954 | 0.9954 |
| No log | 8.0 | 384 | 0.0183 | 0.9962 | 0.9962 |
| No log | 9.0 | 432 | 0.0170 | 0.9967 | 0.9967 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Hate-speech-CNERG/bengali-abusive-MuRIL
|
Hate-speech-CNERG
| 2022-05-03T08:50:49Z | 33 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"bn",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-24T18:59:53Z |
---
language: [bn]
license: afl-3.0
---
This model is used detecting **abusive speech** in **Bengali**. It is finetuned on MuRIL model using bengali abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~
|
Hate-speech-CNERG/urdu-abusive-MuRIL
|
Hate-speech-CNERG
| 2022-05-03T08:43:53Z | 13 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"ur",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-25T19:18:21Z |
---
language: ur
license: afl-3.0
---
This model is used to detect **abusive speech** in **Urdu**. It is finetuned on MuRIL model using Urdu abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~
|
alla1101/distilbert-base-uncased-finetuned-emotion
|
alla1101
| 2022-05-03T08:11:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T07:54:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.924
- name: F1
type: f1
value: 0.9240869504197766
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2236
- Accuracy: 0.924
- F1: 0.9241
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3293 | 0.901 | 0.8979 |
| No log | 2.0 | 500 | 0.2236 | 0.924 | 0.9241 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
niklaspm/linkbert-large-finetuned-squad
|
niklaspm
| 2022-05-03T07:51:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-02T19:06:30Z |
---
license: apache-2.0
---
---
license: apache-2.0
---
**Exact Match** 92.68
**F1** 86.5
Checkout [linkbert-base-finetuned-squad](https://huggingface.co/niklaspm/linkbert-base-finetuned-squad)
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827)
|
niklaspm/linkbert-base-finetuned-squad
|
niklaspm
| 2022-05-03T07:50:32Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"arxiv:2203.15827",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-02T08:53:53Z |
---
license: apache-2.0
---
**Exact Match** 83.19
**F1** 90.46
Checkout [linkbert-large-finetuned-squad](https://huggingface.co/niklaspm/linkbert-large-finetuned-squad) which achives F1:92.68 and EM:86.5
See [LinkBERT Paper](https://arxiv.org/abs/2203.15827)
|
DioLiu/distilbert-base-uncased-finetuned-sst2-nostop
|
DioLiu
| 2022-05-03T06:43:45Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-03T06:31:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-nostop
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-nostop
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0701
- Accuracy: 0.9888
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.125 | 1.0 | 1116 | 0.0975 | 0.9743 |
| 0.0599 | 2.0 | 2232 | 0.0692 | 0.9840 |
| 0.0191 | 3.0 | 3348 | 0.0570 | 0.9871 |
| 0.0109 | 4.0 | 4464 | 0.0660 | 0.9882 |
| 0.0092 | 5.0 | 5580 | 0.0701 | 0.9888 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Hate-speech-CNERG/hindi-codemixed-abusive-MuRIL
|
Hate-speech-CNERG
| 2022-05-03T06:03:59Z | 19 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"arxiv:2204.12543",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-25T05:12:26Z |
---
language: hi-en
license: afl-3.0
---
This model is used detecting **abusive speech** in **Code-Mixed Hindi**. It is finetuned on MuRIL model using code-mixed hindi abusive speech dataset.
The model is trained with learning rates of 2e-5. Training code can be found at this [url](https://github.com/hate-alert/IndicAbusive)
LABEL_0 :-> Normal
LABEL_1 :-> Abusive
### For more details about our paper
Mithun Das, Somnath Banerjee and Animesh Mukherjee. "[Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages](https://arxiv.org/abs/2204.12543)". Accepted at ACM HT 2022.
***Please cite our paper in any published work that uses any of these resources.***
~~~
@article{das2022data,
title={Data Bootstrapping Approaches to Improve Low Resource Abusive Language Detection for Indic Languages},
author={Das, Mithun and Banerjee, Somnath and Mukherjee, Animesh},
journal={arXiv preprint arXiv:2204.12543},
year={2022}
}
~~~
|
Nakul24/Spanbert-emotion-extraction
|
Nakul24
| 2022-05-03T05:10:03Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"question-answering",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-05-01T17:42:46Z |
Enter the Name of Emotion in the Question Field
Enter The Text from which emotion has to be extracted
Example 1-
Question - Guilty
Context - I shouted to my mom
Example 2 -
Question - Sad
Context - I felt betrayed when my girlfriend kissed another guy even though she was drunk
Note: Model is still under development stage so results might be a little strange
|
huggingtweets/lonelythey18
|
huggingtweets
| 2022-05-03T05:01:20Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-03T04:59:03Z |
---
language: en
thumbnail: http://www.huggingtweets.com/lonelythey18/1651554075248/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1488171735174238211/4Y7YAhJG_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Cara</div>
<div style="text-align: center; font-size: 14px;">@lonelythey18</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Cara.
| Data | Cara |
| --- | --- |
| Tweets downloaded | 2640 |
| Retweets | 301 |
| Short tweets | 500 |
| Tweets kept | 1839 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3l0t3r5o/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @lonelythey18's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1znlhqjr) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1znlhqjr/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/lonelythey18')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
BowmanFox/AlliedMasterComputer
|
BowmanFox
| 2022-05-03T01:09:04Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2022-03-26T07:14:49Z |
---
license: other
---
A dataset trained on known dialogue from AM in Harlan Ellison's video game adaption of "I have no mouth and I must scream," alongside the initial quote about hate. Model historically uses DialoGPT, however, will be updated and/or converted to C1-6B as soon as possible.
|
kornosk/bert-election2020-twitter-stance-biden
|
kornosk
| 2022-05-02T22:59:23Z | 135 | 2 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Joe Biden (f-BERT)
Pre-trained weights for **f-BERT** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Joe Biden.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Joe Biden.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Joe Biden.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-biden"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Biden!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Biden is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
```
|
kornosk/bert-election2020-twitter-stance-trump-KE-MLM
|
kornosk
| 2022-05-02T22:58:49Z | 40 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"text-classification",
"twitter",
"stance-detection",
"election2020",
"politics",
"en",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language: "en"
tags:
- twitter
- stance-detection
- election2020
- politics
license: "gpl-3.0"
---
# Pre-trained BERT on Twitter US Election 2020 for Stance Detection towards Donald Trump (KE-MLM)
Pre-trained weights for **KE-MLM model** in [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Training Data
This model is pre-trained on over 5 million English tweets about the 2020 US Presidential Election. Then fine-tuned using our [stance-labeled data](https://github.com/GU-DataLab/stance-detection-KE-MLM) for stance detection towards Donald Trump.
