modelId
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
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| likes
int64 0
11.7k
| library_name
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|---|---|---|---|---|---|---|---|---|---|
jonatasgrosman/exp_w2v2t_en_r-wav2vec2_s44
|
jonatasgrosman
| 2022-07-08T09:36:19Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:35:33Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_r-wav2vec2_s44
Fine-tuned [facebook/wav2vec2-large-robust](https://huggingface.co/facebook/wav2vec2-large-robust) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
epsil/testpyramidsrnd
|
epsil
| 2022-07-08T09:27:21Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-08T09:27:16Z |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: epsil/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
jonatasgrosman/exp_w2v2t_en_xls-r_s468
|
jonatasgrosman
| 2022-07-08T09:10:45Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T09:10:00Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xls-r_s468
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_xls-r_s957
|
jonatasgrosman
| 2022-07-08T08:54:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T08:54:25Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xls-r_s957
Fine-tuned [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_unispeech-sat_s456
|
jonatasgrosman
| 2022-07-08T08:26:50Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech-sat",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T08:26:01Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-sat_s456
Fine-tuned [microsoft/unispeech-sat-large](https://huggingface.co/microsoft/unispeech-sat-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-nl_s281
|
jonatasgrosman
| 2022-07-08T08:09:32Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T08:08:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-nl_s281
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-nl_s169
|
jonatasgrosman
| 2022-07-08T08:00:33Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:59:51Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-nl_s169
Fine-tuned [facebook/wav2vec2-large-nl-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-nl-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-es_s186
|
jonatasgrosman
| 2022-07-08T07:54:17Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:53:28Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s186
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-es_s474
|
jonatasgrosman
| 2022-07-08T07:45:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:44:40Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-es_s474
Fine-tuned [facebook/wav2vec2-large-es-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-es-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ClassCat/roberta-base-french
|
ClassCat
| 2022-07-08T07:34:58Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"fr",
"dataset:wikipedia",
"dataset:cc100",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-04T17:58:21Z |
---
language: fr
license: cc-by-sa-4.0
datasets:
- wikipedia
- cc100
widget:
- text: "Je vais à la <mask>."
- text: "J'aime le <mask>."
- text: "J'ai ouvert la <mask>."
- text: "Je m'appelle <mask>."
- text: "J'ai beaucoup d'<mask>."
---
## RoBERTa French base model (Uncased)
### Prerequisites
transformers==4.19.2
### Model architecture
This model uses RoBERTa base setttings except vocabulary size.
### Tokenizer
Using BPE tokenizer with vocabulary size 50,000.
### Training Data
* [wiki40b/fr](https://www.tensorflow.org/datasets/catalog/wiki40b#wiki40bfr) (French Wikipedia)
* Subset of [CC-100/fr](https://data.statmt.org/cc-100/) : Monolingual Datasets from Web Crawl Data
### Usage
```python
from transformers import pipeline
unmasker = pipeline('fill-mask', model='ClassCat/roberta-base-french')
unmasker("Je vais à la <mask>.")
```
|
jonatasgrosman/exp_w2v2t_en_vp-fr_s51
|
jonatasgrosman
| 2022-07-08T07:29:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:28:38Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s51
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-fr_s691
|
jonatasgrosman
| 2022-07-08T07:20:48Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:20:01Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-fr_s691
Fine-tuned [facebook/wav2vec2-large-fr-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-fr-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_unispeech-ml_s756
|
jonatasgrosman
| 2022-07-08T07:05:35Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T07:04:52Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-ml_s756
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_unispeech-ml_s103
|
jonatasgrosman
| 2022-07-08T06:58:31Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:57:42Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech-ml_s103
Fine-tuned [microsoft/unispeech-large-multi-lingual-1500h-cv](https://huggingface.co/microsoft/unispeech-large-multi-lingual-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_wavlm_s990
|
jonatasgrosman
| 2022-07-08T06:48:30Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:47:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s990
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_wavlm_s767
|
jonatasgrosman
| 2022-07-08T06:33:36Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wavlm",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:32:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wavlm_s767
Fine-tuned [microsoft/wavlm-large](https://huggingface.co/microsoft/wavlm-large) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-sv_s438
|
jonatasgrosman
| 2022-07-08T06:11:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:11:10Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s438
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-sv_s320
|
jonatasgrosman
| 2022-07-08T06:07:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:06:37Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s320
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_vp-sv_s179
|
jonatasgrosman
| 2022-07-08T06:02:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T06:01:42Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-sv_s179
Fine-tuned [facebook/wav2vec2-large-sv-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-sv-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
igpaub/Reinforce-Cartpole-v1
|
igpaub
| 2022-07-08T05:54:39Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T05:53:59Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Cartpole-v1
results:
- metrics:
- type: mean_reward
value: 500.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
jonatasgrosman/exp_w2v2t_en_hubert_s596
|
jonatasgrosman
| 2022-07-08T05:50:29Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:49:43Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_hubert_s596
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_hubert_s875
|
jonatasgrosman
| 2022-07-08T05:46:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"hubert",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:45:44Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_hubert_s875
Fine-tuned [facebook/hubert-large-ll60k](https://huggingface.co/facebook/hubert-large-ll60k) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_unispeech_s809
|
jonatasgrosman
| 2022-07-08T05:41:57Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"unispeech",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:41:08Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_unispeech_s809
Fine-tuned [microsoft/unispeech-large-1500h-cv](https://huggingface.co/microsoft/unispeech-large-1500h-cv) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
phyous/q-Taxi-v3-2
|
phyous
| 2022-07-08T05:32:54Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-08T05:27:28Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-2
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="phyous/q-Taxi-v3-2", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
jonatasgrosman/exp_w2v2t_en_xlsr-53_s279
|
jonatasgrosman
| 2022-07-08T05:26:47Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T05:26:21Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_xlsr-53_s279
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
ChauNguyen23/phobert-base-finetuned-imdb
|
ChauNguyen23
| 2022-07-08T05:03:20Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-08T04:47:50Z |
---
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: phobert-base-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. -->
# phobert-base-finetuned-imdb
This model is a fine-tuned version of [vinai/phobert-base](https://huggingface.co/vinai/phobert-base) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6149
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3266 | 1.0 | 157 | 2.7949 |
| 2.9162 | 2.0 | 314 | 2.6515 |
| 2.8177 | 3.0 | 471 | 2.6452 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonatasgrosman/exp_w2v2t_en_vp-100k_s364
|
jonatasgrosman
| 2022-07-08T04:56:51Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T04:56:25Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_vp-100k_s364
Fine-tuned [facebook/wav2vec2-large-100k-voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
jonatasgrosman/exp_w2v2t_en_wav2vec2_s878
|
jonatasgrosman
| 2022-07-08T03:56:34Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"en",
"dataset:mozilla-foundation/common_voice_7_0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-08T03:23:38Z |
---
language:
- en
license: apache-2.0
tags:
- automatic-speech-recognition
- en
datasets:
- mozilla-foundation/common_voice_7_0
---
# exp_w2v2t_en_wav2vec2_s878
Fine-tuned [facebook/wav2vec2-large-lv60](https://huggingface.co/facebook/wav2vec2-large-lv60) for speech recognition on English using the train split of [Common Voice 7.0](https://huggingface.co/datasets/mozilla-foundation/common_voice_7_0).
When using this model, make sure that your speech input is sampled at 16kHz.
This model has been fine-tuned by the [HuggingSound](https://github.com/jonatasgrosman/huggingsound) tool.
|
okho0653/Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
|
okho0653
| 2022-07-08T03:54:48Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-08T01:09:10Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-zero-shot-tokenizer-truncation-sentiment-model
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 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 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
ccarvajal-reyes/beto-emoji
|
ccarvajal-reyes
| 2022-07-08T03:35:39Z | 14 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-03T07:26:55Z |
---
language:
- es
---
# beto-emoji
Fine-tunning [BETO](https://github.com/dccuchile/beto) for emoji-prediction.
## Repository
Details with training and a use example are shown in [github.com/camilocarvajalreyes/beto-emoji](https://github.com/camilocarvajalreyes/beto-emoji). A deeper analysis of this and other models on the full dataset can be found in [github.com/furrutiav/data-mining-2022](https://github.com/furrutiav/data-mining-2022). We have used this model for a project for [CC5205 Data Mining](https://github.com/dccuchile/CC5205) course.
## Example
Inspired by model card from [cardiffnlp/twitter-roberta-base-emoji](https://huggingface.co/cardiffnlp/twitter-roberta-base-emoji).
```python
from transformers import AutoModelForSequenceClassification
from transformers import TFAutoModelForSequenceClassification
from transformers import AutoTokenizer
import numpy as np
from scipy.special import softmax
import csv
import urllib.request
# Preprocess text (username and link placeholders)
def preprocess(text):
new_text = []
for t in text.split(" "):
t = '@user' if t.startswith('@') and len(t) > 1 else t
t = 'http' if t.startswith('http') else t
new_text.append(t)
return " ".join(new_text)
MODEL = f"ccarvajal/beto-emoji"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
# download label mapping
labels=[]
mapping_link = f"https://raw.githubusercontent.com/camilocarvajalreyes/beto-emoji/main/es_mapping.txt"
with urllib.request.urlopen(mapping_link) as f:
html = f.read().decode('utf-8').split("\n")
csvreader = csv.reader(html, delimiter='\t')
labels = [row[1] for row in csvreader if len(row) > 1]
model = AutoModelForSequenceClassification.from_pretrained(MODEL)
model.save_pretrained(MODEL)
text = "que viva españa"
text = preprocess(text)
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
scores = output[0][0].detach().numpy()
scores = softmax(scores)
ranking = np.argsort(scores)
ranking = ranking[::-1]
for i in range(scores.shape[0]):
l = labels[ranking[i]]
s = scores[ranking[i]]
print(f"{i+1}) {l} {np.round(float(s), 4)}")
```
Output
```python
1) 🇪🇸 0.2508
2) 😍 0.238
3) 👌 0.2225
4) 😂 0.0806
5) ❤ 0.0489
6) 😁 0.0415
7) 😜 0.0232
8) 😎 0.0229
9) 😊 0.0156
10) 😉 0.0119
11) 💜 0.0079
12) 💕 0.0077
13) 💪 0.0066
14) 💘 0.0054
15) 💙 0.0052
16) 💞 0.005
17) 😘 0.0034
18) 🎶 0.0022
19) ✨ 0.0007
```
## Results in test set
precision recall f1-score support
❤ 0.39 0.43 0.41 2141
😍 0.29 0.39 0.33 1408
😂 0.51 0.51 0.51 1499
💕 0.09 0.05 0.06 352
😊 0.12 0.23 0.16 514
😘 0.24 0.23 0.24 397
💪 0.37 0.43 0.40 307
😉 0.15 0.17 0.16 453
👌 0.09 0.16 0.11 180
🇪🇸 0.46 0.46 0.46 424
😎 0.12 0.11 0.11 339
💙 0.36 0.02 0.04 413
💜 0.00 0.00 0.00 235
😜 0.04 0.02 0.02 274
💞 0.00 0.00 0.00 93
✨ 0.26 0.12 0.17 416
🎶 0.25 0.24 0.24 212
💘 0.00 0.00 0.00 134
😁 0.05 0.03 0.04 209
accuracy 0.30 10000
macro_avg 0.20 0.19 0.18 10000
weighted avg 0.29 0.30 0.29 10000
[Another example](https://github.com/camilocarvajalreyes/beto-emoji/blob/main/attention_visualisation.ipynb) with a visualisation of the attention modules within this model is carried out using [bertviz](https://github.com/jessevig/bertviz).
## Reproducibility
The Multilingual Emoji Prediction dataset (Barbieri et al. 2010) consists of tweets in English and Spanish that originally had a single emoji, which is later used as a tag. Test and trial sets can be downloaded [here](https://github.com/fvancesco/Semeval2018-Task2-Emoji-Detection/blob/master/dataset/Semeval2018-Task2-EmojiPrediction.zip?raw=true), but the train set needs to be downloaded using a [twitter crawler](https://github.com/fra82/twitter-crawler/blob/master/semeval2018task2TwitterCrawlerHOWTO.md). The goal is to predict that single emoji that was originally in the tweet using the text in it (out of a fixed set of possible emojis, 20 for English and 19 for Spanish).
Training parameters:
```python
training_args = TrainingArguments(
output_dir="./results",
learning_rate=2e-5,
per_device_train_batch_size=16,
per_device_eval_batch_size=16,
num_train_epochs=5,
weight_decay=0.01
)
```
|
ankitsharma/dummy-model
|
ankitsharma
| 2022-07-08T02:01:21Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"camembert",
"fill-mask",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-08T01:49:43Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: dummy-model
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# dummy-model
This model is a fine-tuned version of [camembert-base](https://huggingface.co/camembert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
katharsis/vqgan-imagenet-dnd
|
katharsis
| 2022-07-08T01:59:40Z | 0 | 0 | null |
[
"license:cc",
"region:us"
] | null | 2022-07-08T01:57:03Z |
---
license: cc
---
# D&D&VQGAN
## Intro
As I get a chance to play around with a lot more of these models. I find myself wanting to create D&D (or general fantasy and Sci-Fi themed images) generated from text prompt (think of what you see being implemented now in AI Dungeon).
|
sam34738/bert-hinglish
|
sam34738
| 2022-07-08T00:00:58Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T23:37:37Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-hinglish
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-hinglish
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5475
## 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: 4e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.3557 | 1.0 | 460 | 0.7714 |
| 0.6349 | 2.0 | 920 | 0.5475 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Tokenizers 0.12.1
|
huggingtweets/fairytale_bot23
|
huggingtweets
| 2022-07-07T21:44:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-07T21:43:08Z |
---
language: en
thumbnail: http://www.huggingtweets.com/fairytale_bot23/1657230245911/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/1486954631464771591/cwgDTNXD_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">Fairytale Generator</div>
<div style="text-align: center; font-size: 14px;">@fairytale_bot23</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 Fairytale Generator.
