modelId
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-30 00:39:23
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 526
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-08-30 00:39:08
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tezign/BERT-LSTM-based-ABSA
|
tezign
| 2022-07-20T10:14:35Z | 32 | 3 |
transformers
|
[
"transformers",
"pytorch",
"BertABSAForSequenceClassification",
"text-classification",
"aspect-term-sentiment-analysis",
"ATSA",
"custom_code",
"en",
"dataset:semeval2014",
"arxiv:2002.04815",
"autotrain_compatible",
"region:us"
] |
text-classification
| 2022-06-28T07:02:53Z |
---
language: en
tags:
- aspect-term-sentiment-analysis
- pytorch
- ATSA
datasets:
- semeval2014
widget:
- text: "[CLS] The appearance is very nice, but the battery life is poor. [SEP] appearance [SEP] "
---
# Note
`Aspect term sentiment analysis`
BERT LSTM based baseline, based on https://github.com/avinashsai/BERT-Aspect *BERT LSTM* implementation.The model trained on SemEval2014-Task 4 laptop and restaurant datasets.
Our Github repo: https://github.com/tezignlab/BERT-LSTM-based-ABSA
Code for the paper "Utilizing BERT Intermediate Layers for Aspect Based Sentiment Analysis and Natural Language Inference" https://arxiv.org/pdf/2002.04815.pdf.
# Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification, TextClassificationPipeline
MODEL = "tezign/BERT-LSTM-based-ABSA"
tokenizer = AutoTokenizer.from_pretrained(MODEL)
model = AutoModelForSequenceClassification.from_pretrained(MODEL, trust_remote_code=True)
classifier = TextClassificationPipeline(model=model, tokenizer=tokenizer)
result = classifier([
{"text": "The appearance is very nice, but the battery life is poor", "text_pair": "appearance"},
{"text": "The appearance is very nice, but the battery life is poor", "text_pair": "battery"}
],
function_to_apply="softmax")
print(result)
"""
print result
>> [{'label': 'positive', 'score': 0.9129462838172913}, {'label': 'negative', 'score': 0.8834680914878845}]
"""
```
|
jamie613/mt5_correct_puntuation
|
jamie613
| 2022-07-20T10:00:44Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-14T01:43:41Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: mt5_correct_puntuation_v3
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. -->
# mt5_correct_puntuation
本模型使用中文維基百科語料微調 [google/mt5-base](https://huggingface.co/google/mt5-base)預訓練模型之中文標點符號訂正器。目前之準確率為 0.794。
This is a [google/mt5-base](https://huggingface.co/google/mt5-base) model trained on Mandarin Wikipedia corpus and finetuned for Mandarin punctuation correction. Currently the accuracy is 0.794.
## Datasets
模型使用中文維基百科公開資料微調。將取得的文本以「。」或「，」切分為不超過100字的句子。因為逗號和句號數量壓倒性地多，為盡量平衡資料集，僅保留包含冒號、分號、驚嘆號、問號的句子，作為正確句。將正確句之「，。：；、！？」隨機以「，。：；、！？」，製作為不正確句。訓練用句子共有291,112句。
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Manishkalra/discourse_classification
|
Manishkalra
| 2022-07-20T09:48:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-07T11:13:57Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: discourse_classification
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. -->
# discourse_classification
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: 0.7639
- Accuracy: 0.6649