# Training Objective
This model is initialized with BERT-base and trained with normal MLM objective with classification layer fine-tuned for stance detection towards Donald Trump.
# Usage
This pre-trained language model is fine-tuned to the stance detection task specifically for Donald Trump.
Please see the [official repository](https://github.com/GU-DataLab/stance-detection-KE-MLM) for more detail.
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
# choose GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
# select mode path here
pretrained_LM_path = "kornosk/bert-election2020-twitter-stance-trump-KE-MLM"
# load model
tokenizer = AutoTokenizer.from_pretrained(pretrained_LM_path)
model = AutoModelForSequenceClassification.from_pretrained(pretrained_LM_path)
id2label = {
0: "AGAINST",
1: "FAVOR",
2: "NONE"
}
##### Prediction Neutral #####
sentence = "Hello World."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Favor #####
sentence = "Go Go Trump!!!"
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
##### Prediction Against #####
sentence = "Trump is the worst."
inputs = tokenizer(sentence.lower(), return_tensors="pt")
outputs = model(**inputs)
predicted_probability = torch.softmax(outputs[0], dim=1)[0].tolist()
print("Sentence:", sentence)
print("Prediction:", id2label[np.argmax(predicted_probability)])
print("Against:", predicted_probability[0])
print("Favor:", predicted_probability[1])
print("Neutral:", predicted_probability[2])
# please consider citing our paper if you feel this is useful :)
```
# Reference
- [Knowledge Enhance Masked Language Model for Stance Detection](https://www.aclweb.org/anthology/2021.naacl-main.376), NAACL 2021.
# Citation
```bibtex
@inproceedings{kawintiranon2021knowledge,
title={Knowledge Enhanced Masked Language Model for Stance Detection},
author={Kawintiranon, Kornraphop and Singh, Lisa},
booktitle={Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies},
year={2021},
publisher={Association for Computational Linguistics},
url={https://www.aclweb.org/anthology/2021.naacl-main.376}
}
```
|
huggingtweets/usrsistakenhelp
|
huggingtweets
| 2022-05-02T22:26:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-02T22:25:02Z |
---
language: en
thumbnail: http://www.huggingtweets.com/usrsistakenhelp/1651530363067/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1520487753896665088/lO1PwH2q_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Rosa - I miss tgamm</div>
<div style="text-align: center; font-size: 14px;">@usrsistakenhelp</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Rosa - I miss tgamm.
| Data | Rosa - I miss tgamm |
| --- | --- |
| Tweets downloaded | 3244 |
| Retweets | 507 |
| Short tweets | 1160 |
| Tweets kept | 1577 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/jxrwgo01/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @usrsistakenhelp's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1z4w7mpe) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1z4w7mpe/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/usrsistakenhelp')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
caush/Clickbait1
|
caush
| 2022-05-02T20:36:10Z | 110 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-26T18:25:39Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Clickbait1
results: []
---
# Clickbait1
This model is a fine-tuned version of [microsoft/Multilingual-MiniLM-L12-H384](https://huggingface.co/microsoft/Multilingual-MiniLM-L12-H384) on the [Webis-Clickbait-17](https://zenodo.org/record/5530410) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0257
## Model description
MiniLM is a distilled model from the paper "MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers".
We fine tune this model to evaluate (regression) the clickbait level of title news.
## Intended uses & limitations
Model looks like the model described in the paper [Predicting Clickbait Strength in Online Social Media](https://aclanthology.org/2020.coling-main.425/) by Indurthi Vijayasaradhi, Syed Bakhtiyar, Gupta Manish, Varma Vasudeva.
The model was trained with english titles.
## Training and evaluation data
We trained the model with the official training data for the chalenge (clickbait17-train-170630.zip (894 MiB, 19538 posts), plus another set that was just available after the end of the challenge (clickbait17-train-170331.zip (157 MiB, 2459 posts).