| Data | Fairytale Generator |
| --- | --- |
| Tweets downloaded | 315 |
| Retweets | 0 |
| Short tweets | 0 |
| Tweets kept | 315 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/lznwr8t9/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 @fairytale_bot23's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2hjhfq1n) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2hjhfq1n/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/fairytale_bot23')
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)
|
osanseviero/en_core_web_sm
|
osanseviero
| 2022-07-07T21:29:21Z | 6 | 0 |
spacy
|
[
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-07-07T21:28:43Z |
---
tags:
- spacy
- token-classification
language:
- en
license: mit
model-index:
- name: en_core_web_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8508041869
- name: NER Recall
type: recall
value: 0.8344851763
- name: NER F Score
type: f_score
value: 0.8425656714
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9726545475
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9180803841
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8996666011
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.9060200669
---
### Details: https://spacy.io/models/en#en_core_web_sm
English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_sm` |
| **Version** | `3.3.0` |
| **spaCy** | `>=3.3.0.dev0,<3.4.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br />[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br />[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (112 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.93 |
| `TOKEN_P` | 99.57 |
| `TOKEN_R` | 99.58 |
| `TOKEN_F` | 99.57 |
| `TAG_ACC` | 97.27 |
| `SENTS_P` | 91.89 |
| `SENTS_R` | 89.35 |
| `SENTS_F` | 90.60 |
| `DEP_UAS` | 91.81 |
| `DEP_LAS` | 89.97 |
| `ENTS_P` | 85.08 |
| `ENTS_R` | 83.45 |
| `ENTS_F` | 84.26 |
|
osanseviero/ca_core_news_sm
|
osanseviero
| 2022-07-07T21:23:31Z | 7 | 0 |
spacy
|
[
"spacy",
"token-classification",
"ca",
"license:gpl-3.0",
"model-index",
"region:us"
] |
token-classification
| 2022-07-07T21:22:35Z |
---
tags:
- spacy
- token-classification
language:
- ca
license: gpl-3.0
model-index:
- name: ca_core_news_sm
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.7934394284
- name: NER Recall
type: recall
value: 0.7903591071
- name: NER F Score
type: f_score
value: 0.7918962723
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.9810266317
- task:
name: POS
type: token-classification
metrics:
- name: POS (UPOS) Accuracy
type: accuracy
value: 0.9810266317
- task:
name: MORPH
type: token-classification
metrics:
- name: Morph (UFeats) Accuracy
type: accuracy
value: 0.9775079343
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.974386827
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.9141207189
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.8816511663
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.990348055
---
### Details: https://spacy.io/models/ca#ca_core_news_sm
Catalan pipeline optimized for CPU. Components: tok2vec, morphologizer, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `ca_core_news_sm` |
| **Version** | `3.3.0` |
| **spaCy** | `>=3.3.0.dev0,<3.4.0` |
| **Default Pipeline** | `tok2vec`, `morphologizer`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `morphologizer`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [UD Catalan AnCora v2.8](https://github.com/UniversalDependencies/UD_Catalan-AnCora) (Martínez Alonso, Héctor; Pascual, Elena; Zeman, Daniel)<br />[UD Catalan AnCora v2.8 + NER v3.2.8](https://github.com/TeMU-BSC/spacy/releases/tag/3.2.8) (Carlos Rodríguez-Penagos and Carme Armentano-Oller)<br />[Catalan Lemmatizer](https://github.com/explosion/spacy-lookups-data) (Text Mining Unit, Barcelona Supercomputing Center) |
| **License** | `GNU GPL 3.0` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (316 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`morphologizer`** | `Definite=Def\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Brck`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Brck`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=NOUN`, `POS=ADP`, `NumType=Card\|Number=Plur\|POS=NUM`, `Gender=Masc\|Number=Plur\|POS=NOUN`, `Number=Sing\|POS=ADJ`, `POS=CCONJ`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Ind`, `NumForm=Digit\|NumType=Card\|POS=NUM`, `NumForm=Digit\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Comm`, `POS=AUX\|VerbForm=Inf`, `Case=Acc,Dat\|POS=PRON\|Person=3\|PrepCase=Npr\|PronType=Prs\|Reflex=Yes`, `Definite=Def\|Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `POS=PRON\|PronType=Rel`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Definite=Def\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=NOUN`, `Gender=Fem\|Number=Plur\|POS=ADJ`, `POS=VERB\|VerbForm=Inf`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Peri`, `Number=Sing\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Sing\|POS=NOUN`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=2\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `POS=SCONJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Definite=Def\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=ADJ\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=VERB\|VerbForm=Ger`, `POS=NOUN`, `Gender=Fem\|NumType=Card\|Number=Sing\|POS=NUM`, `Gender=Fem\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Gender=Fem\|NumType=Ord\|Number=Plur\|POS=ADJ`, `POS=SYM`, `Gender=Masc\|Number=Sing\|POS=ADJ`, `Gender=Masc\|Number=Sing\|POS=ADJ\|VerbForm=Part`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Dem`, `POS=ADV\|Polarity=Neg`, `POS=ADV`, `Number=Sing\|POS=PRON\|PronType=Dem`, `Number=Sing\|POS=NOUN`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=NOUN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=ADJ`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Tot`, `Case=Loc\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Degree=Cmp\|POS=ADV`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Fem\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Ord\|Number=Sing\|POS=ADJ`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Plur\|POS=DET\|PronType=Ind`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Fut\|VerbForm=Fin`, `Gender=Masc\|NumType=Card\|Number=Sing\|POS=NUM`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=DET\|PronType=Ind`, `POS=PUNCT`, `Number=Sing\|POS=DET\|PronType=Rel`, `Case=Gen\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Ind`, `POS=AUX`, `Case=Acc\|Gender=Neut\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Degree=Cmp\|Number=Sing\|POS=ADJ`, `Number=Sing\|POS=VERB`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Dem`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Plur\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Fem,Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Rel`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `AdvType=Tim\|POS=NOUN`, `Gender=Masc\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Case=Acc\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Semi`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Dat\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|NumType=Card\|Number=Plur\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Ind`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `NumForm=Digit\|POS=SYM`, `Gender=Masc\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Sing\|POS=DET\|PronType=Int`, `POS=PRON\|PronType=Int`, `Gender=Fem\|Number=Plur\|POS=DET\|PronType=Int`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `POS=PART`, `Gender=Fem\|Number=Sing\|POS=PRON\|PronType=Dem`, `Gender=Masc\|Number=Sing\|POS=DET\|PronType=Tot`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Dem`, `POS=ADJ`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Degree=Cmp\|Number=Plur\|POS=ADJ`, `POS=PUNCT\|PunctType=Dash`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=VERB\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Gender=Masc\|POS=NOUN`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Int`, `Gender=Masc\|NumType=Ord\|Number=Plur\|POS=ADJ`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=PUNCT\|PunctType=Colo`, `Gender=Masc\|NumType=Card\|POS=NUM`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Number=Sing\|POS=PRON\|PronType=Int`, `POS=PUNCT\|PunctType=Quot`, `Mood=Imp\|Number=Sing\|POS=VERB\|Person=3\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `POS=AUX\|VerbForm=Ger`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Imp\|Number=Sing\|POS=AUX\|Person=3\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|PronType=Ind`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Dem`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Npr\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|PronType=Int`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `NumForm=Digit\|NumType=Frac\|POS=NUM`, `POS=VERB`, `Gender=Fem\|Number=Plur\|POS=PRON\|PronType=Dem`, `Gender=Fem\|POS=NOUN`, `Case=Acc,Dat\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Npr\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Fut\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=3\|Tense=Imp\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=1\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PronType=Prs`, `POS=X`, `Mood=Cnd\|Number=Plur\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Sing\|POS=DET\|PronType=Dem`, `POS=DET`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `NumType=Ord\|Number=Sing\|POS=ADJ`, `Gender=Fem\|Number=Sing\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|Number[psor]=Plur\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Masc\|Number=Plur\|POS=AUX\|Tense=Past\|VerbForm=Part`, `Number=Plur\|POS=PRON\|PronType=Dem`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `POS=PRON\|PronType=Ind`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Imp\|Number=Plur\|POS=VERB\|Person=3\|VerbForm=Fin`, `Case=Nom\|Number=Sing\|POS=PRON\|Person=1\|PronType=Prs`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=1\|PrepCase=Pre\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Qest`, `NumForm=Digit\|NumType=Ord\|POS=ADJ`, `Case=Acc\|POS=PRON\|Person=3\|PrepCase=Pre\|PronType=Prs\|Reflex=Yes`, `NumForm=Digit\|NumType=Frac\|POS=SYM`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Qest`, `NumType=Card\|Number=Sing\|POS=NUM`, `Foreign=Yes\|POS=PRON\|PronType=Int`, `Foreign=Yes\|Mood=Ind\|POS=VERB\|VerbForm=Fin`, `Foreign=Yes\|POS=ADP`, `Gender=Masc\|Number=Sing\|POS=PROPN`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Excl`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Excl`, `Mood=Cnd\|Number=Sing\|POS=AUX\|Person=1\|VerbForm=Fin`, `Number=Plur\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Mood=Sub\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=PUNCT\|PunctSide=Ini\|PunctType=Comm`, `POS=PUNCT\|PunctSide=Fin\|PunctType=Comm`, `Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc,Dat\|Number=Plur\|POS=PRON\|Person=2\|PronType=Prs`, `Mood=Cnd\|Number=Sing\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Cnd\|Number=Plur\|POS=VERB\|Person=1\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Definite=Ind\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Number=Sing\|POS=PRON\|Person=2\|Polite=Form\|PronType=Prs`, `Gender=Masc\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=1\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `POS=VERB\|Tense=Past\|VerbForm=Part`, `Mood=Imp\|Number=Plur\|POS=AUX\|Person=3\|VerbForm=Fin`, `Case=Nom\|POS=PRON\|Person=3\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Sing\|POS=PRON\|PronType=Rel`, `Definite=Ind\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|Number=Sing\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `Number=Plur\|Number[psor]=Plur\|POS=PRON\|Person=1\|Poss=Yes\|PronType=Prs`, `POS=AUX\|Tense=Past\|VerbForm=Part`, `Gender=Fem\|NumType=Card\|POS=NUM`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Plur\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Ind\|Number=Sing\|POS=AUX\|Person=1\|Tense=Fut\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=AUX\|Person=3\|Tense=Past\|VerbForm=Fin`, `AdvType=Tim\|Degree=Cmp\|POS=ADV`, `Case=Acc\|Number=Sing\|POS=PRON\|Person=2\|Polite=Infm\|PrepCase=Pre\|PronType=Prs`, `POS=DET\|PronType=Rel`, `Definite=Ind\|Gender=Fem\|Number=Plur\|POS=DET\|PronType=Art`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin`, `POS=INTJ`, `Mood=Sub\|Number=Sing\|POS=AUX\|Person=1\|Tense=Pres\|VerbForm=Fin`, `POS=VERB\|VerbForm=Fin`, `Mood=Ind\|Number=Plur\|POS=VERB\|Person=3\|Tense=Past\|VerbForm=Fin`, `Definite=Ind\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Mood=Sub\|Number=Plur\|POS=AUX\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=PRON\|Person=3\|Poss=Yes\|PronType=Prs`, `Mood=Sub\|Number=Sing\|POS=VERB\|Person=2\|Tense=Pres\|VerbForm=Fin`, `Case=Acc\|POS=PRON\|Person=3\|PronType=Prs\|Reflex=Yes`, `Foreign=Yes\|POS=NOUN`, `Foreign=Yes\|Mood=Ind\|Number=Sing\|POS=AUX\|Person=3\|Tense=Pres\|VerbForm=Fin`, `Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=PRON\|Person=3\|PronType=Prs`, `Foreign=Yes\|POS=SCONJ`, `Foreign=Yes\|Gender=Fem\|Number=Sing\|POS=DET\|PronType=Art`, `Gender=Masc\|POS=SYM`, `Gender=Fem\|Number=Sing\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Number=Sing\|POS=DET\|Person=3\|Poss=Yes\|PronType=Prs`, `Gender=Masc\|Number=Plur\|Number[psor]=Sing\|POS=DET\|Person=2\|Poss=Yes\|PronType=Prs`, `Gender=Fem\|Number=Sing\|POS=PROPN`, `Mood=Sub\|Number=Plur\|POS=VERB\|Person=1\|Tense=Imp\|VerbForm=Fin`, `Definite=Def\|Foreign=Yes\|Gender=Masc\|Number=Sing\|POS=DET\|PronType=Art`, `Foreign=Yes\|POS=VERB`, `Foreign=Yes\|POS=ADJ`, `Foreign=Yes\|POS=DET`, `Foreign=Yes\|POS=ADV`, `POS=PUNCT\|PunctSide=Fin\|Punta d'aignctType=Brck`, `Degree=Cmp\|POS=ADJ`, `AdvType=Tim\|POS=SYM`, `Number=Plur\|POS=DET\|PronType=Dem`, `Mood=Ind\|Number=Sing\|POS=VERB\|Person=2\|Tense=Fut\|VerbForm=Fin` |
| **`parser`** | `ROOT`, `acl`, `advcl`, `advmod`, `amod`, `appos`, `aux`, `case`, `cc`, `ccomp`, `compound`, `conj`, `cop`, `csubj`, `dep`, `det`, `expl:pass`, `fixed`, `flat`, `iobj`, `mark`, `nmod`, `nsubj`, `nummod`, `obj`, `obl`, `parataxis`, `punct`, `xcomp` |
| **`ner`** | `LOC`, `MISC`, `ORG`, `PER` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.97 |
| `TOKEN_P` | 99.78 |
| `TOKEN_R` | 99.79 |
| `TOKEN_F` | 99.79 |
| `POS_ACC` | 98.10 |
| `MORPH_ACC` | 97.75 |
| `MORPH_MICRO_P` | 99.37 |
| `MORPH_MICRO_R` | 98.67 |
| `MORPH_MICRO_F` | 99.02 |
| `SENTS_P` | 99.01 |
| `SENTS_R` | 99.06 |
| `SENTS_F` | 99.03 |
| `DEP_UAS` | 91.41 |
| `DEP_LAS` | 88.17 |
| `TAG_ACC` | 98.10 |
| `LEMMA_ACC` | 97.44 |
| `ENTS_P` | 79.34 |
| `ENTS_R` | 79.04 |
| `ENTS_F` | 79.19 |
|
kwmr/wav2vec2_japanese
|
kwmr
| 2022-07-07T20:33:05Z | 5 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-07T18:23:30Z |
## Wav2Vec2.0 XLSR-53 large model の日本語 Fine Tuning モデル
[facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53)を日本語用にFine Tuningしたモデル
## 使用データセット
- [Common Voice](https://commonvoice.mozilla.org/ja)
## 使い方
```python
from transformers import Wav2Vec2Processor, Wav2Vec2ForCTC
from datasets import load_dataset
import torch
# load model and processor
processor = Wav2Vec2Processor.from_pretrained("kwmr/wav2vec2_japanese")
model = Wav2Vec2ForCTC.from_pretrained("kwmr/wav2vec2_japanese")
```
|
phyous/q-FrozenLake-v1-4x4-noSlippery
|
phyous
| 2022-07-07T20:31:38Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T20:31:33Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="phyous/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Forkits/Reinforce-CartPole
|
Forkits
| 2022-07-07T20:30:43Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T21:06:43Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole
results:
- metrics:
- type: mean_reward
value: 95.30 +/- 33.98
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
PrimeQA/squad-v1-xlm-roberta-large
|
PrimeQA
| 2022-07-07T20:28:50Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"MRC",
"SQuAD 1.1",
"xlm-roberta-large",
"multilingual",
"arxiv:1606.05250",
"arxiv:1910.07475",
"arxiv:1910.11856",
"arxiv:1911.02116",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2022-07-07T19:46:24Z |
---
tags:
- MRC
- SQuAD 1.1
- xlm-roberta-large
language:
- multilingual
license: apache-2.0
---
# Model description
An XLM-RoBERTa reading comprehension model for [SQuAD 1.1](https://aclanthology.org/D16-1264/).
The model is initialized with [xlm-roberta-large](https://huggingface.co/xlm-roberta-large/) and fine-tuned on the [SQuAD 1.1 train data](https://huggingface.co/datasets/squad).
## Intended uses & limitations
You can use the raw model for the reading comprehension task. Biases associated with the pre-existing language model, xlm-roberta-large, that we used may be present in our fine-tuned model, squad-v1-xlm-roberta-large. This model is used for zero-shot decoding of [MLQA](https://huggingface.co/datasets/mlqa) and [XQuAD](https://huggingface.co/datasets/xquad) datasets.
## Usage
You can use this model directly with the [PrimeQA](https://github.com/primeqa/primeqa) pipeline for reading comprehension [squad.ipynb](https://github.com/primeqa/primeqa/blob/main/notebooks/mrc/squad.ipynb).