- F1: 0.6649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7565 | 1.0 | 1839 | 0.7589 | 0.6635 | 0.6635 |
| 0.6693 | 2.0 | 3678 | 0.7639 | 0.6649 | 0.6649 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilbert_oscarth_0080
|
bigmorning
| 2022-07-20T09:29:02Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-20T09:28:43Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_oscarth_0080
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. -->
# distilbert_oscarth_0080
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1236
- Validation Loss: 1.0821
- Epoch: 79
## 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 |
|:----------:|:---------------:|:-----:|
| 4.1327 | 2.9983 | 0 |
| 2.7813 | 2.4562 | 1 |
| 2.4194 | 2.2066 | 2 |
| 2.2231 | 2.0562 | 3 |
| 2.0894 | 1.9450 | 4 |
| 1.9905 | 1.8621 | 5 |
| 1.9148 | 1.7941 | 6 |
| 1.8508 | 1.7363 | 7 |
| 1.7976 | 1.6909 | 8 |
| 1.7509 | 1.6488 | 9 |
| 1.7126 | 1.6124 | 10 |
| 1.6764 | 1.5835 | 11 |
| 1.6450 | 1.5521 | 12 |
| 1.6175 | 1.5282 | 13 |
| 1.5919 | 1.5045 | 14 |
| 1.5679 | 1.4833 | 15 |
| 1.5476 | 1.4627 | 16 |
| 1.5271 | 1.4498 | 17 |
| 1.5098 | 1.4270 | 18 |
| 1.4909 | 1.4161 | 19 |
| 1.4760 | 1.3995 | 20 |
| 1.4609 | 1.3864 | 21 |
| 1.4475 | 1.3717 | 22 |
| 1.4333 | 1.3590 | 23 |
| 1.4203 | 1.3478 | 24 |
| 1.4093 | 1.3403 | 25 |
| 1.3980 | 1.3296 | 26 |
| 1.3875 | 1.3176 | 27 |
| 1.3773 | 1.3094 | 28 |
| 1.3674 | 1.3011 | 29 |
| 1.3579 | 1.2920 | 30 |
| 1.3497 | 1.2826 | 31 |
| 1.3400 | 1.2764 | 32 |
| 1.3326 | 1.2694 | 33 |
| 1.3236 | 1.2635 | 34 |
| 1.3169 | 1.2536 | 35 |
| 1.3096 | 1.2477 | 36 |
| 1.3024 | 1.2408 | 37 |
| 1.2957 | 1.2364 | 38 |
| 1.2890 | 1.2296 | 39 |
| 1.2818 | 1.2236 | 40 |
| 1.2751 | 1.2168 | 41 |
| 1.2691 | 1.2126 | 42 |
| 1.2644 | 1.2044 | 43 |
| 1.2583 | 1.2008 | 44 |
| 1.2529 | 1.1962 | 45 |
| 1.2473 | 1.1919 | 46 |
| 1.2416 | 1.1857 | 47 |
| 1.2365 | 1.1812 | 48 |
| 1.2318 | 1.1765 | 49 |
| 1.2273 | 1.1738 | 50 |
| 1.2224 | 1.1672 | 51 |
| 1.2177 | 1.1673 | 52 |
| 1.2132 | 1.1595 | 53 |
| 1.2084 | 1.1564 | 54 |
| 1.2033 | 1.1518 | 55 |
| 1.1993 | 1.1481 | 56 |
| 1.1966 | 1.1445 | 57 |
| 1.1924 | 1.1412 | 58 |
| 1.1876 | 1.1378 | 59 |
| 1.1834 | 1.1340 | 60 |
| 1.1806 | 1.1329 | 61 |
| 1.1783 | 1.1289 | 62 |
| 1.1739 | 1.1251 | 63 |
| 1.1705 | 1.1223 | 64 |
| 1.1669 | 1.1192 | 65 |
| 1.1628 | 1.1172 | 66 |
| 1.1599 | 1.1140 | 67 |
| 1.1570 | 1.1084 | 68 |
| 1.1526 | 1.1081 | 69 |
| 1.1496 | 1.1043 | 70 |
| 1.1463 | 1.0999 | 71 |
| 1.1438 | 1.1006 | 72 |
| 1.1397 | 1.0964 | 73 |
| 1.1378 | 1.0918 | 74 |
| 1.1347 | 1.0917 | 75 |
| 1.1319 | 1.0889 | 76 |
| 1.1296 | 1.0855 | 77 |
| 1.1271 | 1.0848 | 78 |
| 1.1236 | 1.0821 | 79 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
tokeron/alephbert-finetuned-metaphor-detection
|
tokeron
| 2022-07-20T09:21:13Z | 13 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"he",
"dataset:Piyutim",
"license:afl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-20T07:06:57Z |
---
license: afl-3.0
language:
- he
tags:
- token-classification
datasets:
- Piyutim
model:
- onlplab/alephbert-base
metrics:
- f1
widget:
- text: "נשבר לי הגב"
example_title: "Broken back"
- text: "ש לו לב זהב"
example_title: "Golden heart"
---
This is a token-classification model.