## Training procedure
Code can be find in [Github](https://github.com/caush/Clickbait).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.05 | 50 | 0.0571 |
| No log | 0.09 | 100 | 0.0448 |
| No log | 0.14 | 150 | 0.0391 |
| No log | 0.18 | 200 | 0.0326 |
| No log | 0.23 | 250 | 0.0343 |
| No log | 0.27 | 300 | 0.0343 |
| No log | 0.32 | 350 | 0.0343 |
| No log | 0.36 | 400 | 0.0346 |
| No log | 0.41 | 450 | 0.0343 |
| 0.0388 | 0.46 | 500 | 0.0297 |
| 0.0388 | 0.5 | 550 | 0.0293 |
| 0.0388 | 0.55 | 600 | 0.0301 |
| 0.0388 | 0.59 | 650 | 0.0290 |
| 0.0388 | 0.64 | 700 | 0.0326 |
| 0.0388 | 0.68 | 750 | 0.0285 |
| 0.0388 | 0.73 | 800 | 0.0285 |
| 0.0388 | 0.77 | 850 | 0.0275 |
| 0.0388 | 0.82 | 900 | 0.0314 |
| 0.0388 | 0.87 | 950 | 0.0309 |
| 0.0297 | 0.91 | 1000 | 0.0277 |
| 0.0297 | 0.96 | 1050 | 0.0281 |
| 0.0297 | 1.0 | 1100 | 0.0273 |
| 0.0297 | 1.05 | 1150 | 0.0270 |
| 0.0297 | 1.09 | 1200 | 0.0291 |
| 0.0297 | 1.14 | 1250 | 0.0293 |
| 0.0297 | 1.18 | 1300 | 0.0269 |
| 0.0297 | 1.23 | 1350 | 0.0276 |
| 0.0297 | 1.28 | 1400 | 0.0279 |
| 0.0297 | 1.32 | 1450 | 0.0267 |
| 0.0265 | 1.37 | 1500 | 0.0270 |
| 0.0265 | 1.41 | 1550 | 0.0300 |
| 0.0265 | 1.46 | 1600 | 0.0274 |
| 0.0265 | 1.5 | 1650 | 0.0274 |
| 0.0265 | 1.55 | 1700 | 0.0266 |
| 0.0265 | 1.59 | 1750 | 0.0267 |
| 0.0265 | 1.64 | 1800 | 0.0267 |
| 0.0265 | 1.68 | 1850 | 0.0280 |
| 0.0265 | 1.73 | 1900 | 0.0274 |
| 0.0265 | 1.78 | 1950 | 0.0272 |
| 0.025 | 1.82 | 2000 | 0.0261 |
| 0.025 | 1.87 | 2050 | 0.0268 |
| 0.025 | 1.91 | 2100 | 0.0268 |
| 0.025 | 1.96 | 2150 | 0.0259 |
| 0.025 | 2.0 | 2200 | 0.0257 |
| 0.025 | 2.05 | 2250 | 0.0260 |
| 0.025 | 2.09 | 2300 | 0.0263 |
| 0.025 | 2.14 | 2350 | 0.0262 |
| 0.025 | 2.19 | 2400 | 0.0269 |
| 0.025 | 2.23 | 2450 | 0.0262 |
| 0.0223 | 2.28 | 2500 | 0.0262 |
| 0.0223 | 2.32 | 2550 | 0.0267 |
| 0.0223 | 2.37 | 2600 | 0.0260 |
| 0.0223 | 2.41 | 2650 | 0.0260 |
| 0.0223 | 2.46 | 2700 | 0.0259 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0a0+17540c5
- Datasets 2.1.0
- Tokenizers 0.12.1
|
amirbr/finetuning-sentiment-model-3000-samples
|
amirbr
| 2022-05-02T20:06:03Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-04-30T09:31:11Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-3000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
|
ali2066
| 2022-05-02T18:36:09Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T18:30:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_all_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0699
- Precision: 0.9942
- Recall: 0.9773
- F1: 0.9857
- Accuracy: 0.9725
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 479 | 0.4036 | 0.8333 | 0.9326 | 0.8802 | 0.8054 |
| 0.5047 | 2.0 | 958 | 0.3749 | 0.8635 | 0.9339 | 0.8973 | 0.8361 |
| 0.3336 | 3.0 | 1437 | 0.3789 | 0.8862 | 0.9184 | 0.9020 | 0.8471 |
| 0.2644 | 4.0 | 1916 | 0.4024 | 0.8762 | 0.9171 | 0.8962 | 0.8371 |
| 0.2233 | 5.0 | 2395 | 0.4195 | 0.8784 | 0.9171 | 0.8973 | 0.8391 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
|
ali2066
| 2022-05-02T18:29:59Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T18:27:39Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_editorials_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.8119
- Precision: 0.2752
- Recall: 0.9522
- F1: 0.4270
- Accuracy: 0.2849
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 166 | 0.0726 | 0.9827 | 1.0 | 0.9913 | 0.9828 |
| No log | 2.0 | 332 | 0.0569 | 0.9827 | 1.0 | 0.9913 | 0.9828 |
| No log | 3.0 | 498 | 0.0434 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
| 0.1021 | 4.0 | 664 | 0.0505 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
| 0.1021 | 5.0 | 830 | 0.0472 | 0.9884 | 1.0 | 0.9942 | 0.9885 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
LACAI/roberta-large-adapted-PFG-progression
|
LACAI
| 2022-05-02T18:28:47Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T18:09:17Z |
---
license: mit
---
Base model: [lacai/roberta-large-dialog-narrative](https://huggingface.co/lacai/roberta-large-dialog-narrative)
Fine tuned as a progression model (to predict the acceptability of a dialogue) on the [Persuasion For Good Dataset](https://gitlab.com/ucdavisnlp/persuasionforgood) (Wang et al., 2019):
Given a complete dialogue from (or in the style of) Persuasion For Good, the task is to predict a numeric score typically in the range (-3, 3) where a higher score means a more acceptable dialogue in context of the donation solicitation task.
This model inherits a special dialogue token `<d>` from its base model, which indicates the start of a dialogue utterance.
**Example input**: `<d>How are you?</s><d>Good! how about yourself?</s><d>Great. Would you like to donate today to help the children?</s>`
For more context and usage information see [https://github.rpi.edu/LACAI/dialogue-progression](https://github.rpi.edu/LACAI/dialogue-progression).
|
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
|
ali2066
| 2022-05-02T18:27:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T18:24:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0703
- Precision: 0.9667
- Recall: 0.0505
- F1: 0.0961
- Accuracy: 0.0766
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 95 | 0.5442 | 0.6667 | 0.1132 | 0.1935 | 0.75 |
| No log | 2.0 | 190 | 0.5316 | 0.5385 | 0.1321 | 0.2121 | 0.74 |
| No log | 3.0 | 285 | 0.5384 | 0.4615 | 0.2264 | 0.3038 | 0.725 |
| No log | 4.0 | 380 | 0.5503 | 0.4286 | 0.2264 | 0.2963 | 0.715 |
| No log | 5.0 | 475 | 0.5529 | 0.4286 | 0.2264 | 0.2963 | 0.715 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
ali2066/DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
|
ali2066
| 2022-05-02T18:23:52Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T18:22:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERT_FINAL_ctxSentence_TRAIN_essays_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7321
- Precision: 0.9795
- Recall: 0.7277
- F1: 0.835
- Accuracy: 0.7208
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 130 | 0.3755 | 0.8521 | 0.9910 | 0.9163 | 0.8529 |
| No log | 2.0 | 260 | 0.3352 | 0.8875 | 0.9638 | 0.9241 | 0.8713 |
| No log | 3.0 | 390 | 0.3370 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
| 0.4338 | 4.0 | 520 | 0.3415 | 0.8957 | 0.9321 | 0.9135 | 0.8566 |
| 0.4338 | 5.0 | 650 | 0.3416 | 0.8918 | 0.9321 | 0.9115 | 0.8529 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
hassnain/wav2vec2-base-timit-demo-colab3000
|
hassnain
| 2022-05-02T17:34:38Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-02T12:25:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab3000
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab3000
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6852
- eval_wer: 0.3845
- eval_runtime: 71.297
- eval_samples_per_second: 9.846
- eval_steps_per_second: 1.234
- epoch: 24.22
- step: 8500
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
wpatena/PB-Chlamy
|
wpatena
| 2022-05-02T16:34:01Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-04-12T22:35:19Z |
These are files for the trained protein localization prediction model PB-Chlamy, created for the paper **"A Chloroplast Protein Atlas Reveals Novel Structures and Spatial Organization of Biosynthetic Pathways"** by Lianyong Wang, Weronika Patena, Kelly A. Van Baalen, Yihua Xie, Emily R. Singer, Sophia Gavrilenko, Michelle Warren-Williams, Linqu Han, Henry Harrigan, Vivian Chen, Vinh Ton, Saw Kyin, Henry H. Shwe, Matthew H. Cahn, Alexandra Wilson, Jianping Hu, Christoph Benning, Danny J. Schnell, Claire D. McWhite, Martin Jonikas (submitted for publication in May 2022).