```bibtex
@article{2016arXiv160605250R,
author = {{Rajpurkar}, Pranav and {Zhang}, Jian and {Lopyrev},
Konstantin and {Liang}, Percy},
title = "{SQuAD: 100,000+ Questions for Machine Comprehension of Text}",
journal = {arXiv e-prints},
year = 2016,
eid = {arXiv:1606.05250},
pages = {arXiv:1606.05250},
archivePrefix = {arXiv},
eprint = {1606.05250},
}
```
```bibtex
@article{lewis2019mlqa,
title={MLQA: Evaluating Cross-lingual Extractive Question Answering},
author={Lewis, Patrick and Oguz, Barlas and Rinott, Ruty and Riedel, Sebastian and Schwenk, Holger},
journal={arXiv preprint arXiv:1910.07475},
year={2019}
}
```
```bibtex
@article{Artetxe:etal:2019,
author = {Mikel Artetxe and Sebastian Ruder and Dani Yogatama},
title = {On the cross-lingual transferability of monolingual representations},
journal = {CoRR},
volume = {abs/1910.11856},
year = {2019},
archivePrefix = {arXiv},
eprint = {1910.11856}
}
```
```bibtex
@article{DBLP:journals/corr/abs-1911-02116,
author = {Alexis Conneau and
Kartikay Khandelwal and
Naman Goyal and
Vishrav Chaudhary and
Guillaume Wenzek and
Francisco Guzm{\'{a}}n and
Edouard Grave and
Myle Ott and
Luke Zettlemoyer and
Veselin Stoyanov},
title = {Unsupervised Cross-lingual Representation Learning at Scale},
journal = {CoRR},
volume = {abs/1911.02116},
year = {2019},
url = {http://arxiv.org/abs/1911.02116},
eprinttype = {arXiv},
eprint = {1911.02116},
timestamp = {Mon, 11 Nov 2019 18:38:09 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-02116.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243
|
mbyanfei
| 2022-07-07T20:02:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain",
"en",
"dataset:mbyanfei/autotrain-data-amazon-shoe-reviews-classification",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T19:48:42Z |
---
tags: autotrain
language: en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- mbyanfei/autotrain-data-amazon-shoe-reviews-classification
co2_eq_emissions: 27.982443349742287
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1104340243
- CO2 Emissions (in grams): 27.982443349742287
## Validation Metrics
- Loss: 0.9584922790527344
- Accuracy: 0.5843
- Macro F1: 0.5801009597024507
- Micro F1: 0.5843
- Weighted F1: 0.5792137097243996
- Macro Precision: 0.5897236028586046
- Micro Precision: 0.5843
- Weighted Precision: 0.5896188517045103
- Macro Recall: 0.5857983081566331
- Micro Recall: 0.5843
- Weighted Recall: 0.5843
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("mbyanfei/autotrain-amazon-shoe-reviews-classification-1104340243", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
drhyrum/bert-tiny-torch-vuln
|
drhyrum
| 2022-07-07T19:17:08Z | 271 | 2 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"BERT",
"MNLI",
"NLI",
"transformer",
"pre-training",
"en",
"arxiv:1908.08962",
"arxiv:2110.01518",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-06-27T17:26:26Z |
---
language:
- en
license:
- mit
tags:
- BERT
- MNLI
- NLI
- transformer
- pre-training
---
The following model is a Pytorch pre-trained model obtained from converting Tensorflow checkpoint found in the [official Google BERT repository](https://github.com/google-research/bert).
This is one of the smaller pre-trained BERT variants, together with [bert-mini](https://huggingface.co/prajjwal1/bert-mini) [bert-small](https://huggingface.co/prajjwal1/bert-small) and [bert-medium](https://huggingface.co/prajjwal1/bert-medium). They were introduced in the study `Well-Read Students Learn Better: On the Importance of Pre-training Compact Models` ([arxiv](https://arxiv.org/abs/1908.08962)), and ported to HF for the study `Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics` ([arXiv](https://arxiv.org/abs/2110.01518)). These models are supposed to be trained on a downstream task.
If you use the model, please consider citing both the papers:
```
@misc{bhargava2021generalization,
title={Generalization in NLI: Ways (Not) To Go Beyond Simple Heuristics},
author={Prajjwal Bhargava and Aleksandr Drozd and Anna Rogers},
year={2021},
eprint={2110.01518},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@article{DBLP:journals/corr/abs-1908-08962,
author = {Iulia Turc and
Ming{-}Wei Chang and
Kenton Lee and
Kristina Toutanova},
title = {Well-Read Students Learn Better: The Impact of Student Initialization
on Knowledge Distillation},
journal = {CoRR},
volume = {abs/1908.08962},
year = {2019},
url = {http://arxiv.org/abs/1908.08962},
eprinttype = {arXiv},
eprint = {1908.08962},
timestamp = {Thu, 29 Aug 2019 16:32:34 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1908-08962.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Config of this model:
- `prajjwal1/bert-tiny` (L=2, H=128) [Model Link](https://huggingface.co/prajjwal1/bert-tiny)
Other models to check out:
- `prajjwal1/bert-mini` (L=4, H=256) [Model Link](https://huggingface.co/prajjwal1/bert-mini)
- `prajjwal1/bert-small` (L=4, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-small)
- `prajjwal1/bert-medium` (L=8, H=512) [Model Link](https://huggingface.co/prajjwal1/bert-medium)
Original Implementation and more info can be found in [this Github repository](https://github.com/prajjwal1/generalize_lm_nli).
Twitter: [@prajjwal_1](https://twitter.com/prajjwal_1)
|
quanxi/TESTppo-LunarLander-v2
|
quanxi
| 2022-07-07T19:12:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T19:11:34Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 104.87 +/- 85.47
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
loicmagne/pr_dataset_metadata
|
loicmagne
| 2022-07-07T19:06:41Z | 0 | 0 | null |
[
"pytorch",
"tensorboard",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"region:us"
] | null | 2022-07-07T19:06:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: pr_dataset_metadata
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: eval_accuracy
type: accuracy
value: 1.0
---
<!-- 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. -->
# pr_dataset_metadata
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: 0.6216
- eval_accuracy: 1.0
- eval_runtime: 0.4472
- eval_samples_per_second: 2.236
- eval_steps_per_second: 2.236
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: not_parallel
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.0.0
- Tokenizers 0.12.1
|
Mascariddu8/distilbert-base-uncased-finetuned-imdb
|
Mascariddu8
| 2022-07-07T17:47:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-07T17:34:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7086 | 1.0 | 157 | 2.4897 |
| 2.5796 | 2.0 | 314 | 2.4230 |
| 2.5269 | 3.0 | 471 | 2.4354 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bothrajat/dqn-SpaceInvadersNoFrameskip-v4
|
bothrajat
| 2022-07-07T16:38:53Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T16:33:10Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 274.50 +/- 31.50
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga bothrajat -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga bothrajat
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', True),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
gemasphi/laprador_untrained
|
gemasphi
| 2022-07-07T15:20:10Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-07-07T15:20:02Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# gemasphi/laprador_untrained
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('gemasphi/laprador_untrained')
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('gemasphi/laprador_untrained')
model = AutoModel.from_pretrained('gemasphi/laprador_untrained')
# 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=gemasphi/laprador_untrained)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 -->
|
juanna/gptdc
|
juanna
| 2022-07-07T15:13:14Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-07T11:22:26Z |
skt에서 만든 gptdc를 ainize 서비스를 이용해서 훈련시키고 huggingface에서 시뮬레이션 합니다
|
mmazuecos/Reinforce-Pixelcopter-PLE-v0
|
mmazuecos
| 2022-07-07T14:43:02Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T14:42:53Z |
---
tags:
- Pixelcopter-PLE-v0
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-Pixelcopter-PLE-v0
results:
- metrics:
- type: mean_reward
value: -2.70 +/- 0.46
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Pixelcopter-PLE-v0
type: Pixelcopter-PLE-v0
---
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
Mascariddu8/bert-finetuned-ner
|
Mascariddu8
| 2022-07-07T14:36:28Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-06T17:10:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9357296670531721
- name: Recall
type: recall
value: 0.9506900033658701
- name: F1
type: f1
value: 0.9431505133984472
- name: Accuracy
type: accuracy
value: 0.9857390946017542
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0639
- Precision: 0.9357
- Recall: 0.9507
- F1: 0.9432
- Accuracy: 0.9857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0847 | 1.0 | 1756 | 0.0636 | 0.9150 | 0.9387 | 0.9267 | 0.9840 |
| 0.0399 | 2.0 | 3512 | 0.0592 | 0.9302 | 0.9485 | 0.9393 | 0.9854 |
| 0.0201 | 3.0 | 5268 | 0.0639 | 0.9357 | 0.9507 | 0.9432 | 0.9857 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
gemasphi/laprador_trained
|
gemasphi
| 2022-07-07T14:25:10Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-07-07T14:25:03Z |
---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# gemasphi/laprador_trained
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('gemasphi/laprador_trained')
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('gemasphi/laprador_trained')
model = AutoModel.from_pretrained('gemasphi/laprador_trained')
# 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=gemasphi/laprador_trained)
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 -->
|
mmazuecos/Reinforce-CartPole-v1
|
mmazuecos
| 2022-07-07T14:16:52Z | 0 | 0 | null |
[
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T14:16:40Z |
---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- metrics:
- type: mean_reward
value: 78.00 +/- 15.28
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 5 of the Deep Reinforcement Learning Class: https://github.com/huggingface/deep-rl-class/tree/main/unit5
|
dminiotas05/distilbert-base-uncased-finetuned-ft500_6class600
|
dminiotas05
| 2022-07-07T13:23:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T12:40:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-ft500_6class600
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-ft500_6class600
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: 1.6317
- Accuracy: 0.35
- F1: 0.3327
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.5717 | 1.0 | 188 | 1.5375 | 0.3067 | 0.2820 |
| 1.4338 | 2.0 | 376 | 1.5354 | 0.3207 | 0.2824 |
| 1.3516 | 3.0 | 564 | 1.4852 | 0.3573 | 0.3287 |
| 1.2722 | 4.0 | 752 | 1.4997 | 0.366 | 0.3534 |
| 1.1923 | 5.0 | 940 | 1.5474 | 0.362 | 0.3454 |
| 1.1156 | 6.0 | 1128 | 1.5998 | 0.3547 | 0.3387 |
| 1.0522 | 7.0 | 1316 | 1.6154 | 0.3473 | 0.3316 |
| 1.0148 | 8.0 | 1504 | 1.6317 | 0.35 | 0.3327 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-2022-07-07
|
Zengwei
| 2022-07-07T13:03:44Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-07-07T07:51:32Z |
Introduction
See https://github.com/k2-fsa/icefall/pull/330
and https://github.com/k2-fsa/icefall/pull/452
It has random combiner inside.
Note: There is something wrong in the log file, which has been fixed in https://github.com/k2-fsa/icefall/pull/468.
|
Zengwei/icefall-asr-librispeech-pruned-transducer-stateless5-M-2022-07-07
|
Zengwei
| 2022-07-07T12:30:37Z | 0 | 0 | null |
[
"tensorboard",
"region:us"
] | null | 2022-07-07T10:17:44Z |
Introduction
See https://github.com/k2-fsa/icefall/pull/330
and https://github.com/k2-fsa/icefall/pull/452
It has random combiner inside.
|
Vikasbhandari/TRY
|
Vikasbhandari
| 2022-07-07T12:17:31Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-07T11:42:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: TRY
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. -->
# TRY
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: 0.4234
- eval_wer: 0.3884
- eval_runtime: 51.9275
- eval_samples_per_second: 32.353
- eval_steps_per_second: 4.044
- epoch: 7.03
- step: 3500
## 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 4.17.0
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
|
paola-md/recipe-roberta-is
|
paola-md
| 2022-07-07T11:53:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-07T08:40:25Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: recipe-roberta-is
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. -->
# recipe-roberta-is
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8382
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.334 | 1.0 | 961 | 1.1217 |
| 1.1638 | 2.0 | 1922 | 1.0369 |
| 1.0936 | 3.0 | 2883 | 0.9922 |
| 1.0503 | 4.0 | 3844 | 0.9606 |
| 1.0188 | 5.0 | 4805 | 0.9314 |
| 0.9953 | 6.0 | 5766 | 0.9256 |
| 0.9769 | 7.0 | 6727 | 0.9109 |
| 0.9599 | 8.0 | 7688 | 0.8978 |
| 0.9461 | 9.0 | 8649 | 0.8813 |
| 0.9377 | 10.0 | 9610 | 0.8777 |
| 0.9253 | 11.0 | 10571 | 0.8755 |
| 0.918 | 12.0 | 11532 | 0.8601 |
| 0.9112 | 13.0 | 12493 | 0.8541 |
| 0.9043 | 14.0 | 13454 | 0.8548 |
| 0.8984 | 15.0 | 14415 | 0.8470 |
| 0.8958 | 16.0 | 15376 | 0.8412 |
| 0.8914 | 17.0 | 16337 | 0.8345 |
| 0.8882 | 18.0 | 17298 | 0.8353 |
| 0.8871 | 19.0 | 18259 | 0.8344 |
| 0.8839 | 20.0 | 19220 | 0.8382 |
### Framework versions
- Transformers 4.19.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178
|
zhifei
| 2022-07-07T10:49:19Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain",
"unk",
"dataset:zhifei/autotrain-data-autotrain-chinese-title-summarization-9",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-07T10:48:04Z |
---
tags: autotrain
language: unk
widget:
- text: "I love AutoTrain 🤗"
datasets:
- zhifei/autotrain-data-autotrain-chinese-title-summarization-9
co2_eq_emissions: 1.565396518204961
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1101340178
- CO2 Emissions (in grams): 1.565396518204961
## Validation Metrics
- Loss: 0.00012778821110259742
- Rouge1: 29.2308
- Rouge2: 0.0
- RougeL: 29.2308
- RougeLsum: 29.2308
- Gen Len: 18.4462
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/zhifei/autotrain-autotrain-chinese-title-summarization-9-1101340178
```
|
Fulccrum/trainii_ac94u-label-classification
|
Fulccrum
| 2022-07-07T10:48:17Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-07-07T10:48:16Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on trainii_ac94u to apply classification on label
**Metrics of the best model:**
accuracy 0.361046
recall_macro 0.353192
precision_macro 0.240667
f1_macro 0.278231
Name: LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-9 {color: black;background-color: white;}#sk-container-id-9 pre{padding: 0;}#sk-container-id-9 div.sk-toggleable {background-color: white;}#sk-container-id-9 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-9 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-9 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-9 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-9 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-9 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-9 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-9 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-9 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-9 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-9 div.sk-item {position: relative;z-index: 1;}#sk-container-id-9 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-9 div.sk-item::before, #sk-container-id-9 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-9 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-9 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-9 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-9 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-9 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-9 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-9 div.sk-label-container {text-align: center;}#sk-container-id-9 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-9 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-9" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-27" type="checkbox" ><label for="sk-estimator-id-27" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])),('logisticregression',LogisticRegression(C=0.1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-28" type="checkbox" ><label for="sk-estimator-id-28" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
id True False False ... False False False
text False False False ... False True False[2 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-29" type="checkbox" ><label for="sk-estimator-id-29" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=0.1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
TestZee/t5-small-finetuned-custom-wion-test-BIG
|
TestZee
| 2022-07-07T10:31:54Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-07T10:30:30Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TestZee/t5-small-finetuned-custom-wion-test-BIG
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. -->
# TestZee/t5-small-finetuned-custom-wion-test-BIG
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1165
- Validation Loss: 0.4609
- Epoch: 29
## 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': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.9622 | 0.8875 | 0 |
| 1.9276 | 0.8601 | 1 |
| 1.8301 | 0.8342 | 2 |
| 1.7776 | 0.8104 | 3 |
| 1.7345 | 0.7878 | 4 |
| 1.7733 | 0.7660 | 5 |
| 1.5626 | 0.7448 | 6 |
| 1.6111 | 0.7245 | 7 |
| 1.6754 | 0.7050 | 8 |
| 1.5030 | 0.6867 | 9 |
| 1.5101 | 0.6696 | 10 |
| 1.4328 | 0.6536 | 11 |
| 1.4311 | 0.6383 | 12 |
| 1.3917 | 0.6232 | 13 |
| 1.4102 | 0.6071 | 14 |
| 1.3732 | 0.5948 | 15 |
| 1.3468 | 0.5828 | 16 |
| 1.2817 | 0.5712 | 17 |
| 1.2920 | 0.5600 | 18 |
| 1.2696 | 0.5491 | 19 |
| 1.2552 | 0.5385 | 20 |
| 1.1859 | 0.5285 | 21 |
| 1.1995 | 0.5188 | 22 |
| 1.1690 | 0.5094 | 23 |
| 1.1678 | 0.5003 | 24 |
| 1.1420 | 0.4916 | 25 |
| 1.0959 | 0.4830 | 26 |
| 1.0848 | 0.4750 | 27 |
| 1.1248 | 0.4677 | 28 |
| 1.1165 | 0.4609 | 29 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
hugginglearners/malayalam-blurr-xlm-roberta-base
|
hugginglearners
| 2022-07-07T10:17:28Z | 0 | 2 |
fastai
|
[
"fastai",
"text-generation",
"ml",
"dataset:rajeshradhakrishnan/malayalam_wiki",
"region:us"
] |
text-generation
| 2022-07-06T11:10:26Z |
---
tags:
- fastai
- text-generation
language: ml
widget:
- text: "ഓഹരി വിപണി തകരുമ്പോള് നിക്ഷേപം എങ്ങനെ സുരക്ഷിതമാക്കാം"
example_title: "Malayalam Casual Language Model"
datasets:
- rajeshradhakrishnan/malayalam_wiki
---
# Blurr x Casual Machine Learning Model trained on Malayalam (മലയാളം) text. (Working in Progress)
[](https://nbviewer.org/github/rajeshradhakrishnanmvk/kitchen2.0/blob/main/ml/malayalam_blurr_xlm_roberta_base.ipynb)
---
# malayalam-blurr-xlm-roberta-base (base-sized model)
malayalam-blurr-xlm-roberta-base model is pre-trained on [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) using the library [blurr](https://ohmeow.github.io/blurr/) Language Model using fastai x huggingface frameworks.