This model is AlephBert fine-tuned on detecting metaphors from Hebrew Piyutim
model-index:
- name: tokeron/alephbert-finetuned-metaphor-detection
results: []
# model
This model fine-tunes onlplab/alephbert-base model on Piyutim dataset.
### About Us
Created by Michael Toker in collaboration with Yonatan Belinkov, Benny Kornfeld, Oren Mishali, and Ophir Münz-Manor.
For more cooperation, please contact email:
tok@campus.technion.ac.il
|
jaeyeon/korean-aihub-learning-2
|
jaeyeon
| 2022-07-20T08:31:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-20T07:38:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: korean-aihub-learning-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# korean-aihub-learning-2
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9945
- Wer: 0.9533
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 0.99 | 35 | 46.3840 | 1.0 |
| No log | 1.99 | 70 | 26.0949 | 1.0 |
| 37.1581 | 2.99 | 105 | 19.0168 | 1.0 |
| 37.1581 | 3.99 | 140 | 13.3294 | 1.0 |
| 37.1581 | 4.99 | 175 | 7.9410 | 1.0 |
| 12.5054 | 5.99 | 210 | 5.0323 | 1.0 |
| 12.5054 | 6.99 | 245 | 4.6242 | 1.0 |
| 12.5054 | 7.99 | 280 | 4.6206 | 1.0 |
| 4.8394 | 8.99 | 315 | 4.5820 | 1.0 |
| 4.8394 | 9.99 | 350 | 4.5629 | 1.0 |
| 4.8394 | 10.99 | 385 | 4.5385 | 1.0 |
| 4.6489 | 11.99 | 420 | 4.5627 | 1.0 |
| 4.6489 | 12.99 | 455 | 4.5276 | 1.0 |
| 4.6489 | 13.99 | 490 | 4.5292 | 1.0 |
| 4.5654 | 14.99 | 525 | 4.5179 | 1.0 |
| 4.5654 | 15.99 | 560 | 4.4928 | 1.0 |
| 4.5654 | 16.99 | 595 | 4.4791 | 1.0 |
| 4.521 | 17.99 | 630 | 4.4649 | 1.0 |
| 4.521 | 18.99 | 665 | 4.4588 | 1.0 |
| 4.3529 | 19.99 | 700 | 4.3632 | 1.0 |
| 4.3529 | 20.99 | 735 | 4.2990 | 1.0 |
| 4.3529 | 21.99 | 770 | 4.2326 | 0.9988 |
| 4.1301 | 22.99 | 805 | 4.0843 | 1.0 |
| 4.1301 | 23.99 | 840 | 3.9784 | 0.9975 |
| 4.1301 | 24.99 | 875 | 3.7876 | 1.0 |
| 3.7047 | 25.99 | 910 | 3.6109 | 0.9988 |
| 3.7047 | 26.99 | 945 | 3.4049 | 0.9828 |
| 3.7047 | 27.99 | 980 | 3.1913 | 0.9606 |
| 3.006 | 28.99 | 1015 | 3.0567 | 0.9508 |
| 3.006 | 29.99 | 1050 | 2.9945 | 0.9533 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
knkarthick/bart-large-xsum-samsum
|
knkarthick
| 2022-07-20T08:29:15Z | 49 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: "Hannah: Hey, do you have Betty's number?\nAmanda: Lemme check\nAmanda: Sorry,\
\ can't find it.\nAmanda: Ask Larry\nAmanda: He called her last time we were at\
\ the park together\nHannah: I don't know him well\nAmanda: Don't be shy, he's\
\ very nice\nHannah: If you say so..\nHannah: I'd rather you texted him\nAmanda:\
\ Just text him \U0001F642\nHannah: Urgh.. Alright\nHannah: Bye\nAmanda: Bye bye\n"
model-index:
- name: bart-large-xsum-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: 'SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization'
type: samsum
metrics:
- name: Validation ROUGE-1
type: rouge-1
value: 54.3921
- name: Validation ROUGE-2
type: rouge-2
value: 29.8078
- name: Validation ROUGE-L
type: rouge-l
value: 45.1543
- name: Test ROUGE-1
type: rouge-1
value: 53.3059
- name: Test ROUGE-2
type: rouge-2
value: 28.355
- name: Test ROUGE-L
type: rouge-l
value: 44.0953
- task:
type: summarization
name: Summarization
dataset:
name: samsum
type: samsum
config: samsum
split: train
metrics:
- name: ROUGE-1
type: rouge
value: 46.2492
verified: true
- name: ROUGE-2
type: rouge
value: 21.346
verified: true
- name: ROUGE-L
type: rouge
value: 37.2787
verified: true
- name: ROUGE-LSUM
type: rouge
value: 42.1317
verified: true
- name: loss
type: loss
value: 1.6859958171844482
verified: true
- name: gen_len
type: gen_len
value: 23.7103
verified: true
---
## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 🙂
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye
'''
summarizer(conversation)
```
|
knkarthick/meeting-summary-samsum
|
knkarthick
| 2022-07-20T08:28:58Z | 43 | 8 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"seq2seq",
"summarization",
"en",
"dataset:samsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-03-02T23:29:05Z |
---
language: en
tags:
- bart
- seq2seq
- summarization
license: apache-2.0
datasets:
- samsum
widget:
- text: |
Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 🙂
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye
model-index:
- name: bart-large-xsum-samsum
results:
- task:
name: Abstractive Text Summarization
type: abstractive-text-summarization
dataset:
name: "SAMSum Corpus: A Human-annotated Dialogue Dataset for Abstractive Summarization"
type: samsum
metrics:
- name: Validation ROUGE-1
type: rouge-1
value: 54.3921
- name: Validation ROUGE-2
type: rouge-2
value: 29.8078
- name: Validation ROUGE-L
type: rouge-l
value: 45.1543
- name: Test ROUGE-1
type: rouge-1
value: 53.3059
- name: Test ROUGE-2
type: rouge-2
value: 28.355
- name: Test ROUGE-L
type: rouge-l
value: 44.0953
---
## `bart-large-xsum-samsum`
This model was obtained by fine-tuning `facebook/bart-large-xsum` on [Samsum](https://huggingface.co/datasets/samsum) dataset.
## Usage
```python
from transformers import pipeline
summarizer = pipeline("summarization", model="knkarthick/bart-large-xsum-samsum")
conversation = '''Hannah: Hey, do you have Betty's number?
Amanda: Lemme check
Amanda: Sorry, can't find it.
Amanda: Ask Larry
Amanda: He called her last time we were at the park together
Hannah: I don't know him well
Amanda: Don't be shy, he's very nice
Hannah: If you say so..