|
espnet/farsi_commonvoice_blstm
|
espnet
| 2022-05-02T15:50:24Z | 5 | 3 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"fa",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-02T15:49:22Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: fa
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/farsi_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/farsi_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon May 2 11:48:56 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `716eb8f92e19708acfd08ba3bd39d40890d3a84b`
- Commit date: `Thu Apr 28 19:50:59 2022 -0400`
## asr_train_asr_rnn_raw_fa_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|68904|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|68904|91.4|7.2|1.4|1.0|9.5|30.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|331506|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|331506|97.2|1.3|1.5|0.7|3.6|30.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_fa|9728|230963|0.0|0.0|100.0|0.0|100.0|100.0|
|decode_rnn_asr_model_valid.acc.best/test_fa|9728|230963|95.9|2.4|1.6|0.7|4.7|30.1|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_raw_fa_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 15
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 30
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_fa_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_fa_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_fa_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_fa_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_fa_sp/wav.scp
- speech
- sound
- - dump/raw/train_fa_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_fa/wav.scp
- speech
- sound
- - dump/raw/dev_fa/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ی
- ا
- ه
- ▁
- ر
- م
- و
- د
- ت
- ش
- ن
- ل
- ▁ب
- ز
- ب
- .
- ▁م
- ان
- ▁ا
- س
- ک
- ▁می
- گ
- ف
- ▁د
- ؟
- ق
- ▁و
- ید
- ▁ن
- ند
- ست
- ار
- ▁چ
- ع
- ج
- ▁ت
- ▁ک
- ▁با
- خ
- ون
- ▁پ
- ▁به
- ▁من
- ▁س
- ▁را
- ،
- ▁خ
- ▁این
- ▁کن
- ▁آ
- ▁در
- ای
- ▁از
- اد
- ▁است
- ح
- ص
- ▁ش
- ط
- ▁تو
- ین
- ▁دار
- ▁که
- ال
- ▁رو
- ▁گ
- ▁ج
- ور
- ام
- ▁هم
- ▁ح
- فت
- رد
- یم
- پ
- غ
- چ
- ذ
- ض
- ظ
- '!'
- ث
- ً
- ئ
- '"'
- ژ
- ك
- آ
- ي
- ':'
- ى
- '-'
- ِ
- أ
- َ
- »
- ـ
- ','
- ُ
- (
- )
- ء
- ٔ
- ٬
- ّ
- ؛
- B
- C
- A
- E
- G
- M
- S
- ؤ
- I
- ;
- T
- H
- _
- F
- D
- ۀ
- Y
- N
- K
- U
- –
- ٌ
- P
- O
- Q
- Z
- '&'
- L
- R
- ة
- X
- ā
- '#'
- “
- '='
- «
- š
- ْ
- ے
- ”
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/fa_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_fa_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/greek_commonvoice_blstm
|
espnet
| 2022-05-02T15:35:07Z | 0 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"el",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-02T15:34:01Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: el
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/greek_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/greek_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Sun Apr 17 19:51:46 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_train_asr_rnn_tr_raw_el_bpe150_sp
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|10574|90.7|7.7|1.6|0.5|9.9|27.4|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|61731|96.6|1.5|1.9|0.6|4.0|27.5|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_el|1681|44869|95.7|2.4|1.9|0.7|5.0|27.5|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn_tr.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_train_asr_rnn_tr_raw_el_bpe150_sp
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_el_bpe150_sp/train/speech_shape
- exp/asr_stats_raw_el_bpe150_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_el_bpe150_sp/valid/speech_shape
- exp/asr_stats_raw_el_bpe150_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_el_sp/wav.scp
- speech
- sound
- - dump/raw/train_el_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_el/wav.scp
- speech
- sound
- - dump/raw/dev_el/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- ▁
- α
- ν
- ρ
- ι
- ε
- ο
- τ
- ς
- λ
- ά
- σ
- κ
- ό
- .
- ί
- ▁π
- έ
- ω
- π
- γ
- η
- μ
- υ
- ','
- ή
- ▁το
- χ
- θ
- ώ
- ▁και
- ▁του
- δ
- τα
- αν
- ει
- ▁να
- ▁σ
- ου
- σε
- ▁κ
- ύ
- ού
- φ
- στ
- ρα
- ια
- ▁μ
- ▁δ
- ▁έ
- τι
- β
- ρι
- μα
- πο
- εί
- ▁φ
- ▁με
- κα
- ▁α
- ος
- ;
- ▁χ
- '!'