Ref: [Causal Language Modeling](https://ohmeow.github.io/blurr/text-modeling-language-modeling.html#Causal-language-modeling).
## Usage
```
!pip install -Uqq huggingface_hub["fastai"] ohmeow-blurr
from huggingface_hub import from_pretrained_fastai
learner = from_pretrained_fastai(repo_id)
learner.blurr_generate("ബ്ളൂർ പഠിക്കാൻ വളെരെ എളുപ്പമാണ് എന്തുകൊണ്ട് എന്നാൽ", max_length=50, do_sample=True, top_k=25)
```
## Intended uses & limitations
It's not fine tuned to the state of the art accuracy
## Training and evaluation data
[Wiki 2020 Malayalam Dataset ](https://huggingface.co/datasets/rajeshradhakrishnan/malayalam_wiki)
|
osanseviero/ppo-LunarLander-v11
|
osanseviero
| 2022-07-07T09:43:04Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T09:42:42Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -115.46 +/- 0.00
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
huggingtweets/marsajal
|
huggingtweets
| 2022-07-07T09:42:16Z | 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/marsajal/1657186931820/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/1463196823728771079/wZc0m7cd_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">ajeng🦦</div>
<div style="text-align: center; font-size: 14px;">@marsajal</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 ajeng🦦.
| Data | ajeng🦦 |
| --- | --- |
| Tweets downloaded | 214 |
| Retweets | 37 |
| Short tweets | 41 |
| Tweets kept | 136 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3kdiymty/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 @marsajal's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/lfk0v9ey/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/marsajal')
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)
|
osanseviero/ppo-LunarLander-v9
|
osanseviero
| 2022-07-07T09:37:00Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T09:36:34Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -30.40 +/- 0.00
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
osanseviero/ppo-LunarLander-v6
|
osanseviero
| 2022-07-07T09:29:20Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T09:07:08Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -443.18 +/- 0.00
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
|
gary109
| 2022-07-07T09:10:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"gary109/AI_Light_Dance",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-01T03:42:00Z |
---
license: apache-2.0
tags:
- automatic-speech-recognition
- gary109/AI_Light_Dance
- generated_from_trainer
model-index:
- name: ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
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. -->
# ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8797
- Wer: 0.5513
## 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-06
- 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
- lr_scheduler_warmup_steps: 100
- num_epochs: 30.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9613 | 1.0 | 2309 | 1.0171 | 0.7271 |
| 0.8254 | 2.0 | 4618 | 0.9771 | 0.6650 |
| 0.7406 | 3.0 | 6927 | 0.9174 | 0.6420 |
| 0.74 | 4.0 | 9236 | 0.9551 | 0.6371 |
| 0.5855 | 5.0 | 11545 | 0.9262 | 0.6453 |
| 0.5536 | 6.0 | 13854 | 0.9056 | 0.5894 |
| 0.505 | 7.0 | 16163 | 0.9166 | 0.6029 |
| 0.449 | 8.0 | 18472 | 0.8816 | 0.5873 |
| 0.4219 | 9.0 | 20781 | 0.8970 | 0.5589 |
| 0.5764 | 10.0 | 23090 | 0.9189 | 0.5649 |
| 0.5075 | 11.0 | 25399 | 0.8797 | 0.5513 |
| 0.4366 | 12.0 | 27708 | 0.9011 | 0.5567 |
| 0.4915 | 13.0 | 30017 | 0.9248 | 0.5455 |
| 0.3554 | 14.0 | 32326 | 0.9309 | 0.5374 |
| 0.3975 | 15.0 | 34635 | 0.9103 | 0.5259 |
| 0.4119 | 16.0 | 36944 | 0.9402 | 0.5290 |
| 0.267 | 17.0 | 39253 | 0.9479 | 0.5115 |
| 0.3107 | 18.0 | 41562 | 0.9428 | 0.5099 |
| 0.2684 | 19.0 | 43871 | 0.9508 | 0.5133 |
| 0.2125 | 20.0 | 46180 | 0.9737 | 0.5097 |
| 0.3149 | 21.0 | 48489 | 0.9992 | 0.5095 |
| 0.2313 | 22.0 | 50798 | 1.0037 | 0.5059 |
| 0.2674 | 23.0 | 53107 | 1.0091 | 0.5040 |
| 0.2056 | 24.0 | 55416 | 1.0082 | 0.5076 |
| 0.2781 | 25.0 | 57725 | 1.0160 | 0.5015 |
| 0.2005 | 26.0 | 60034 | 1.0390 | 0.5131 |
| 0.2221 | 27.0 | 62343 | 1.0401 | 0.5074 |
| 0.1857 | 28.0 | 64652 | 1.0484 | 0.5096 |
| 0.1562 | 29.0 | 66961 | 1.0516 | 0.5064 |
| 0.3027 | 30.0 | 69270 | 1.0543 | 0.5049 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
m-newhauser/distilbert-political-tweets
|
m-newhauser
| 2022-07-07T09:07:44Z | 75 | 23 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"en",
"dataset:m-newhauser/senator-tweets",
"license:lgpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-03-02T23:29:05Z |
---
language:
- en
license: lgpl-3.0
library_name: transformers
tags:
- text-classification
- transformers
- pytorch
- generated_from_keras_callback
metrics:
- accuracy
- f1
datasets:
- m-newhauser/senator-tweets
widget:
- text: "This pandemic has shown us clearly the vulgarity of our healthcare system. Highest costs in the world, yet not enough nurses or doctors. Many millions uninsured, while insurance company profits soar. The struggle continues. Healthcare is a human right. Medicare for all."
example_title: "Bernie Sanders (D)"
- text: "Team Biden would rather fund the Ayatollah's Death to America regime than allow Americans to produce energy for our own domestic consumption."
example_title: "Ted Cruz (R)"
---
# distilbert-political-tweets 🗣 🇺🇸
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the [m-newhauser/senator-tweets](https://huggingface.co/datasets/m-newhauser/senator-tweets) dataset, which contains all tweets made by United States senators during the first year of the Biden Administration.
It achieves the following results on the evaluation set:
* Accuracy: 0.9076
* F1: 0.9117
## Model description
The goal of this model is to classify short pieces of text as having either Democratic or Republican sentiment. The model was fine-tuned on 99,693 tweets (51.6% Democrat, 48.4% Republican) made by US senators in 2021.
Model accuracy may not hold up on pieces of text longer than a tweet.
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: Adam
- training_precision: float32
- learning_rate = 5e-5
- num_epochs = 5
### Framework versions
- Transformers 4.16.2
- TensorFlow 2.8.0
- Datasets 1.18.3
- Tokenizers 0.11.6
|
kalyanavirundhubiryani/Best-Biryani-in-Chennai-Kalyana-virundhu-Biryani
|
kalyanavirundhubiryani
| 2022-07-07T09:07:31Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-07-07T08:48:29Z |
Kalyana Virundhu Biryani is one of the best biryani in Chennai." We Serve various types of Biryani along with our special side-Dish. Order us"Phone: +91 8939234566 or visit our website
https://www.kalyanavirundhubiryani.com/
#biryanifamousinchennai
#biryanibestinchennai
#chennaibestbiryanihotel
#specialbiryaniinchennai
#KalyanaVirundhuBiryani
|
osanseviero/ppo-LunarLander-v5
|
osanseviero
| 2022-07-07T08:59:26Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T08:47:49Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -479.21 +/- 0.00
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
osanseviero/ppo-LunarLander-v4
|
osanseviero
| 2022-07-07T08:47:35Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-05T19:12:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: -247.76 +/- 0.00
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
avichr/Legal-heBERT_ft
|
avichr
| 2022-07-07T07:31:58Z | 28 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"arxiv:1911.03090",
"arxiv:2010.02559",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-05-05T06:49:36Z |
# Legal-HeBERT
Legal-HeBERT is a BERT model for Hebrew legal and legislative domains. It is intended to improve the legal NLP research and tools development in Hebrew. We release two versions of Legal-HeBERT. The first version is a fine-tuned model of [HeBERT](https://github.com/avichaychriqui/HeBERT) applied on legal and legislative documents. The second version uses [HeBERT](https://github.com/avichaychriqui/HeBERT)'s architecture guidlines to train a BERT model from scratch. <br>
We continue collecting legal data, examining different architectural designs, and performing tagged datasets and legal tasks for evaluating and to development of a Hebrew legal tools.
## Training Data
Our training datasets are:
| Name | Hebrew Description | Size (GB) | Documents | Sentences | Words | Notes |
|----------------------------------------------------------------------------------------------------------------------------------- |-------------------------------------------------------------------------- |----------- |----------- |------------ |------------- |----------------------------------------- |
| The Israeli Law Book | ספר החוקים הישראלי | 0.05 | 2338 | 293352 | 4851063 | |
| Judgments of the Supreme Court | מאגר פסקי הדין של בית המשפט העליון | 0.7 | 212348 | 5790138 | 79672415 | |
| custody courts | החלטות בתי הדין למשמורת | 2.46 | 169,708 | 8,555,893 | 213,050,492 | |
| Law memoranda, drafts of secondary legislation and drafts of support tests that have been distributed to the public for comment | תזכירי חוק, טיוטות חקיקת משנה וטיוטות מבחני תמיכה שהופצו להערות הציבור | 0.4 | 3,291 | 294,752 | 7,218,960 | |
| Supervisors of Land Registration judgments | מאגר פסקי דין של המפקחים על רישום המקרקעין | 0.02 | 559 | 67,639 | 1,785,446 | |
| Decisions of the Labor Court - Corona | מאגר החלטות בית הדין לעניין שירות התעסוקה – קורונה | 0.001 | 146 | 3505 | 60195 | |
| Decisions of the Israel Lands Council | החלטות מועצת מקרקעי ישראל | | 118 | 11283 | 162692 | aggregate file |
| Judgments of the Disciplinary Tribunal and the Israel Police Appeals Tribunal | פסקי דין של בית הדין למשמעת ובית הדין לערעורים של משטרת ישראל | 0.02 | 54 | 83724 | 1743419 | aggregate files |
| Disciplinary Appeals Committee in the Ministry of Health | ועדת ערר לדין משמעתי במשרד הבריאות | 0.004 | 252 | 21010 | 429807 | 465 files are scanned and didn't parser |
| Attorney General's Positions | מאגר התייצבויות היועץ המשפטי לממשלה | 0.008 | 281 | 32724 | 813877 | |
| Legal-Opinion of the Attorney General | מאגר חוות דעת היועץ המשפטי לממשלה | 0.002 | 44 | 7132 | 188053 | |
| | | | | | | |
| total | | 3.665 | 389,139 | 15,161,152 | 309,976,419 | |
We thank <b>Yair Gardin</b> for the referring to the governance data, <b>Elhanan Schwarts</b> for collecting and parsing The Israeli law book, and <b>Jonathan Schler</b> for collecting the judgments of the supreme court.
## Training process
* Vocabulary size: 50,000 tokens
* 4 epochs (1M steps±)
* lr=5e-5
* mlm_probability=0.15
* batch size = 32 (for each gpu)
* NVIDIA GeForce RTX 2080 TI + NVIDIA GeForce RTX 3090 (1 week training)
### Additional training settings:
<b>Fine-tuned [HeBERT](https://github.com/avichaychriqui/HeBERT) model:</b> The first eight layers were freezed (like [Lee et al. (2019)](https://arxiv.org/abs/1911.03090) suggest)<br>
<b>Legal-HeBERT trained from scratch:</b> The training process is similar to [HeBERT](https://github.com/avichaychriqui/HeBERT) and inspired by [Chalkidis et al. (2020)](https://arxiv.org/abs/2010.02559) <br>
## How to use
The models can be found in huggingface hub and can be fine-tunned to any down-stream task:
```
# !pip install transformers==4.14.1
from transformers import AutoTokenizer, AutoModel
model_name = 'avichr/Legal-heBERT_ft' # for the fine-tuned HeBERT model
model_name = 'avichr/Legal-heBERT' # for legal HeBERT model trained from scratch
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model=model_name,
)
fill_mask("הקורונה לקחה את [MASK] ולנו לא נשאר דבר.")
```
## Stay tuned!
We are still working on our models and the datasets. We will edit this page as we progress. We are open for collaborations.
## If you used this model please cite us as :
Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai, Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts (June 27, 2022). Available at: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4147127
```
@article{chriqui2021hebert,
title={Legal HeBERT: A BERT-based NLP Model for Hebrew Legal, Judicial and Legislative Texts},
author={Chriqui, Avihay, Yahav, Inbal and Bar-Siman-Tov, Ittai},
journal={SSRN preprint:4147127},
year={2022}
}
```
## Contact us
[Avichay Chriqui](mailto:avichayc@mail.tau.ac.il), The Coller AI Lab <br>
[Inbal yahav](mailto:inbalyahav@tauex.tau.ac.il), The Coller AI Lab <br>
[Ittai Bar-Siman-Tov](mailto:Ittai.Bar-Siman-Tov@biu.ac.il), the BIU Innovation Lab for Law, Data-Science and Digital Ethics <br>
Thank you, תודה, شكرا <br>
|
go2k/q-FrozenLake-v1-4x4-noSlippery
|
go2k
| 2022-07-07T05:26:00Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-07T05:25:54Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="go2k/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Evelyn18/distilbert-base-uncased-becasv2-5
|
Evelyn18
| 2022-07-07T04:25:27Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-07T04:20:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becasv2-5
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-becasv2-5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0409
## 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: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 5.3475 |
| No log | 2.0 | 12 | 4.6045 |
| No log | 3.0 | 18 | 4.1832 |
| No log | 4.0 | 24 | 3.8223 |
| No log | 5.0 | 30 | 3.4798 |
| No log | 6.0 | 36 | 3.2615 |
| No log | 7.0 | 42 | 3.1414 |
| No log | 8.0 | 48 | 3.1067 |
| No log | 9.0 | 54 | 2.9950 |
| No log | 10.0 | 60 | 2.9482 |
| No log | 11.0 | 66 | 2.9536 |
| No log | 12.0 | 72 | 3.0180 |
| No log | 13.0 | 78 | 3.0515 |
| No log | 14.0 | 84 | 3.0444 |
| No log | 15.0 | 90 | 3.0409 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
qanastek/pos-french-camembert-flair
|
qanastek
| 2022-07-06T23:49:12Z | 52 | 3 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"fr",
"dataset:qanastek/ANTILLES",
"arxiv:1911.03894",
"arxiv:1011.4088",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: fr
datasets:
- qanastek/ANTILLES
widget:
- text: "George Washington est allé à Washington"
---
# POET: A French Extended Part-of-Speech Tagger
- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
- Embeddings: [Flair](https://aclanthology.org/C18-1139.pdf) & [CamemBERT](https://arxiv.org/abs/1911.03894)
- Sequence Labelling: [Bi-LSTM-CRF](https://arxiv.org/abs/1011.4088)
- Number of Epochs: 50
**People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
* [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2)
**Affiliations**
1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France.