Hannah: I'd rather you texted him
Amanda: Just text him 🙂
Hannah: Urgh.. Alright
Hannah: Bye
Amanda: Bye bye
'''
summarizer(conversation)
```
|
lqdisme/distilbert-base-uncased-finetuned-squad
|
lqdisme
| 2022-07-20T08:03:52Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-20T04:25:02Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
FAICAM/distilbert-base-uncased-finetuned-cola
|
FAICAM
| 2022-07-20T07:54:29Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-20T07:47:13Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: FAICAM/distilbert-base-uncased-finetuned-cola
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. -->
# FAICAM/distilbert-base-uncased-finetuned-cola
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1871
- Validation Loss: 0.4889
- Train Matthews Correlation: 0.5644
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2670, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Matthews Correlation | Epoch |
|:----------:|:---------------:|:--------------------------:|:-----:|
| 0.5111 | 0.5099 | 0.4325 | 0 |
| 0.3227 | 0.4561 | 0.5453 | 1 |
| 0.1871 | 0.4889 | 0.5644 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
wenkai-li/distilbert-base-uncased-finetuned-wikiandmark_epoch20
|
wenkai-li
| 2022-07-20T07:33:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-20T02:43:58Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-wikiandmark_epoch20
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-wikiandmark_epoch20
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0561
- Accuracy: 0.9944
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.0224 | 1.0 | 1859 | 0.0277 | 0.9919 |
| 0.0103 | 2.0 | 3718 | 0.0298 | 0.9925 |
| 0.0047 | 3.0 | 5577 | 0.0429 | 0.9924 |
| 0.0038 | 4.0 | 7436 | 0.0569 | 0.9922 |
| 0.0019 | 5.0 | 9295 | 0.0554 | 0.9936 |
| 0.0028 | 6.0 | 11154 | 0.0575 | 0.9928 |
| 0.002 | 7.0 | 13013 | 0.0544 | 0.9926 |
| 0.0017 | 8.0 | 14872 | 0.0553 | 0.9935 |
| 0.001 | 9.0 | 16731 | 0.0498 | 0.9924 |
| 0.0001 | 10.0 | 18590 | 0.0398 | 0.9934 |
| 0.0 | 11.0 | 20449 | 0.0617 | 0.9935 |
| 0.0002 | 12.0 | 22308 | 0.0561 | 0.9944 |
| 0.0002 | 13.0 | 24167 | 0.0755 | 0.9934 |
| 0.0 | 14.0 | 26026 | 0.0592 | 0.9941 |
| 0.0 | 15.0 | 27885 | 0.0572 | 0.9939 |
| 0.0 | 16.0 | 29744 | 0.0563 | 0.9941 |
| 0.0 | 17.0 | 31603 | 0.0587 | 0.9936 |
| 0.0005 | 18.0 | 33462 | 0.0673 | 0.9937 |
| 0.0 | 19.0 | 35321 | 0.0651 | 0.9933 |
| 0.0 | 20.0 | 37180 | 0.0683 | 0.9936 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilbert_oscarth_0060
|
bigmorning
| 2022-07-20T05:21:20Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-20T05:20:36Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_oscarth_0060
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. -->
# distilbert_oscarth_0060
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.1876
- Validation Loss: 1.1378
- Epoch: 59
## 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 |
|:----------:|:---------------:|:-----:|
| 4.1327 | 2.9983 | 0 |
| 2.7813 | 2.4562 | 1 |