- ▁β
- ες
- ▁στο
- τε
- ▁γ
- '"'
- τη
- ▁ο
- ▁Π
- ▁δε
- ▁που
- ▁μου
- με
- ▁τα
- σα
- λα
- Μ
- ιά
- ▁από
- εις
- ▁την
- έρ
- ▁ε
- ▁τον
- ρά
- λο
- ▁είπε
- ▁μα
- ψ
- Τ
- ''''
- Κ
- Σ
- Ε
- Α
- Θ
- '-'
- Η
- Ά
- Ν
- Δ
- Χ
- ’
- Ξ
- »
- Π
- ΐ
- Ώ
- Ο
- A
- O
- ·
- ':'
- E
- G
- H
- N
- R
- T
- V
- Υ
- ϋ
- Ψ
- ́
- ‘
- Ι
- Ί
- Ρ
- Ω
- «
- Ύ
- Ζ
- ϊ
- Ή
- Φ
- Λ
- Ό
- Γ
- Έ
- Β
- ζ
- M
- ξ
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/el_token_list/bpe_unigram150/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_el_bpe150_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
espnet/zh-CN_commonvoice_blstm
|
espnet
| 2022-05-02T15:27:21Z | 1 | 0 |
espnet
|
[
"espnet",
"audio",
"automatic-speech-recognition",
"dataset:commonvoice",
"arxiv:1804.00015",
"license:cc-by-4.0",
"region:us"
] |
automatic-speech-recognition
| 2022-05-02T15:24:05Z |
---
tags:
- espnet
- audio
- automatic-speech-recognition
language: zh-CN
datasets:
- commonvoice
license: cc-by-4.0
---
## ESPnet2 ASR model
### `espnet/zh-CN_commonvoice_blstm`
This model was trained by dzeinali using commonvoice recipe in [espnet](https://github.com/espnet/espnet/).
### Demo: How to use in ESPnet2
```bash
cd espnet
git checkout 716eb8f92e19708acfd08ba3bd39d40890d3a84b
pip install -e .
cd egs2/commonvoice/asr1
./run.sh --skip_data_prep false --skip_train true --download_model espnet/zh-CN_commonvoice_blstm
```
<!-- Generated by scripts/utils/show_asr_result.sh -->
# RESULTS
## Environments
- date: `Mon Apr 18 13:15:36 EDT 2022`
- python version: `3.9.5 (default, Jun 4 2021, 12:28:51) [GCC 7.5.0]`
- espnet version: `espnet 0.10.6a1`
- pytorch version: `pytorch 1.8.1+cu102`
- Git hash: `5e6e95d087af8a7a4c33c4248b75114237eae64b`
- Commit date: `Mon Apr 4 21:04:45 2022 -0400`
## asr_zh_lr_0.1_epoch_50
### WER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_zh_CN|9698|9698|14.9|85.1|0.0|0.0|85.1|85.1|
### CER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_zh_CN|9698|156383|79.4|19.5|1.1|0.8|21.4|85.1|
### TER
|dataset|Snt|Wrd|Corr|Sub|Del|Ins|Err|S.Err|
|---|---|---|---|---|---|---|---|---|
|decode_rnn_asr_model_valid.acc.ave/test_zh_CN|9698|166069|80.8|18.2|1.1|0.7|20.0|84.9|
## ASR config
<details><summary>expand</summary>
```
config: conf/tuning/train_asr_rnn_zh.yaml
print_config: false
log_level: INFO
dry_run: false
iterator_type: sequence
output_dir: exp/asr_zh_lr_0.1_epoch_50
ngpu: 1
seed: 0
num_workers: 1
num_att_plot: 3
dist_backend: nccl
dist_init_method: env://
dist_world_size: null
dist_rank: null
local_rank: 0
dist_master_addr: null
dist_master_port: null
dist_launcher: null
multiprocessing_distributed: false
unused_parameters: false
sharded_ddp: false
cudnn_enabled: true
cudnn_benchmark: false
cudnn_deterministic: true
collect_stats: false
write_collected_feats: false
max_epoch: 50
patience: 3
val_scheduler_criterion:
- valid
- loss
early_stopping_criterion:
- valid
- loss
- min
best_model_criterion:
- - train
- loss
- min
- - valid
- loss
- min
- - train
- acc
- max
- - valid
- acc
- max
keep_nbest_models:
- 10
nbest_averaging_interval: 0
grad_clip: 5.0
grad_clip_type: 2.0
grad_noise: false
accum_grad: 1
no_forward_run: false
resume: true
train_dtype: float32
use_amp: false
log_interval: null
use_matplotlib: true
use_tensorboard: true
use_wandb: false
wandb_project: null
wandb_id: null
wandb_entity: null
wandb_name: null
wandb_model_log_interval: -1
detect_anomaly: false
pretrain_path: null
init_param: []
ignore_init_mismatch: false
freeze_param: []
num_iters_per_epoch: null
batch_size: 16
valid_batch_size: null
batch_bins: 1000000
valid_batch_bins: null
train_shape_file:
- exp/asr_stats_raw_zh-CN_bpe4409_sp/train/speech_shape
- exp/asr_stats_raw_zh-CN_bpe4409_sp/train/text_shape.bpe
valid_shape_file:
- exp/asr_stats_raw_zh-CN_bpe4409_sp/valid/speech_shape
- exp/asr_stats_raw_zh-CN_bpe4409_sp/valid/text_shape.bpe
batch_type: folded
valid_batch_type: null
fold_length:
- 80000
- 150
sort_in_batch: descending
sort_batch: descending
multiple_iterator: false
chunk_length: 500
chunk_shift_ratio: 0.5
num_cache_chunks: 1024
train_data_path_and_name_and_type:
- - dump/raw/train_zh_CN_sp/wav.scp
- speech
- sound
- - dump/raw/train_zh_CN_sp/text
- text
- text
valid_data_path_and_name_and_type:
- - dump/raw/dev_zh_CN/wav.scp
- speech
- sound
- - dump/raw/dev_zh_CN/text
- text
- text
allow_variable_data_keys: false
max_cache_size: 0.0
max_cache_fd: 32