## Demo: How to use in Flair
Requires [Flair](https://pypi.org/project/flair/): ```pip install flair```
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# Load the model
model = SequenceTagger.load("qanastek/pos-french")
sentence = Sentence("George Washington est allé à Washington")
# predict tags
model.predict(sentence)
# print predicted pos tags
print(sentence.to_tagged_string())
```
Output:
## Training data
`ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb).
Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora.
We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001.
The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html).
Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive.
## Original Tags
```plain
PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ
```
## New additional POS tags
| Abbreviation | Description | Examples |
|:--------:|:--------:|:--------:|
| PREP | Preposition | de |
| AUX | Auxiliary Verb | est |
| ADV | Adverb | toujours |
| COSUB | Subordinating conjunction | que |
| COCO | Coordinating Conjunction | et |
| PART | Demonstrative particle | -t |
| PRON | Pronoun | qui ce quoi |
| PDEMMS | Demonstrative Pronoun - Singular Masculine | ce |
| PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux |
| PDEMFS | Demonstrative Pronoun - Singular Feminine | cette |
| PDEMFP | Demonstrative Pronoun - Plural Feminine | celles |
| PINDMS | Indefinite Pronoun - Singular Masculine | tout |
| PINDMP | Indefinite Pronoun - Plural Masculine | autres |
| PINDFS | Indefinite Pronoun - Singular Feminine | chacune |
| PINDFP | Indefinite Pronoun - Plural Feminine | certaines |
| PROPN | Proper noun | Houston |
| XFAMIL | Last name | Levy |
| NUM | Numerical Adjective | trentaine vingtaine |
| DINTMS | Masculine Numerical Adjective | un |
| DINTFS | Feminine Numerical Adjective | une |
| PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui |
| PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y |
| PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la |
| PPOBJFP | Pronoun complements of objects - Plural Feminine | en y |
| PPER1S | Personal Pronoun First-Person - Singular | je |
| PPER2S | Personal Pronoun Second-Person - Singular | tu |
| PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il |
| PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils |
| PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle |
| PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles |
| PREFS | Reflexive Pronoun First-Person - Singular | me m' |
| PREF | Reflexive Pronoun Third-Person - Singular | se s' |
| PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous |
| VERB | Verb | obtient |
| VPPMS | Past Participle - Singular Masculine | formulé |
| VPPMP | Past Participle - Plural Masculine | classés |
| VPPFS | Past Participle - Singular Feminine | appelée |
| VPPFP | Past Participle - Plural Feminine | sanctionnées |
| DET | Determinant | les l' |
| DETMS | Determinant - Singular Masculine | les |
| DETFS | Determinant - Singular Feminine | la |
| ADJ | Adjective | capable sérieux |
| ADJMS | Adjective - Singular Masculine | grand important |
| ADJMP | Adjective - Plural Masculine | grands petits |
| ADJFS | Adjective - Singular Feminine | française petite |
| ADJFP | Adjective - Plural Feminine | légères petites |
| NOUN | Noun | temps |
| NMS | Noun - Singular Masculine | drapeau |
| NMP | Noun - Plural Masculine | journalistes |
| NFS | Noun - Singular Feminine | tête |
| NFP | Noun - Plural Feminine | ondes |
| PREL | Relative Pronoun | qui dont |
| PRELMS | Relative Pronoun - Singular Masculine | lequel |
| PRELMP | Relative Pronoun - Plural Masculine | lesquels |
| PRELFS | Relative Pronoun - Singular Feminine | laquelle |
| PRELFP | Relative Pronoun - Plural Feminine | lesquelles |
| INTJ | Interjection | merci bref |
| CHIF | Numbers | 1979 10 |
| SYM | Symbol | € % |
| YPFOR | Endpoint | . |
| PUNCT | Ponctuation | : , |
| MOTINC | Unknown words | Technology Lady |
| X | Typos & others | sfeir 3D statu |
## Evaluation results
The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu).
```plain
Results:
- F-score (micro) 0.9797
- F-score (macro) 0.9178
- Accuracy 0.9797
By class:
precision recall f1-score support
PREP 0.9966 0.9987 0.9976 1483
PUNCT 1.0000 1.0000 1.0000 833
NMS 0.9634 0.9801 0.9717 753
DET 0.9923 0.9984 0.9954 645
VERB 0.9913 0.9811 0.9862 583
NFS 0.9667 0.9839 0.9752 560
ADV 0.9940 0.9821 0.9880 504
PROPN 0.9541 0.8937 0.9229 395
DETMS 1.0000 1.0000 1.0000 362
AUX 0.9860 0.9915 0.9888 355
YPFOR 1.0000 1.0000 1.0000 353
NMP 0.9666 0.9475 0.9570 305
COCO 0.9959 1.0000 0.9980 245
ADJMS 0.9463 0.9385 0.9424 244
DETFS 1.0000 1.0000 1.0000 240
CHIF 0.9648 0.9865 0.9755 222
NFP 0.9515 0.9849 0.9679 199
ADJFS 0.9657 0.9286 0.9468 182
VPPMS 0.9387 0.9745 0.9563 157
COSUB 1.0000 0.9844 0.9921 128
DINTMS 0.9918 0.9918 0.9918 122
XFAMIL 0.9298 0.9217 0.9258 115
PPER3MS 1.0000 1.0000 1.0000 87
ADJMP 0.9294 0.9634 0.9461 82
PDEMMS 1.0000 1.0000 1.0000 75
ADJFP 0.9861 0.9342 0.9595 76
PREL 0.9859 1.0000 0.9929 70
DINTFS 0.9839 1.0000 0.9919 61
PREF 1.0000 1.0000 1.0000 52
PPOBJMS 0.9565 0.9362 0.9462 47
PREFP 0.9778 1.0000 0.9888 44
PINDMS 1.0000 0.9773 0.9885 44
VPPFS 0.8298 0.9750 0.8966 40
PPER1S 1.0000 1.0000 1.0000 42
SYM 1.0000 0.9474 0.9730 38
NOUN 0.8824 0.7692 0.8219 39
PRON 1.0000 0.9677 0.9836 31
PDEMFS 1.0000 1.0000 1.0000 29
VPPMP 0.9286 1.0000 0.9630 26
ADJ 0.9524 0.9091 0.9302 22
PPER3MP 1.0000 1.0000 1.0000 20
VPPFP 1.0000 1.0000 1.0000 19
PPER3FS 1.0000 1.0000 1.0000 18
MOTINC 0.3333 0.4000 0.3636 15
PREFS 1.0000 1.0000 1.0000 10
PPOBJMP 1.0000 0.8000 0.8889 10
PPOBJFS 0.6250 0.8333 0.7143 6
INTJ 0.5000 0.6667 0.5714 6
PART 1.0000 1.0000 1.0000 4
PDEMMP 1.0000 1.0000 1.0000 3
PDEMFP 1.0000 1.0000 1.0000 3
PPER3FP 1.0000 1.0000 1.0000 2
NUM 1.0000 0.3333 0.5000 3
PPER2S 1.0000 1.0000 1.0000 2
PPOBJFP 0.5000 0.5000 0.5000 2
PRELMS 1.0000 1.0000 1.0000 2
PINDFS 0.5000 1.0000 0.6667 1
PINDMP 1.0000 1.0000 1.0000 1
X 0.0000 0.0000 0.0000 1
PINDFP 1.0000 1.0000 1.0000 1
micro avg 0.9797 0.9797 0.9797 10019
macro avg 0.9228 0.9230 0.9178 10019
weighted avg 0.9802 0.9797 0.9798 10019
samples avg 0.9797 0.9797 0.9797 10019
```
## BibTeX Citations
Please cite the following paper when using this model.
ANTILLES corpus and POET taggers:
```latex
@inproceedings{labrak:hal-03696042,
TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}},
AUTHOR = {Labrak, Yanis and Dufour, Richard},
URL = {https://hal.archives-ouvertes.fr/hal-03696042},
BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}},
ADDRESS = {Brno, Czech Republic},
PUBLISHER = {{Springer}},
YEAR = {2022},
MONTH = Sep,
KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers},
PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf},
HAL_ID = {hal-03696042},
HAL_VERSION = {v1},
}
```
UD_French-GSD corpora:
```latex
@misc{
universaldependencies,
title={UniversalDependencies/UD_French-GSD},
url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
author={UniversalDependencies}
}
```
LIA TAGG:
```latex
@techreport{LIA_TAGG,
author = {Frédéric Béchet},
title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
institution = {Aix-Marseille University & CNRS},
year = {2001}
}
```
Flair Embeddings:
```latex
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
## Acknowledgment
This work was financially supported by [Zenidoc](https://zenidoc.fr/)
|
qanastek/pos-french-camembert
|
qanastek
| 2022-07-06T23:48:53Z | 19 | 9 |
transformers
|
[
"transformers",
"pytorch",
"camembert",
"token-classification",
"Transformers",
"sequence-tagger-model",
"fr",
"dataset:qanastek/ANTILLES",
"arxiv:1911.03894",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-03-02T23:29:05Z |
---
tags:
- Transformers
- token-classification
- sequence-tagger-model
language: fr
datasets:
- qanastek/ANTILLES
widget:
- text: "George Washington est allé à Washington"
---
# POET: A French Extended Part-of-Speech Tagger
- Corpora: [ANTILLES](https://github.com/qanastek/ANTILLES)
- Embeddings & Sequence Labelling: [CamemBERT](https://arxiv.org/abs/1911.03894)
- Number of Epochs: 115
**People Involved**
* [LABRAK Yanis](https://www.linkedin.com/in/yanis-labrak-8a7412145/) (1)
* [DUFOUR Richard](https://cv.archives-ouvertes.fr/richard-dufour) (2)
**Affiliations**
1. [LIA, NLP team](https://lia.univ-avignon.fr/), Avignon University, Avignon, France.
2. [LS2N, TALN team](https://www.ls2n.fr/equipe/taln/), Nantes University, Nantes, France.
## Demo: How to use in HuggingFace Transformers
Requires [transformers](https://pypi.org/project/transformers/): ```pip install transformers```
```python
from transformers import CamembertTokenizer, CamembertForTokenClassification, TokenClassificationPipeline
tokenizer = CamembertTokenizer.from_pretrained('qanastek/pos-french-camembert')
model = CamembertForTokenClassification.from_pretrained('qanastek/pos-french-camembert')
pos = TokenClassificationPipeline(model=model, tokenizer=tokenizer)
def make_prediction(sentence):
labels = [l['entity'] for l in pos(sentence)]
return list(zip(sentence.split(" "), labels))
res = make_prediction("George Washington est allé à Washington")
```
Output:
## Training data
`ANTILLES` is a part-of-speech tagging corpora based on [UD_French-GSD](https://universaldependencies.org/treebanks/fr_gsd/index.html) which was originally created in 2015 and is based on the [universal dependency treebank v2.0](https://github.com/ryanmcd/uni-dep-tb).
Originally, the corpora consists of 400,399 words (16,341 sentences) and had 17 different classes. Now, after applying our tags augmentation we obtain 60 different classes which add linguistic and semantic information such as the gender, number, mood, person, tense or verb form given in the different CoNLL-03 fields from the original corpora.
We based our tags on the level of details given by the [LIA_TAGG](http://pageperso.lif.univ-mrs.fr/frederic.bechet/download.html) statistical POS tagger written by [Frédéric Béchet](http://pageperso.lif.univ-mrs.fr/frederic.bechet/index-english.html) in 2001.
The corpora used for this model is available on [Github](https://github.com/qanastek/ANTILLES) at the [CoNLL-U format](https://universaldependencies.org/format.html).
Training data are fed to the model as free language and doesn't pass a normalization phase. Thus, it's made the model case and punctuation sensitive.
## Original Tags
```plain
PRON VERB SCONJ ADP CCONJ DET NOUN ADJ AUX ADV PUNCT PROPN NUM SYM PART X INTJ
```
## New additional POS tags
| Abbreviation | Description | Examples |
|:--------:|:--------:|:--------:|
| PREP | Preposition | de |
| AUX | Auxiliary Verb | est |
| ADV | Adverb | toujours |
| COSUB | Subordinating conjunction | que |
| COCO | Coordinating Conjunction | et |
| PART | Demonstrative particle | -t |
| PRON | Pronoun | qui ce quoi |
| PDEMMS | Demonstrative Pronoun - Singular Masculine | ce |
| PDEMMP | Demonstrative Pronoun - Plural Masculine | ceux |
| PDEMFS | Demonstrative Pronoun - Singular Feminine | cette |
| PDEMFP | Demonstrative Pronoun - Plural Feminine | celles |
| PINDMS | Indefinite Pronoun - Singular Masculine | tout |
| PINDMP | Indefinite Pronoun - Plural Masculine | autres |
| PINDFS | Indefinite Pronoun - Singular Feminine | chacune |
| PINDFP | Indefinite Pronoun - Plural Feminine | certaines |
| PROPN | Proper noun | Houston |
| XFAMIL | Last name | Levy |
| NUM | Numerical Adjective | trentaine vingtaine |
| DINTMS | Masculine Numerical Adjective | un |
| DINTFS | Feminine Numerical Adjective | une |
| PPOBJMS | Pronoun complements of objects - Singular Masculine | le lui |
| PPOBJMP | Pronoun complements of objects - Plural Masculine | eux y |
| PPOBJFS | Pronoun complements of objects - Singular Feminine | moi la |
| PPOBJFP | Pronoun complements of objects - Plural Feminine | en y |
| PPER1S | Personal Pronoun First-Person - Singular | je |
| PPER2S | Personal Pronoun Second-Person - Singular | tu |
| PPER3MS | Personal Pronoun Third-Person - Singular Masculine | il |
| PPER3MP | Personal Pronoun Third-Person - Plural Masculine | ils |
| PPER3FS | Personal Pronoun Third-Person - Singular Feminine | elle |
| PPER3FP | Personal Pronoun Third-Person - Plural Feminine | elles |
| PREFS | Reflexive Pronoun First-Person - Singular | me m' |
| PREF | Reflexive Pronoun Third-Person - Singular | se s' |
| PREFP | Reflexive Pronoun First / Second-Person - Plural | nous vous |
| VERB | Verb | obtient |
| VPPMS | Past Participle - Singular Masculine | formulé |
| VPPMP | Past Participle - Plural Masculine | classés |
| VPPFS | Past Participle - Singular Feminine | appelée |
| VPPFP | Past Participle - Plural Feminine | sanctionnées |
| DET | Determinant | les l' |
| DETMS | Determinant - Singular Masculine | les |
| DETFS | Determinant - Singular Feminine | la |
| ADJ | Adjective | capable sérieux |
| ADJMS | Adjective - Singular Masculine | grand important |
| ADJMP | Adjective - Plural Masculine | grands petits |
| ADJFS | Adjective - Singular Feminine | française petite |
| ADJFP | Adjective - Plural Feminine | légères petites |
| NOUN | Noun | temps |
| NMS | Noun - Singular Masculine | drapeau |
| NMP | Noun - Plural Masculine | journalistes |
| NFS | Noun - Singular Feminine | tête |
| NFP | Noun - Plural Feminine | ondes |
| PREL | Relative Pronoun | qui dont |
| PRELMS | Relative Pronoun - Singular Masculine | lequel |
| PRELMP | Relative Pronoun - Plural Masculine | lesquels |
| PRELFS | Relative Pronoun - Singular Feminine | laquelle |
| PRELFP | Relative Pronoun - Plural Feminine | lesquelles |
| INTJ | Interjection | merci bref |
| CHIF | Numbers | 1979 10 |
| SYM | Symbol | € % |
| YPFOR | Endpoint | . |
| PUNCT | Ponctuation | : , |
| MOTINC | Unknown words | Technology Lady |
| X | Typos & others | sfeir 3D statu |
## Evaluation results
The test corpora used for this evaluation is available on [Github](https://github.com/qanastek/ANTILLES/blob/main/ANTILLES/test.conllu).
```plain
precision recall f1-score support
ADJ 0.9040 0.8828 0.8933 128
ADJFP 0.9811 0.9585 0.9697 434
ADJFS 0.9606 0.9826 0.9715 918
ADJMP 0.9613 0.9357 0.9483 451
ADJMS 0.9561 0.9611 0.9586 952
ADV 0.9870 0.9948 0.9908 1524
AUX 0.9956 0.9964 0.9960 1124
CHIF 0.9798 0.9774 0.9786 1239
COCO 1.0000 0.9989 0.9994 884
COSUB 0.9939 0.9939 0.9939 328
DET 0.9972 0.9972 0.9972 2897
DETFS 0.9990 1.0000 0.9995 1007
DETMS 1.0000 0.9993 0.9996 1426
DINTFS 0.9967 0.9902 0.9934 306
DINTMS 0.9923 0.9948 0.9935 387
INTJ 0.8000 0.8000 0.8000 5
MOTINC 0.5049 0.5827 0.5410 266
NFP 0.9807 0.9675 0.9740 892
NFS 0.9778 0.9699 0.9738 2588
NMP 0.9687 0.9495 0.9590 1367
NMS 0.9759 0.9560 0.9659 3181
NOUN 0.6164 0.8673 0.7206 113
NUM 0.6250 0.8333 0.7143 6
PART 1.0000 0.9375 0.9677 16
PDEMFP 1.0000 1.0000 1.0000 3
PDEMFS 1.0000 1.0000 1.0000 89
PDEMMP 1.0000 1.0000 1.0000 20
PDEMMS 1.0000 1.0000 1.0000 222
PINDFP 1.0000 1.0000 1.0000 3
PINDFS 0.8571 1.0000 0.9231 12
PINDMP 0.9000 1.0000 0.9474 9
PINDMS 0.9286 0.9701 0.9489 67
PINTFS 0.0000 0.0000 0.0000 2
PPER1S 1.0000 1.0000 1.0000 62
PPER2S 0.7500 1.0000 0.8571 3
PPER3FP 1.0000 1.0000 1.0000 9
PPER3FS 1.0000 1.0000 1.0000 96
PPER3MP 1.0000 1.0000 1.0000 31
PPER3MS 1.0000 1.0000 1.0000 377
PPOBJFP 1.0000 0.7500 0.8571 4
PPOBJFS 0.9167 0.8919 0.9041 37
PPOBJMP 0.7500 0.7500 0.7500 12
PPOBJMS 0.9371 0.9640 0.9504 139
PREF 1.0000 1.0000 1.0000 332
PREFP 1.0000 1.0000 1.0000 64
PREFS 1.0000 1.0000 1.0000 13
PREL 0.9964 0.9964 0.9964 277
PRELFP 1.0000 1.0000 1.0000 5
PRELFS 0.8000 1.0000 0.8889 4
PRELMP 1.0000 1.0000 1.0000 3
PRELMS 1.0000 1.0000 1.0000 11
PREP 0.9971 0.9977 0.9974 6161
PRON 0.9836 0.9836 0.9836 61
PROPN 0.9468 0.9503 0.9486 4310
PUNCT 1.0000 1.0000 1.0000 4019
SYM 0.9394 0.8158 0.8732 76
VERB 0.9956 0.9921 0.9938 2273
VPPFP 0.9145 0.9469 0.9304 113
VPPFS 0.9562 0.9597 0.9580 273
VPPMP 0.8827 0.9728 0.9256 147
VPPMS 0.9778 0.9794 0.9786 630
VPPRE 0.0000 0.0000 0.0000 1
X 0.9604 0.9935 0.9766 1073
XFAMIL 0.9386 0.9113 0.9248 1342
YPFOR 1.0000 1.0000 1.0000 2750
accuracy 0.9778 47574
macro avg 0.9151 0.9285 0.9202 47574
weighted avg 0.9785 0.9778 0.9780 47574
```
## BibTeX Citations
Please cite the following paper when using this model.
ANTILLES corpus and POET taggers:
```latex
@inproceedings{labrak:hal-03696042,
TITLE = {{ANTILLES: An Open French Linguistically Enriched Part-of-Speech Corpus}},
AUTHOR = {Labrak, Yanis and Dufour, Richard},
URL = {https://hal.archives-ouvertes.fr/hal-03696042},
BOOKTITLE = {{25th International Conference on Text, Speech and Dialogue (TSD)}},
ADDRESS = {Brno, Czech Republic},
PUBLISHER = {{Springer}},
YEAR = {2022},
MONTH = Sep,
KEYWORDS = {Part-of-speech corpus ; POS tagging ; Open tools ; Word embeddings ; Bi-LSTM ; CRF ; Transformers},
PDF = {https://hal.archives-ouvertes.fr/hal-03696042/file/ANTILLES_A_freNch_linguisTIcaLLy_Enriched_part_of_Speech_corpus.pdf},
HAL_ID = {hal-03696042},
HAL_VERSION = {v1},
}
```
UD_French-GSD corpora:
```latex
@misc{
universaldependencies,
title={UniversalDependencies/UD_French-GSD},
url={https://github.com/UniversalDependencies/UD_French-GSD}, journal={GitHub},
author={UniversalDependencies}
}
```
LIA TAGG:
```latex
@techreport{LIA_TAGG,
author = {Frédéric Béchet},
title = {LIA_TAGG: a statistical POS tagger + syntactic bracketer},
institution = {Aix-Marseille University & CNRS},
year = {2001}
}
```
Flair Embeddings:
```latex
@inproceedings{akbik2018coling,
title={Contextual String Embeddings for Sequence Labeling},
author={Akbik, Alan and Blythe, Duncan and Vollgraf, Roland},
booktitle = {{COLING} 2018, 27th International Conference on Computational Linguistics},
pages = {1638--1649},
year = {2018}
}
```
## Acknowledgment
This work was financially supported by [Zenidoc](https://zenidoc.fr/)
|
ricardo-filho/bert_base_tcm_teste
|
ricardo-filho
| 2022-07-06T23:23:13Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-06T18:05:49Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bert_base_tcm_teste
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert_base_tcm_teste
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0192
- Criterio Julgamento Precision: 0.7209
- Criterio Julgamento Recall: 0.8942
- Criterio Julgamento F1: 0.7983
- Criterio Julgamento Number: 104
- Data Sessao Precision: 0.6351
- Data Sessao Recall: 0.8545
- Data Sessao F1: 0.7287
- Data Sessao Number: 55
- Modalidade Licitacao Precision: 0.9224
- Modalidade Licitacao Recall: 0.9596
- Modalidade Licitacao F1: 0.9406
- Modalidade Licitacao Number: 421
- Numero Exercicio Precision: 0.8872
- Numero Exercicio Recall: 0.9351
- Numero Exercicio F1: 0.9105
- Numero Exercicio Number: 185
- Objeto Licitacao Precision: 0.2348
- Objeto Licitacao Recall: 0.4576
- Objeto Licitacao F1: 0.3103
- Objeto Licitacao Number: 59
- Valor Objeto Precision: 0.5424
- Valor Objeto Recall: 0.7805
- Valor Objeto F1: 0.64
- Valor Objeto Number: 41
- Overall Precision: 0.7683
- Overall Recall: 0.8971
- Overall F1: 0.8277
- Overall Accuracy: 0.9948
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Criterio Julgamento Precision | Criterio Julgamento Recall | Criterio Julgamento F1 | Criterio Julgamento Number | Data Sessao Precision | Data Sessao Recall | Data Sessao F1 | Data Sessao Number | Modalidade Licitacao Precision | Modalidade Licitacao Recall | Modalidade Licitacao F1 | Modalidade Licitacao Number | Numero Exercicio Precision | Numero Exercicio Recall | Numero Exercicio F1 | Numero Exercicio Number | Objeto Licitacao Precision | Objeto Licitacao Recall | Objeto Licitacao F1 | Objeto Licitacao Number | Valor Objeto Precision | Valor Objeto Recall | Valor Objeto F1 | Valor Objeto Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------------------:|:--------------------------:|:----------------------:|:--------------------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------------------:|:---------------------------:|:-----------------------:|:---------------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:--------------------------:|:-----------------------:|:-------------------:|:-----------------------:|:----------------------:|:-------------------:|:---------------:|:-------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.0346 | 0.96 | 2750 | 0.0329 | 0.6154 | 0.8462 | 0.7126 | 104 | 0.5495 | 0.9091 | 0.6849 | 55 | 0.8482 | 0.9287 | 0.8866 | 421 | 0.7438 | 0.9730 | 0.8431 | 185 | 0.0525 | 0.3220 | 0.0903 | 59 | 0.4762 | 0.7317 | 0.5769 | 41 | 0.5565 | 0.8763 | 0.6807 | 0.9880 |
| 0.0309 | 1.92 | 5500 | 0.0322 | 0.6694 | 0.7788 | 0.72 | 104 | 0.5976 | 0.8909 | 0.7153 | 55 | 0.9178 | 0.9549 | 0.9360 | 421 | 0.8211 | 0.8432 | 0.8320 | 185 | 0.15 | 0.2034 | 0.1727 | 59 | 0.2203 | 0.3171 | 0.26 | 41 | 0.7351 | 0.8243 | 0.7771 | 0.9934 |
| 0.0179 | 2.88 | 8250 | 0.0192 | 0.7209 | 0.8942 | 0.7983 | 104 | 0.6351 | 0.8545 | 0.7287 | 55 | 0.9224 | 0.9596 | 0.9406 | 421 | 0.8872 | 0.9351 | 0.9105 | 185 | 0.2348 | 0.4576 | 0.3103 | 59 | 0.5424 | 0.7805 | 0.64 | 41 | 0.7683 | 0.8971 | 0.8277 | 0.9948 |
| 0.0174 | 3.84 | 11000 | 0.0320 | 0.7522 | 0.8173 | 0.7834 | 104 | 0.5741 | 0.5636 | 0.5688 | 55 | 0.8881 | 0.9430 | 0.9147 | 421 | 0.8490 | 0.8811 | 0.8647 | 185 | 0.2436 | 0.3220 | 0.2774 | 59 | 0.5370 | 0.7073 | 0.6105 | 41 | 0.7719 | 0.8370 | 0.8031 | 0.9946 |
| 0.0192 | 4.8 | 13750 | 0.0261 | 0.6744 | 0.8365 | 0.7468 | 104 | 0.6190 | 0.7091 | 0.6610 | 55 | 0.9169 | 0.9430 | 0.9297 | 421 | 0.8404 | 0.8541 | 0.8472 | 185 | 0.2059 | 0.3559 | 0.2609 | 59 | 0.5088 | 0.7073 | 0.5918 | 41 | 0.7521 | 0.8451 | 0.7959 | 0.9949 |
| 0.0158 | 5.76 | 16500 | 0.0250 | 0.6641 | 0.8173 | 0.7328 | 104 | 0.5610 | 0.8364 | 0.6715 | 55 | 0.9199 | 0.9549 | 0.9371 | 421 | 0.9167 | 0.9514 | 0.9337 | 185 | 0.1912 | 0.4407 | 0.2667 | 59 | 0.4828 | 0.6829 | 0.5657 | 41 | 0.7386 | 0.8821 | 0.8040 | 0.9948 |
| 0.0126 | 6.72 | 19250 | 0.0267 | 0.6694 | 0.7981 | 0.7281 | 104 | 0.6386 | 0.9636 | 0.7681 | 55 | 0.8723 | 0.9572 | 0.9128 | 421 | 0.8812 | 0.9622 | 0.9199 | 185 | 0.2180 | 0.4915 | 0.3021 | 59 | 0.5323 | 0.8049 | 0.6408 | 41 | 0.7308 | 0.9006 | 0.8068 | 0.9945 |
| 0.0162 | 7.68 | 22000 | 0.0328 | 0.675 | 0.7788 | 0.7232 | 104 | 0.6604 | 0.6364 | 0.6481 | 55 | 0.9263 | 0.9549 | 0.9404 | 421 | 0.8535 | 0.9135 | 0.8825 | 185 | 0.2471 | 0.3559 | 0.2917 | 59 | 0.5091 | 0.6829 | 0.5833 | 41 | 0.7788 | 0.8509 | 0.8133 | 0.9948 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
nateraw/new-modelcard-template-test
|
nateraw
| 2022-07-06T19:18:09Z | 0 | 0 | null |
[
"image-classification",
"created-with-modelcards",
"en",
"arxiv:1910.09700",
"license:mit",
"region:us"
] |
image-classification
| 2022-07-06T19:17:41Z |
---
language: en
license: mit
tags:
- image-classification
- created-with-modelcards
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Bias, Risks, and Limitations](#bias-risks-and-limitations)
4. [Training Details](#training-details)
5. [Evaluation](#evaluation)
6. [Model Examination](#model-examination)
7. [Environmental Impact](#environmental-impact)
8. [Technical Specifications](#technical-specifications-optional)
9. [Citation](#citation)
10. [Glossary](#glossary-optional)
11. [More Information](#more-information-optional)
12. [Model Card Authors](#model-card-authors-optional)
13. [Model Card Contact](#model-card-contact)
14. [How To Get Started With the Model](#how-to-get-started-with-the-model)
# Model Details
## Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [Optional]:** [More Information Needed]
- **Model type:** Language model
- **Language(s) (NLP):** en
- **License:** mit
- **Related Models:** [More Information Needed]
- **Parent Model:** [More Information Needed]
- **Resources for more information:** [More Information Needed]
# Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
## Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
## Downstream Use [Optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
## Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
# Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
## Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recomendations.
# Training Details
## Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
## Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
### Preprocessing
[More Information Needed]
### Speeds, Sizes, Times
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
# Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
## Testing Data, Factors & Metrics
### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
## Results
[More Information Needed]
# Model Examination
[More Information Needed]
# Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
# Technical Specifications [optional]
## Model Architecture and Objective
[More Information Needed]
## Compute Infrastructure
[More Information Needed]
### Hardware
[More Information Needed]
### Software
[More Information Needed]
# Citation
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
# Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
# More Information [optional]
[More Information Needed]
# Model Card Authors [optional]
[More Information Needed]
# Model Card Contact
[More Information Needed]
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
[More Information Needed]
</details>
|
ManqingLiu/xlm-roberta-base-finetuned-panx-de
|
ManqingLiu
| 2022-07-06T18:16:00Z | 5 | 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
| 2022-07-06T17:02:45Z |
---
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: 0.8627004891366169
---
<!-- 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: 0.1363
- F1: 0.8627
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2539 | 1.0 | 525 | 0.1697 | 0.8179 |
| 0.1317 | 2.0 | 1050 | 0.1327 | 0.8516 |
| 0.0819 | 3.0 | 1575 | 0.1363 | 0.8627 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BigTimeCoderSean/q-Taxi-v3
|
BigTimeCoderSean
| 2022-07-06T18:13:18Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T18:13:12Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.54 +/- 2.70
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="BigTimeCoderSean/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
wiselinjayajos/t5-end2end-questions-generation-cv-squadV2
|
wiselinjayajos
| 2022-07-06T17:20:59Z | 26 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-06T02:47:35Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: t5-end2end-questions-generation-cv-squadV2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-end2end-questions-generation-cv-squadV2
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8541
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.6703 | 2.17 | 100 | 1.9685 |
| 1.9718 | 4.34 | 200 | 1.8541 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigscience/tr11-176B-logs
|
bigscience
| 2022-07-06T17:01:14Z | 0 | 250 | null |
[
"tensorboard",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
"code",
"en",
"es",
"eu",
"fon",
"fr",
"gu",
"hi",
"id",
"ig",
"ki",
"kn",
"lg",
"ln",
"ml",
"mr",
"ne",
"nso",
"ny",
"or",
"pa",
"pt",
"rn",
"rw",
"sn",
"st",
"sw",
"ta",
"te",
"tn",
"ts",
"tum",
"tw",
"ur",
"vi",
"wo",
"xh",
"yo",
"zh",
"zhs",
"zht",
"zu",
"region:us"
] | null | 2022-03-03T04:38:09Z |
---
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
---
# BigScience Large Language Model Training
Training a multilingual 176 billion parameters model in the open
[BigScience](https://bigscience.huggingface.co) is a open and collaborative workshop around the study and creation of very large language models gathering more than 1000 researchers around the worlds. You can find more information on the main website at https://bigscience.huggingface.co.