valid_max_cache_size: null
optim: adadelta
optim_conf:
lr: 0.1
scheduler: null
scheduler_conf: {}
token_list:
- <blank>
- <unk>
- 。
- ▁
- 的
- ','
- 是
- 为
- 在
- 人
- 和
- 中
- 大
- 科
- 有
- 尔
- 一
- 斯
- 了
- 种
- 于
- 县
- 与
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- 市
- 地
- 克
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- 不
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- 以
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- 名
- 会
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- 德
- 年
- 南
- 长
- 国
- 拉
- 多
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- 被
- 里
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- 部
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- 时
- 来
- 内
- 及
- 学
- 家
- 之
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- 所
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- 到
- 布
- 任
- 高
- 利
- 亚
- 对
- 省
- 分
- 文
- 由
- 体
- 用
- 主
- 北
- 新
- 安
- 生
- 法
- 天
- 公
- 巴
- 马
- 罗
- 者
- 成
- 都
- 而
- 三
- 道
- 十
- 线
- 小
- 原
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- 其
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- 水
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- 前
- 格
- 性
- 金
- 场
- 作
- 因
- 军
- 曾
- 加
- 代
- 明
- 此
- 林
- 等
- 同
- 王
- 平
- 兰
- 发
- 位
- 最
- 方
- 他
- 将
- 可
- 本
- 基
- 士
- 两
- 城
- 战
- 数
- 路
- 尼
- 日
- 花
- 球
- 氏
- 面
- 维
- 字
- 河
- 纳
- 动
- 合
- 机
- 卡
- 自
- 石
- 过
- 或
- 度
- 达
- 个
- 夫
- 化
- 民
- 力
- 伊
- 次
- 理
- 制
- 比
- 教
- 至
- 古
- 级
- 物
- 太
- 并
- 总
- 光
- 阿
- 正
- 事
- 叶
- 普
- 镇
- 门
- 党
- 车
- 电
- 四
- 色
- 着
- 府
- 瓦
- 开
- 台
- 能
- 建
- 汉
- ·
- 华
- 入
- 量
- 莱
- 相
- 书
- 女
- 得
- 改
- 六
- 龙
- 重
- 从
- 族
- 首
- 类
- 语
- 星
- 勒
- 该
- 美
- 点
- 还
- 黄
- 流
- 萨
- 雷
- 神
- 无
- 岛
- 塔
- 则
- 波
- 治
- 武
- 向
- 二
- 草
- 实
- 队
- 手
- 元
- 号
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- 嘧
- 嗓
- 啶
- 匍
- 剖
- 倭
- 乓
- 麝
- 鲫
- 鲡
- 骼
- 豌
- 蜕
- 蔽
- 葜
- 萸
- 菝
- 荸
- 舶
- 膀
- 聋
- 翡
- 羧
- 缆
- 绸
- 糟
- 箬
- 甥
- 牺
- 炀
- 溲
- 歙
- 橄
- 梧
- 柠
- 暑
- 幌
- 帜
- 奸
- 卉
- 匿
- 傣
- 倪
- 伺
- —
- 骚
- 隍
- 阐
- 跆
- 蝴
- 葶
- 苈
- 翌
- 縻
- 篷
- 皂
- 淆
- 榴
- 榧
- 楝
- 梢
- 晤
- 摧
- 惜
- 帘
- 寸
- 咕
- 厕
- 匝
- 釜
- 遮
- 谴
- 蝙
- 蜘
- 蜍
- 葫
- 茱
- 肪
- 肚
- 瑰
- 瀑
- 氰
- 歉
- 檬
- 桤
- 桌
- 斛
- 惧
- 垩
- 啤
- 啄
- 厢
- 兖
- .
- 鹦
- 镰
- 逍
- 螯
- 藜
- 蔬
- 莆
- 苋
- 芩
- 睛
- 溧
- 柽
- 斥
- 掘
- 挝
- 峭
- 厌
- 侠
- 乂
- 鼱
- 迄
- 藨
- 苹
- 竺
- 痕
- 椭
- 蛭
- 蒜
- 菩
- 玫
- 榈
- 棠
- 梵
- 寓
- 逮
- 趋
- 荠
- 罂
- 猩
- 槟
- 隧
- 蓼
- 荨
- 啡
- 粟
- 稣
- 氟
- 橡
- 柿
- 怖
- 蛱
- 胁
- 祯
- 硝
- 挪
- 尝
- 酋
- 薹
- 硫
- 蛄
- 粹
- 璃
- 爸
- 浏
- 咖
- 估
- 隋
- 柬
- 檗
- 蔷
- 碍
- 氨
- 蝽
- 玻
- 恢
- 矛
- 怎
- 疫
- 棕
- 娱
- 鳅
- 茛
- 蜱
- 矶
- 哺
- 械
- 堇
- 蚤
- 瑚
- 稳
- 苔
- 疆
- 乾
- 挥
- 孩
- 萄
- 藓
- 括
- <sos/eos>
init: null
input_size: null
ctc_conf:
dropout_rate: 0.0
ctc_type: builtin
reduce: true
ignore_nan_grad: true
joint_net_conf: null
model_conf:
ctc_weight: 0.5
use_preprocessor: true
token_type: bpe
bpemodel: data/zh-CN_token_list/bpe_unigram4409/bpe.model
non_linguistic_symbols: null
cleaner: null
g2p: null
speech_volume_normalize: null
rir_scp: null
rir_apply_prob: 1.0
noise_scp: null
noise_apply_prob: 1.0
noise_db_range: '13_15'
frontend: default
frontend_conf:
fs: 16k
specaug: specaug
specaug_conf:
apply_time_warp: true
time_warp_window: 5
time_warp_mode: bicubic
apply_freq_mask: true
freq_mask_width_range:
- 0
- 27
num_freq_mask: 2
apply_time_mask: true
time_mask_width_ratio_range:
- 0.0
- 0.05
num_time_mask: 2
normalize: global_mvn
normalize_conf:
stats_file: exp/asr_stats_raw_zh-CN_bpe4409_sp/train/feats_stats.npz
preencoder: null
preencoder_conf: {}
encoder: vgg_rnn
encoder_conf:
rnn_type: lstm
bidirectional: true
use_projection: true
num_layers: 4
hidden_size: 1024
output_size: 1024
postencoder: null
postencoder_conf: {}
decoder: rnn
decoder_conf:
num_layers: 2
hidden_size: 1024
sampling_probability: 0
att_conf:
atype: location
adim: 1024
aconv_chans: 10
aconv_filts: 100
required:
- output_dir
- token_list
version: 0.10.6a1
distributed: false
```
</details>
### Citing ESPnet
```BibTex
@inproceedings{watanabe2018espnet,
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
title={{ESPnet}: End-to-End Speech Processing Toolkit},
year={2018},
booktitle={Proceedings of Interspeech},
pages={2207--2211},
doi={10.21437/Interspeech.2018-1456},
url={http://dx.doi.org/10.21437/Interspeech.2018-1456}
}
```
or arXiv:
```bibtex
@misc{watanabe2018espnet,
title={ESPnet: End-to-End Speech Processing Toolkit},
author={Shinji Watanabe and Takaaki Hori and Shigeki Karita and Tomoki Hayashi and Jiro Nishitoba and Yuya Unno and Nelson Yalta and Jahn Heymann and Matthew Wiesner and Nanxin Chen and Adithya Renduchintala and Tsubasa Ochiai},