The training of BigScience’s main model started on **March 11, 2022 11:42am PST** and will continue for 3-4 months on 384 A100 80GB GPUs of the Jean Zay public supercomputer
You can follow the training at [https://twitter.com/BigScienceLLM](https://twitter.com/BigScienceLLM) or on [the Tensorboards tab above](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss).
## More information on the model, dataset, hardware, environmental consideration:
### **The model**
- 176B parameters decoder-only architecture (GPT-like)
- 70 layers - 112 attention heads per layers - hidden dimensionality of 14336 - 2048 tokens sequence length
- ALiBi positional embeddings - GeLU activation function
- **More information**:
- Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: [https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours](https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours)
- More details on the architecture/optimizer: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml)
### **The dataset**
- Multilingual: 46 languages: Full list is here: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling)
- 341.6 billion tokens (1.5 TB of text data)
- Tokenizer vocabulary: 250,680 tokens
- More information:
- Blog post detailing the design choices during the dataset creation: [https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling](https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling)
### **The engineering side**
- number of GPU used for the training: 384 A100 GPU with 80 GB of memory each
- one copy of the model takes 48 GPUs (using 60 GB of memory on each GPU)
- checkpoint size: the bf16 weights are 329GB, the full checkpoint with optimizer states is 2.3TB
- training throughput: ~150 TFLOPs
- estimated training time: 3-4 months depending on throughput and unexpected events
- **More information**:
- Blog post on the hardware/engineering side: [https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model](https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model)
- Details on the distributed setup used for the training: [https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml)
- Tensorboard updated during the training: [https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss](https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss)
- Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): [https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md](https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md)
### **Environmental considerations**
- [Jean Zay](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html), the supercomputer we are using for model training, is mostly powered by nuclear energy, which is a low carbon energy source.
- Significant efforts were made to make sure that the computing infrastructure is as efficient as possible — the heat generated by the hardware even gets used for heating buildings on campus!
- **More information**:
- We are currently working on making a precise estimate of the carbon emitted during all of the steps of model training, including intermediate experiments as well as inference.
- More soon!
|
nawta/wav2vec2-wtimit-finetune
|
nawta
| 2022-07-06T16:07:23Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-06T09:40:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-wtimit-finetune
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-wtimit-finetune
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: 0.0383
- Wer: 0.0160
## 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: 64
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 5.3743 | 2.82 | 500 | 2.9567 | 1.0 |
| 1.866 | 5.65 | 1000 | 0.2856 | 0.2580 |
| 0.2005 | 8.47 | 1500 | 0.0979 | 0.0669 |
| 0.08 | 11.3 | 2000 | 0.0617 | 0.0325 |
| 0.0497 | 14.12 | 2500 | 0.0578 | 0.0284 |
| 0.0348 | 16.95 | 3000 | 0.0557 | 0.0239 |
| 0.0269 | 19.77 | 3500 | 0.0447 | 0.0212 |
| 0.0198 | 22.6 | 4000 | 0.0437 | 0.0177 |
| 0.016 | 25.42 | 4500 | 0.0407 | 0.0164 |
| 0.014 | 28.25 | 5000 | 0.0383 | 0.0160 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.1+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
saekomdalkom/t5-small-finetuned-xsum
|
saekomdalkom
| 2022-07-06T15:25:39Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:xsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-06T13:04:22Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.3577
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4783
- Rouge1: 28.3577
- Rouge2: 7.759
- Rougel: 22.274
- Rougelsum: 22.2869
- Gen Len: 18.8298
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:------:|:---------:|:-------:|
| 2.7158 | 1.0 | 12753 | 2.4783 | 28.3577 | 7.759 | 22.274 | 22.2869 | 18.8298 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
cacauvicosa/heart1ohr2x9e-target-classification
|
cacauvicosa
| 2022-07-06T15:11:05Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-07-06T15:11:03Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on heart1ohr2x9e to apply classification on target
**Metrics of the best model:**
accuracy 0.885854
average_precision 0.949471
roc_auc 0.050633
recall_macro 0.885324
f1_macro 0.885610
Name: LogisticRegression(class_weight='balanced', max_iter=1000), dtype: float64
**See model plot below:**
<style>#sk-container-id-8 {color: black;background-color: white;}#sk-container-id-8 pre{padding: 0;}#sk-container-id-8 div.sk-toggleable {background-color: white;}#sk-container-id-8 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-8 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-8 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-8 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-8 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-8 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-8 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-8 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-8 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-8 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-8 div.sk-item {position: relative;z-index: 1;}#sk-container-id-8 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-8 div.sk-item::before, #sk-container-id-8 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-8 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-8 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-8 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-8 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-8 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-8 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-8 div.sk-label-container {text-align: center;}#sk-container-id-8 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-8 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-8" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
age False False False ... False False False
sex False False False ... False False False
cp False False False ... False False False
trestbps True False False ... False False False
chol True False False ... False False False
fbs False False False ... False False False
restecg False Fa...... False False False
thalach True False False ... False False False
exang False False False ... False False False
oldpeak True False False ... False False False
slope False False False ... False False False
ca False False False ... False False False
thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-24" type="checkbox" ><label for="sk-estimator-id-24" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
age False False False ... False False False
sex False False False ... False False False
cp False False False ... False False False
trestbps True False False ... False False False
chol True False False ... False False False
fbs False False False ... False False False
restecg False Fa...... False False False
thalach True False False ... False False False
exang False False False ... False False False
oldpeak True False False ... False False False
slope False False False ... False False False
ca False False False ... False False False
thal False False False ... False False False[13 rows x 7 columns])),('logisticregression',LogisticRegression(C=1, class_weight='balanced',max_iter=1000))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-25" type="checkbox" ><label for="sk-estimator-id-25" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
age False False False ... False False False
sex False False False ... False False False
cp False False False ... False False False
trestbps True False False ... False False False
chol True False False ... False False False
fbs False False False ... False False False
restecg False False False ... False False False
thalach True False False ... False False False
exang False False False ... False False False
oldpeak True False False ... False False False
slope False False False ... False False False
ca False False False ... False False False
thal False False False ... False False False[13 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-26" type="checkbox" ><label for="sk-estimator-id-26" class="sk-toggleable__label sk-toggleable__label-arrow">LogisticRegression</label><div class="sk-toggleable__content"><pre>LogisticRegression(C=1, class_weight='balanced', max_iter=1000)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
IsaMaks/distilbert-base-uncased-finetuned-ner
|
IsaMaks
| 2022-07-06T14:48:51Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-24T08:41:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8874
- Precision: 0.2534
- Recall: 0.3333
- F1: 0.2879
- Accuracy: 0.7603
- True predictions: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
- True labels: [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy | True predictions | True labels |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| No log | 1.0 | 2 | 0.9937 | 0.2839 | 0.3072 | 0.2951 | 0.6712 | [0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 1, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 2] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
| No log | 2.0 | 4 | 0.9155 | 0.2523 | 0.3273 | 0.2850 | 0.7466 | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
| No log | 3.0 | 6 | 0.8874 | 0.2534 | 0.3333 | 0.2879 | 0.7603 | [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] | [0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 1, 2, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 2, 0, 1, 2, 2, 2, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0] |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bothrajat/q-FrozenLake-v1-4x4-noSlippery
|
bothrajat
| 2022-07-06T14:34:51Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-04T12:45:20Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="bothrajat/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
luizapzbn/titanicht_mp88q-Survived-classification
|
luizapzbn
| 2022-07-06T13:25:48Z | 0 | 0 |
sklearn
|
[
"sklearn",
"tabular-classification",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-classification
| 2022-07-06T13:25:46Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-classification
- baseline-trainer
---
## Baseline Model trained on titanicht_mp88q to apply classification on Survived
**Metrics of the best model:**
accuracy 0.803597
average_precision 0.801332
roc_auc 0.848079
recall_macro 0.795883
f1_macro 0.793746
Name: DecisionTreeClassifier(class_weight='balanced', max_depth=5), dtype: float64
**See model plot below:**
<style>#sk-container-id-7 {color: black;background-color: white;}#sk-container-id-7 pre{padding: 0;}#sk-container-id-7 div.sk-toggleable {background-color: white;}#sk-container-id-7 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-7 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-7 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-7 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-7 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-7 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-7 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-7 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-7 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-7 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-7 div.sk-item {position: relative;z-index: 1;}#sk-container-id-7 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-7 div.sk-item::before, #sk-container-id-7 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-7 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-7 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-7 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-7 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-7 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-7 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-7 div.sk-label-container {text-align: center;}#sk-container-id-7 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-7 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-7" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Pclass False False False ... False False False
Name False False False ... False True False
Sex False False False ... False False False
Age True False False ... False False False
SibSp False False False ... False False False
Parch False False False ... False False False
Ticket False False False ... False True False
Fare True False False ... False False False
Cabin False False False ... False True False
Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-21" type="checkbox" ><label for="sk-estimator-id-21" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Pclass False False False ... False False False
Name False False False ... False True False
Sex False False False ... False False False
Age True False False ... False False False
SibSp False False False ... False False False
Parch False False False ... False False False
Ticket False False False ... False True False
Fare True False False ... False False False
Cabin False False False ... False True False
Embarked False False False ... False False False[10 rows x 7 columns])),('decisiontreeclassifier',DecisionTreeClassifier(class_weight='balanced', max_depth=5))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-22" type="checkbox" ><label for="sk-estimator-id-22" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Pclass False False False ... False False False
Name False False False ... False True False
Sex False False False ... False False False
Age True False False ... False False False
SibSp False False False ... False False False
Parch False False False ... False False False
Ticket False False False ... False True False
Fare True False False ... False False False
Cabin False False False ... False True False
Embarked False False False ... False False False[10 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-23" type="checkbox" ><label for="sk-estimator-id-23" class="sk-toggleable__label sk-toggleable__label-arrow">DecisionTreeClassifier</label><div class="sk-toggleable__content"><pre>DecisionTreeClassifier(class_weight='balanced', max_depth=5)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
sumitrsch/muril_base_multiconer22_bn
|
sumitrsch
| 2022-07-06T12:33:20Z | 4 | 3 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-30T07:24:11Z |
---
license: afl-3.0
---
Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO
|
srg/outhimar_64-Close-regression
|
srg
| 2022-07-06T12:33:04Z | 0 | 4 |
sklearn
|
[
"sklearn",
"tabular-regression",
"baseline-trainer",
"license:apache-2.0",
"region:us"
] |
tabular-regression
| 2022-07-06T12:33:02Z |
---
license: apache-2.0
library_name: sklearn
tags:
- tabular-regression
- baseline-trainer
---
## Baseline Model trained on outhimar_64 to apply regression on Close
**Metrics of the best model:**
r2 0.999858
neg_mean_squared_error -1.067685
Name: Ridge(alpha=10), dtype: float64
**See model plot below:**
<style>#sk-container-id-6 {color: black;background-color: white;}#sk-container-id-6 pre{padding: 0;}#sk-container-id-6 div.sk-toggleable {background-color: white;}#sk-container-id-6 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-6 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-6 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-6 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-6 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-6 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-6 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-6 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-6 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-6 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-6 div.sk-item {position: relative;z-index: 1;}#sk-container-id-6 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-6 div.sk-item::before, #sk-container-id-6 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-6 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-6 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-6 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-6 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-6 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-6 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-6 div.sk-label-container {text-align: center;}#sk-container-id-6 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-6 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-6" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Date False False False ... True False False
Open True False False ... False False False
High True False False ... False False False
Low True False False ... False False False
Adj Close True False False ... False False False
Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item sk-dashed-wrapped"><div class="sk-label-container"><div class="sk-label sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-18" type="checkbox" ><label for="sk-estimator-id-18" class="sk-toggleable__label sk-toggleable__label-arrow">Pipeline</label><div class="sk-toggleable__content"><pre>Pipeline(steps=[('easypreprocessor',EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Date False False False ... True False False
Open True False False ... False False False
High True False False ... False False False
Low True False False ... False False False
Adj Close True False False ... False False False
Volume True False False ... False False False[6 rows x 7 columns])),('ridge', Ridge(alpha=10))])</pre></div></div></div><div class="sk-serial"><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-19" type="checkbox" ><label for="sk-estimator-id-19" class="sk-toggleable__label sk-toggleable__label-arrow">EasyPreprocessor</label><div class="sk-toggleable__content"><pre>EasyPreprocessor(types= continuous dirty_float low_card_int ... date free_string useless
Date False False False ... True False False
Open True False False ... False False False
High True False False ... False False False
Low True False False ... False False False
Adj Close True False False ... False False False
Volume True False False ... False False False[6 rows x 7 columns])</pre></div></div></div><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-20" type="checkbox" ><label for="sk-estimator-id-20" class="sk-toggleable__label sk-toggleable__label-arrow">Ridge</label><div class="sk-toggleable__content"><pre>Ridge(alpha=10)</pre></div></div></div></div></div></div></div>
**Disclaimer:** This model is trained with dabl library as a baseline, for better results, use [AutoTrain](https://huggingface.co/autotrain).
**Logs of training** including the models tried in the process can be found in logs.txt
|
sumitrsch/Indic-bert_multiconer22_bn
|
sumitrsch
| 2022-07-06T12:32:40Z | 3 | 2 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"token-classification",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-06T10:07:47Z |
---
license: afl-3.0
---
Put this model path in variable best_model_path in first cell of given colab notebook for testing semeval multiconer task for bangla track.
https://colab.research.google.com/drive/1P9827acdS7i6eZTi4B0cOms5qLREqvUO
|
dandelin/vilt-b32-mlm
|
dandelin
| 2022-07-06T12:18:37Z | 66,336 | 11 |
transformers
|
[
"transformers",
"pytorch",
"vilt",
"fill-mask",
"arxiv:2102.03334",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
---
# Vision-and-Language Transformer (ViLT), pre-trained only
Vision-and-Language Transformer (ViLT) model pre-trained on GCC+SBU+COCO+VG (200k steps). It was introduced in the paper [ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision](https://arxiv.org/abs/2102.03334) by Kim et al. and first released in [this repository](https://github.com/dandelin/ViLT). Note: this model only includes the language modeling head.
Disclaimer: The team releasing ViLT did not write a model card for this model so this model card has been written by the Hugging Face team.
## Intended uses & limitations
You can use the raw model for masked language modeling given an image and a piece of text with [MASK] tokens.
### How to use
Here is how to use this model in PyTorch:
```
from transformers import ViltProcessor, ViltForMaskedLM
import requests
from PIL import Image
import re
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
text = "a bunch of [MASK] laying on a [MASK]."
processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-mlm")
model = ViltForMaskedLM.from_pretrained("dandelin/vilt-b32-mlm")
# prepare inputs
encoding = processor(image, text, return_tensors="pt")
# forward pass
outputs = model(**encoding)
tl = len(re.findall("\[MASK\]", text))
inferred_token = [text]
# gradually fill in the MASK tokens, one by one
with torch.no_grad():
for i in range(tl):
encoded = processor.tokenizer(inferred_token)
input_ids = torch.tensor(encoded.input_ids).to(device)
encoded = encoded["input_ids"][0][1:-1]
outputs = model(input_ids=input_ids, pixel_values=pixel_values)
mlm_logits = outputs.logits[0] # shape (seq_len, vocab_size)
# only take into account text features (minus CLS and SEP token)
mlm_logits = mlm_logits[1 : input_ids.shape[1] - 1, :]
mlm_values, mlm_ids = mlm_logits.softmax(dim=-1).max(dim=-1)
# only take into account text
mlm_values[torch.tensor(encoded) != 103] = 0
select = mlm_values.argmax().item()
encoded[select] = mlm_ids[select].item()
inferred_token = [processor.decode(encoded)]
selected_token = ""
encoded = processor.tokenizer(inferred_token)
processor.decode(encoded.input_ids[0], skip_special_tokens=True)
```
## Training data
(to do)
## Training procedure
### Preprocessing
(to do)
### Pretraining
(to do)
## Evaluation results
(to do)
### BibTeX entry and citation info
```bibtex
@misc{kim2021vilt,
title={ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision},
author={Wonjae Kim and Bokyung Son and Ildoo Kim},
year={2021},
eprint={2102.03334},
archivePrefix={arXiv},
primaryClass={stat.ML}
}
```
|
SiddharthaM/beit-base-patch16-224-pt22k-ft22k-rim_one-new
|
SiddharthaM
| 2022-07-06T11:17:32Z | 55 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-06T10:31:21Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-ft22k-rim_one-new
results:
- task:
type: image-classification
name: Image Classification
dataset:
type: rimonedl
name: RIM ONE DL
split: test
metrics:
- type: f1
value: 0.9197860962566845
name: F1
- task:
type: image-classification
name: Image Classification
dataset:
type: rim one
name: RIMONEDL
split: test
metrics:
- type: precision
value: 0.9247311827956989
name: precision
- type: recall
value: 0.9148936170212766
name: Recall
- type: accuracy
value: 0.8972602739726028
name: Accuracy
- type: roc_auc
value: 0.8901391162029461
name: AUC
---
<!-- 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. -->
# beit-base-patch16-224-pt22k-ft22k-rim_one-new
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k-ft22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k-ft22k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4550
- Accuracy: 0.8767
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.73 | 2 | 0.2411 | 0.9178 |
| No log | 1.73 | 4 | 0.2182 | 0.8973 |
| No log | 2.73 | 6 | 0.3085 | 0.8973 |
| No log | 3.73 | 8 | 0.2794 | 0.8973 |
| 0.1392 | 4.73 | 10 | 0.2398 | 0.9110 |
| 0.1392 | 5.73 | 12 | 0.2925 | 0.8973 |
| 0.1392 | 6.73 | 14 | 0.2798 | 0.9110 |
| 0.1392 | 7.73 | 16 | 0.2184 | 0.9178 |
| 0.1392 | 8.73 | 18 | 0.3007 | 0.9110 |
| 0.0416 | 9.73 | 20 | 0.3344 | 0.9041 |
| 0.0416 | 10.73 | 22 | 0.3626 | 0.9110 |
| 0.0416 | 11.73 | 24 | 0.4842 | 0.8904 |
| 0.0416 | 12.73 | 26 | 0.3664 | 0.8973 |
| 0.0416 | 13.73 | 28 | 0.3458 | 0.9110 |
| 0.0263 | 14.73 | 30 | 0.2810 | 0.9110 |
| 0.0263 | 15.73 | 32 | 0.4695 | 0.8699 |
| 0.0263 | 16.73 | 34 | 0.3723 | 0.9041 |
| 0.0263 | 17.73 | 36 | 0.3447 | 0.9041 |
| 0.0263 | 18.73 | 38 | 0.3708 | 0.8904 |
| 0.0264 | 19.73 | 40 | 0.4052 | 0.9110 |
| 0.0264 | 20.73 | 42 | 0.4492 | 0.9041 |
| 0.0264 | 21.73 | 44 | 0.4649 | 0.8904 |
| 0.0264 | 22.73 | 46 | 0.4061 | 0.9178 |
| 0.0264 | 23.73 | 48 | 0.4136 | 0.9110 |
| 0.0139 | 24.73 | 50 | 0.4183 | 0.8973 |
| 0.0139 | 25.73 | 52 | 0.4504 | 0.8904 |
| 0.0139 | 26.73 | 54 | 0.4368 | 0.8973 |
| 0.0139 | 27.73 | 56 | 0.4711 | 0.9110 |
| 0.0139 | 28.73 | 58 | 0.3928 | 0.9110 |
| 0.005 | 29.73 | 60 | 0.4550 | 0.8767 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
paola-md/recipe-test
|
paola-md
| 2022-07-06T10:32:13Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-06T10:27:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: recipe-test
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. -->
# recipe-test
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: 2.9583
## 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
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.3675 | 1.0 | 16 | 3.0009 |
| 3.0062 | 2.0 | 32 | 2.9583 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kws/q-Taxi-v3
|
kws
| 2022-07-06T10:24:02Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T10:23:57Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="kws/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
dnouri/brats_mri_segmentation
|
dnouri
| 2022-07-06T09:54:53Z | 0 | 1 | null |
[
"monai",
"arxiv:1810.11654",
"region:us"
] | null | 2022-07-06T09:13:12Z |
---
tags:
- monai
---
# Model Overview
A pre-trained model for volumetric (3D) segmentation of brain tumor subregions from multimodal MRIs based on BraTS 2018 data. The whole pipeline is modified from [clara_pt_brain_mri_segmentation](https://catalog.ngc.nvidia.com/orgs/nvidia/teams/med/models/clara_pt_brain_mri_segmentation).
## Workflow
The model is trained to segment 3 nested subregions of primary brain tumors (gliomas): the "enhancing tumor" (ET), the "tumor core" (TC), the "whole tumor" (WT) based on 4 aligned input MRI scans (T1c, T1, T2, FLAIR).
- The ET is described by areas that show hyper intensity in T1c when compared to T1, but also when compared to "healthy" white matter in T1c.
- The TC describes the bulk of the tumor, which is what is typically resected. The TC entails the ET, as well as the necrotic (fluid-filled) and the non-enhancing (solid) parts of the tumor.
- The WT describes the complete extent of the disease, as it entails the TC and the peritumoral edema (ED), which is typically depicted by hyper-intense signal in FLAIR.
## Data
The training data is from the [Multimodal Brain Tumor Segmentation Challenge (BraTS) 2018](https://www.med.upenn.edu/sbia/brats2018/data.html).
- Target: 3 tumor subregions
- Task: Segmentation
- Modality: MRI
- Size: 285 3D volumes (4 channels each)
The provided labelled data was partitioned, based on our own split, into training (200 studies), validation (42 studies) and testing (43 studies) datasets.
Please run `scripts/prepare_datalist.py` to produce the data list. The command is like:
```
python scripts/prepare_datalist.py --path your-brats18-dataset-path
```
## Training configuration
This model utilized a similar approach described in 3D MRI brain tumor segmentation
using autoencoder regularization, which was a winning method in BraTS2018 [1]. The training was performed with the following:
- GPU: At least 16GB of GPU memory.
- Actual Model Input: 224 x 224 x 144
- AMP: True
- Optimizer: Adam
- Learning Rate: 1e-4
- Loss: DiceLoss
## Input
Input: 4 channel MRI (4 aligned MRIs T1c, T1, T2, FLAIR at 1x1x1 mm)
1. Normalizing to unit std with zero mean
2. Randomly cropping to (224, 224, 144)
3. Randomly spatial flipping
4. Randomly scaling and shifting intensity of the volume
## Output
Output: 3 channels
- Label 0: TC tumor subregion
- Label 1: WT tumor subregion
- Label 2: ET tumor subregion
## Model Performance
The achieved Dice scores on the validation data are:
- Tumor core (TC): 0.8559
- Whole tumor (WT): 0.9026
- Enhancing tumor (ET): 0.7905
- Average: 0.8518
## commands example
Execute training:
```
python -m monai.bundle run training --meta_file configs/metadata.json --config_file configs/train.json --logging_file configs/logging.conf
```
Override the `train` config to execute multi-GPU training:
```
torchrun --standalone --nnodes=1 --nproc_per_node=8 -m monai.bundle run training --meta_file configs/metadata.json --config_file "['configs/train.json','configs/multi_gpu_train.json']" --logging_file configs/logging.conf
```
Override the `train` config to execute evaluation with the trained model:
```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file "['configs/train.json','configs/evaluate.json']" --logging_file configs/logging.conf
```
Execute inference:
```
python -m monai.bundle run evaluating --meta_file configs/metadata.json --config_file configs/inference.json --logging_file configs/logging.conf
```
# Disclaimer
This is an example, not to be used for diagnostic purposes.
# References
[1] Myronenko, Andriy. "3D MRI brain tumor segmentation using autoencoder regularization." International MICCAI Brainlesion Workshop. Springer, Cham, 2018. https://arxiv.org/abs/1810.11654.
|
go2k/TEST2ppo-LunarLander-v2
|
go2k
| 2022-07-06T06:26:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-06T06:26:22Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 200.81 +/- 77.09
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
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
dee4hf/autotrain-deephate2-1093539673
|
dee4hf
| 2022-07-06T04:28:59Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"albert",
"text-classification",
"autotrain",
"bn",
"dataset:dee4hf/autotrain-data-deephate2",
"co2_eq_emissions",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-06T04:25:25Z |
---
tags: autotrain
language: bn
widget:
- text: "I love AutoTrain 🤗"
datasets:
- dee4hf/autotrain-data-deephate2
co2_eq_emissions: 7.663051290039914
---
# Model Trained Using AutoTrain
- Problem type: Multi-class Classification
- Model ID: 1093539673
- CO2 Emissions (in grams): 7.663051290039914
## Validation Metrics
- Loss: 0.34404119849205017
- Accuracy: 0.8843120070113936
- Macro F1: 0.8771237753798016
- Micro F1: 0.8843120070113936
- Weighted F1: 0.8843498914288083
- Macro Precision: 0.8745249813256932
- Micro Precision: 0.8843120070113936
- Weighted Precision: 0.8854719661321065
- Macro Recall: 0.8812563739901838
- Micro Recall: 0.8843120070113936
- Weighted Recall: 0.8843120070113936
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/models/dee4hf/autotrain-deephate2-1093539673
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("dee4hf/autotrain-deephate2-1093539673", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("dee4hf/autotrain-deephate2-1093539673", use_auth_token=True)
inputs = tokenizer("I love AutoTrain", return_tensors="pt")
outputs = model(**inputs)
```
|
miyoung/newProject
|
miyoung
| 2022-07-06T01:16:16Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-06-17T04:39:53Z |
### What's Hugging Face?!!!
https://towardsdatascience.com/whats-hugging-face-122f4e7eb11a
Hugging Face is a community and data science platform that provides: Tools that enable users to build, train and deploy ML models based on open source (OS) code and technologies!!!!!.
|
emilys/twitter-roberta-base-dec2021-WNUT
|
emilys
| 2022-07-05T22:26:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"token-classification",
"generated_from_trainer",
"dataset:wnut_17",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-05T22:21:52Z |
---
tags:
- generated_from_trainer
datasets:
- wnut_17
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: twitter-roberta-base-dec2021-WNUT
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wnut_17
type: wnut_17
args: wnut_17
metrics:
- name: Precision
type: precision
value: 0.7111716621253406
- name: Recall
type: recall
value: 0.6244019138755981
- name: F1
type: f1
value: 0.664968152866242
- name: Accuracy
type: accuracy
value: 0.9642789042140724
---
<!-- 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-dec2021-WNUT
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-dec2021](https://huggingface.co/cardiffnlp/twitter-roberta-base-dec2021) on the wnut_17 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2152
- Precision: 0.7112
- Recall: 0.6244
- F1: 0.6650
- Accuracy: 0.9643
## 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: 1024
- 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 | 0.46 | 25 | 0.2818 | 0.0982 | 0.0383 | 0.0551 | 0.9241 |
| No log | 0.93 | 50 | 0.2158 | 0.6181 | 0.4569 | 0.5254 | 0.9480 |
| No log | 1.39 | 75 | 0.1930 | 0.6682 | 0.5347 | 0.5940 | 0.9555 |
| No log | 1.85 | 100 | 0.1728 | 0.6583 | 0.5646 | 0.6079 | 0.9594 |
| No log | 2.31 | 125 | 0.1787 | 0.7050 | 0.5718 | 0.6314 | 0.9619 |
| No log | 2.78 | 150 | 0.2051 | 0.6979 | 0.5251 | 0.5993 | 0.9587 |
| No log | 3.24 | 175 | 0.1755 | 0.7172 | 0.5945 | 0.6501 | 0.9621 |
| No log | 3.7 | 200 | 0.1720 | 0.6943 | 0.6304 | 0.6608 | 0.9645 |
| No log | 4.17 | 225 | 0.1873 | 0.7203 | 0.6316 | 0.6730 | 0.9646 |
| No log | 4.63 | 250 | 0.1781 | 0.6934 | 0.6196 | 0.6545 | 0.9638 |
| No log | 5.09 | 275 | 0.1953 | 0.7040 | 0.6172 | 0.6577 | 0.9631 |
| No log | 5.56 | 300 | 0.1953 | 0.7223 | 0.6316 | 0.6739 | 0.9642 |
| No log | 6.02 | 325 | 0.1839 | 0.7008 | 0.6471 | 0.6729 | 0.9648 |
| No log | 6.48 | 350 | 0.1995 | 0.716 | 0.6423 | 0.6772 | 0.9650 |
| No log | 6.94 | 375 | 0.2056 | 0.7251 | 0.6184 | 0.6675 | 0.9640 |
| No log | 7.41 | 400 | 0.2044 | 0.7065 | 0.6220 | 0.6616 | 0.9640 |
| No log | 7.87 | 425 | 0.2042 | 0.7201 | 0.6400 | 0.6776 | 0.9650 |
| No log | 8.33 | 450 | 0.2247 | 0.7280 | 0.6244 | 0.6722 | 0.9638 |
| No log | 8.8 | 475 | 0.2060 | 0.7064 | 0.6447 | 0.6742 | 0.9649 |
| 0.0675 | 9.26 | 500 | 0.2152 | 0.7112 | 0.6244 | 0.6650 | 0.9643 |
| 0.0675 | 9.72 | 525 | 0.2086 | 0.7070 | 0.6495 | 0.6771 | 0.9650 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Evelyn18/distilbert-base-uncased-becas-4
|
Evelyn18
| 2022-07-05T21:55:19Z | 20 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-01T02:20:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: distilbert-base-uncased-becas-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. -->
# distilbert-base-uncased-becas-4
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1357
## 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: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 9 | 4.9618 |
| No log | 2.0 | 18 | 4.1071 |
| No log | 3.0 | 27 | 3.5438 |
| No log | 4.0 | 36 | 3.2115 |
| No log | 5.0 | 45 | 2.9524 |
| No log | 6.0 | 54 | 3.0645 |
| No log | 7.0 | 63 | 2.9351 |
| No log | 8.0 | 72 | 3.1037 |
| No log | 9.0 | 81 | 3.1132 |
| No log | 10.0 | 90 | 3.1357 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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