year={2018},
eprint={1804.00015},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
|
fahadtouseef/wav2vec2-base-timit-demo-colab_2
|
fahadtouseef
| 2022-05-02T14:18:38Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-05-02T11:50:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-colab_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-colab_2
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3801
- Wer: 0.3035
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.7227 | 3.52 | 500 | 2.6961 | 1.0 |
| 1.1237 | 7.04 | 1000 | 0.6088 | 0.5315 |
| 0.4886 | 10.56 | 1500 | 0.4709 | 0.4353 |
| 0.3148 | 14.08 | 2000 | 0.4341 | 0.3942 |
| 0.2229 | 17.61 | 2500 | 0.4035 | 0.3616 |
| 0.1693 | 21.13 | 3000 | 0.3868 | 0.3289 |
| 0.1393 | 24.65 | 3500 | 0.3993 | 0.3135 |
| 0.118 | 28.17 | 4000 | 0.3801 | 0.3035 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.10.3
|
waboucay/camembert-base-finetuned-xnli_fr-finetuned-nli-rua_wl
|
waboucay
| 2022-05-02T14:00:24Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T13:58:49Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 69.9 | 69.9 |
| test | 68.8 | 68.8 |
|
waboucay/camembert-base-finetuned-nli-rua_wl
|
waboucay
| 2022-05-02T13:54:59Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"text-classification",
"nli",
"fr",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T13:48:52Z |
---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 73.8 | 73.7 |
| test | 74.4 | 74.3 |
|
ali2066/DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
|
ali2066
| 2022-05-02T13:37:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T13:12:40Z |
---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# DistilBERTFINAL_ctxSentence_TRAIN_webDiscourse_TEST_NULL_second_train_set_null_False
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base](https://huggingface.co/cardiffnlp/twitter-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2555
- Precision: 1.0
- Recall: 0.0200
- F1: 0.0393
- Accuracy: 0.0486
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 95 | 0.5756 | nan | 0.0 | nan | 0.715 |
| No log | 2.0 | 190 | 0.5340 | 0.6429 | 0.1579 | 0.2535 | 0.735 |
| No log | 3.0 | 285 | 0.5298 | 0.5833 | 0.3684 | 0.4516 | 0.745 |
| No log | 4.0 | 380 | 0.5325 | 0.5789 | 0.3860 | 0.4632 | 0.745 |
| No log | 5.0 | 475 | 0.5452 | 0.4815 | 0.4561 | 0.4685 | 0.705 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu113
- Datasets 1.18.0
- Tokenizers 0.10.3
|
kSaluja/new-test-model2
|
kSaluja
| 2022-05-02T12:58:39Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-04-25T14:30:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: new-test-model2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# new-test-model2
This model is a fine-tuned version of [bert-large-uncased](https://huggingface.co/bert-large-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1040
- Precision: 0.9722
- Recall: 0.9757
- F1: 0.9739
- Accuracy: 0.9808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 151 | 0.1819 | 0.9360 | 0.9405 | 0.9382 | 0.9540 |
| No log | 2.0 | 302 | 0.1196 | 0.9637 | 0.9639 | 0.9638 | 0.9703 |
| No log | 3.0 | 453 | 0.1322 | 0.9614 | 0.9682 | 0.9648 | 0.9711 |
| 0.2764 | 4.0 | 604 | 0.1071 | 0.9677 | 0.9725 | 0.9701 | 0.9763 |
| 0.2764 | 5.0 | 755 | 0.1084 | 0.9709 | 0.9766 | 0.9737 | 0.9790 |
| 0.2764 | 6.0 | 906 | 0.1015 | 0.9717 | 0.9739 | 0.9728 | 0.9791 |
| 0.0342 | 7.0 | 1057 | 0.1208 | 0.9686 | 0.9727 | 0.9706 | 0.9785 |
| 0.0342 | 8.0 | 1208 | 0.1068 | 0.9680 | 0.9752 | 0.9716 | 0.9798 |
| 0.0342 | 9.0 | 1359 | 0.1028 | 0.9719 | 0.9743 | 0.9731 | 0.9807 |
| 0.0129 | 10.0 | 1510 | 0.1040 | 0.9722 | 0.9757 | 0.9739 | 0.9808 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
tomh/toxigen_hatebert
|
tomh
| 2022-05-02T12:42:51Z | 1,476 | 11 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"arxiv:2203.09509",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-01T13:02:09Z |
---
language:
- en
tags:
- text-classification
---
Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, Maarten Sap, Dipankar Ray, Ece Kamar.
This model comes from the paper [ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection](https://arxiv.org/abs/2203.09509) and can be used to detect implicit hate speech.
Please visit the [Github Repository](https://github.com/microsoft/TOXIGEN) for the training dataset and further details.
```bibtex
@inproceedings{hartvigsen2022toxigen,
title = "{T}oxi{G}en: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection",
author = "Hartvigsen, Thomas and Gabriel, Saadia and Palangi, Hamid and Sap, Maarten and Ray, Dipankar and Kamar, Ece",
booktitle = "Proceedings of the 60th Annual Meeting of the Association of Computational Linguistics",
year = "2022"
}
```
|
DioLiu/distilbert-base-uncased-finetuned-sst2-newdata
|
DioLiu
| 2022-05-02T12:40:09Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T12:18:04Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-sst2-newdata
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-sst2-newdata
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0588
- Accuracy: 0.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0543 | 1.0 | 1116 | 0.0307 | 0.9911 |
| 0.0235 | 2.0 | 2232 | 0.0372 | 0.9911 |
| 0.0102 | 3.0 | 3348 | 0.0486 | 0.9914 |
| 0.0003 | 4.0 | 4464 | 0.0563 | 0.9914 |
| 0.0008 | 5.0 | 5580 | 0.0588 | 0.9911 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1
|
madatnlp/kor-math-roberta-finetune
|
madatnlp
| 2022-05-02T11:44:14Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"roberta",
"fill-mask",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-04-30T11:16:10Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: madatnlp/kor-math-roberta-finetune
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# madatnlp/kor-math-roberta-finetune
This model is a fine-tuned version of [klue/roberta-base](https://huggingface.co/klue/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3205
- Validation Loss: 1.1407
- Epoch: 26
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 1e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4242 | 2.0873 | 0 |
| 1.9159 | 1.6264 | 1 |
| 1.5933 | 1.4521 | 2 |
| 1.3806 | 1.3584 | 3 |
| 1.2487 | 1.2904 | 4 |
| 1.1464 | 1.2388 | 5 |
| 1.0552 | 1.2076 | 6 |
| 0.9889 | 1.1818 | 7 |
| 0.9118 | 1.1607 | 8 |
| 0.8459 | 1.1349 | 9 |
| 0.7838 | 1.1193 | 10 |
| 0.7389 | 1.1193 | 11 |
| 0.6864 | 1.1080 | 12 |
| 0.6495 | 1.1001 | 13 |
| 0.6103 | 1.1001 | 14 |
| 0.5795 | 1.0990 | 15 |
| 0.5436 | 1.0954 | 16 |
| 0.5136 | 1.0997 | 17 |
| 0.4906 | 1.0954 | 18 |
| 0.4565 | 1.1021 | 19 |
| 0.4347 | 1.1075 | 20 |
| 0.4131 | 1.1075 | 21 |
| 0.3924 | 1.1220 | 22 |
| 0.3741 | 1.1298 | 23 |
| 0.3549 | 1.1352 | 24 |
| 0.3395 | 1.1286 | 25 |
| 0.3205 | 1.1407 | 26 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.8.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
nanopass/distilbert-base-uncased-emotion-2
|
nanopass
| 2022-05-02T09:43:02Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"jax",
"distilbert",
"text-classification",
"emotion",
"en",
"dataset:emotion",
"arxiv:1910.01108",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-05-02T09:42:09Z |
---
language:
- en
thumbnail: https://avatars3.githubusercontent.com/u/32437151?s=460&u=4ec59abc8d21d5feea3dab323d23a5860e6996a4&v=4
tags:
- text-classification
- emotion
- pytorch
license: apache-2.0
datasets:
- emotion
metrics:
- Accuracy, F1 Score
---
# Distilbert-base-uncased-emotion
## Model description:
[Distilbert](https://arxiv.org/abs/1910.01108) is created with knowledge distillation during the pre-training phase which reduces the size of a BERT model by 40%, while retaining 97% of its language understanding. It's smaller, faster than Bert and any other Bert-based model.
[Distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) finetuned on the emotion dataset using HuggingFace Trainer with below Hyperparameters
```
learning rate 2e-5,
batch size 64,
num_train_epochs=8,
```
## Model Performance Comparision on Emotion Dataset from Twitter:
| Model | Accuracy | F1 Score | Test Sample per Second |
| --- | --- | --- | --- |
| [Distilbert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/distilbert-base-uncased-emotion) | 93.8 | 93.79 | 398.69 |
| [Bert-base-uncased-emotion](https://huggingface.co/bhadresh-savani/bert-base-uncased-emotion) | 94.05 | 94.06 | 190.152 |
| [Roberta-base-emotion](https://huggingface.co/bhadresh-savani/roberta-base-emotion) | 93.95 | 93.97| 195.639 |
| [Albert-base-v2-emotion](https://huggingface.co/bhadresh-savani/albert-base-v2-emotion) | 93.6 | 93.65 | 182.794 |
## How to Use the model:
```python
from transformers import pipeline
classifier = pipeline("text-classification",model='bhadresh-savani/distilbert-base-uncased-emotion', return_all_scores=True)
prediction = classifier("I love using transformers. The best part is wide range of support and its easy to use", )
print(prediction)
"""
Output:
[[
{'label': 'sadness', 'score': 0.0006792712374590337},
{'label': 'joy', 'score': 0.9959300756454468},
{'label': 'love', 'score': 0.0009452480007894337},
{'label': 'anger', 'score': 0.0018055217806249857},
{'label': 'fear', 'score': 0.00041110432357527316},
{'label': 'surprise', 'score': 0.0002288572577526793}
]]
"""
```
## Dataset:
[Twitter-Sentiment-Analysis](https://huggingface.co/nlp/viewer/?dataset=emotion).
## Training procedure
[Colab Notebook](https://github.com/bhadreshpsavani/ExploringSentimentalAnalysis/blob/main/SentimentalAnalysisWithDistilbert.ipynb)
## Eval results
```json
{
'test_accuracy': 0.938,
'test_f1': 0.937932884041714,
'test_loss': 0.1472451239824295,
'test_mem_cpu_alloc_delta': 0,
'test_mem_cpu_peaked_delta': 0,
'test_mem_gpu_alloc_delta': 0,
'test_mem_gpu_peaked_delta': 163454464,
'test_runtime': 5.0164,
'test_samples_per_second': 398.69
}
```
## Reference:
* [Natural Language Processing with Transformer By Lewis Tunstall, Leandro von Werra, Thomas Wolf](https://learning.oreilly.com/library/view/natural-language-processing/9781098103231/)
|
driboune/skin_type
|
driboune
| 2022-05-02T08:08:40Z | 183 | 3 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-04-29T15:59:55Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: skin_type
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8222222328186035
---
# skin_type
Aiming for fairness in image classification for humans, knowing the skin type of subjects is relevant to make sure the model performs correctly on all skin types.
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### dark skin
#### light skin
|
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