| 2.4194 | 2.2066 | 2 |
| 2.2231 | 2.0562 | 3 |
| 2.0894 | 1.9450 | 4 |
| 1.9905 | 1.8621 | 5 |
| 1.9148 | 1.7941 | 6 |
| 1.8508 | 1.7363 | 7 |
| 1.7976 | 1.6909 | 8 |
| 1.7509 | 1.6488 | 9 |
| 1.7126 | 1.6124 | 10 |
| 1.6764 | 1.5835 | 11 |
| 1.6450 | 1.5521 | 12 |
| 1.6175 | 1.5282 | 13 |
| 1.5919 | 1.5045 | 14 |
| 1.5679 | 1.4833 | 15 |
| 1.5476 | 1.4627 | 16 |
| 1.5271 | 1.4498 | 17 |
| 1.5098 | 1.4270 | 18 |
| 1.4909 | 1.4161 | 19 |
| 1.4760 | 1.3995 | 20 |
| 1.4609 | 1.3864 | 21 |
| 1.4475 | 1.3717 | 22 |
| 1.4333 | 1.3590 | 23 |
| 1.4203 | 1.3478 | 24 |
| 1.4093 | 1.3403 | 25 |
| 1.3980 | 1.3296 | 26 |
| 1.3875 | 1.3176 | 27 |
| 1.3773 | 1.3094 | 28 |
| 1.3674 | 1.3011 | 29 |
| 1.3579 | 1.2920 | 30 |
| 1.3497 | 1.2826 | 31 |
| 1.3400 | 1.2764 | 32 |
| 1.3326 | 1.2694 | 33 |
| 1.3236 | 1.2635 | 34 |
| 1.3169 | 1.2536 | 35 |
| 1.3096 | 1.2477 | 36 |
| 1.3024 | 1.2408 | 37 |
| 1.2957 | 1.2364 | 38 |
| 1.2890 | 1.2296 | 39 |
| 1.2818 | 1.2236 | 40 |
| 1.2751 | 1.2168 | 41 |
| 1.2691 | 1.2126 | 42 |
| 1.2644 | 1.2044 | 43 |
| 1.2583 | 1.2008 | 44 |
| 1.2529 | 1.1962 | 45 |
| 1.2473 | 1.1919 | 46 |
| 1.2416 | 1.1857 | 47 |
| 1.2365 | 1.1812 | 48 |
| 1.2318 | 1.1765 | 49 |
| 1.2273 | 1.1738 | 50 |
| 1.2224 | 1.1672 | 51 |
| 1.2177 | 1.1673 | 52 |
| 1.2132 | 1.1595 | 53 |
| 1.2084 | 1.1564 | 54 |
| 1.2033 | 1.1518 | 55 |
| 1.1993 | 1.1481 | 56 |
| 1.1966 | 1.1445 | 57 |
| 1.1924 | 1.1412 | 58 |
| 1.1876 | 1.1378 | 59 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bigmorning/distilgpt_oscarth_0040
|
bigmorning
| 2022-07-20T03:34:29Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-20T03:34:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilgpt_oscarth_0040
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. -->
# distilgpt_oscarth_0040
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.0004
- Validation Loss: 2.8864
- Epoch: 39
## 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 |
|:----------:|:---------------:|:-----:|
| 5.6021 | 4.5759 | 0 |
| 4.4536 | 4.1235 | 1 |
| 4.1386 | 3.9013 | 2 |
| 3.9546 | 3.7563 | 3 |
| 3.8255 | 3.6477 | 4 |
| 3.7271 | 3.5617 | 5 |
| 3.6488 | 3.4936 | 6 |
| 3.5844 | 3.4379 | 7 |
| 3.5301 | 3.3891 | 8 |
| 3.4833 | 3.3448 | 9 |
| 3.4427 | 3.3098 | 10 |
| 3.4068 | 3.2750 | 11 |
| 3.3749 | 3.2425 | 12 |
| 3.3462 | 3.2211 | 13 |
| 3.3202 | 3.1941 | 14 |
| 3.2964 | 3.1720 | 15 |
| 3.2749 | 3.1512 | 16 |
| 3.2548 | 3.1322 | 17 |
| 3.2363 | 3.1141 | 18 |
| 3.2188 | 3.0982 | 19 |
| 3.2025 | 3.0818 | 20 |
| 3.1871 | 3.0678 | 21 |
| 3.1724 | 3.0533 | 22 |
| 3.1583 | 3.0376 | 23 |
| 3.1446 | 3.0256 | 24 |
| 3.1318 | 3.0122 | 25 |
| 3.1195 | 3.0016 | 26 |
| 3.1079 | 2.9901 | 27 |
| 3.0968 | 2.9826 | 28 |
| 3.0863 | 2.9711 | 29 |
| 3.0761 | 2.9593 | 30 |
| 3.0665 | 2.9514 | 31 |
| 3.0572 | 2.9432 | 32 |
| 3.0483 | 2.9347 | 33 |
| 3.0396 | 2.9250 | 34 |
| 3.0313 | 2.9160 | 35 |
| 3.0232 | 2.9095 | 36 |
| 3.0153 | 2.9028 | 37 |
| 3.0078 | 2.8949 | 38 |
| 3.0004 | 2.8864 | 39 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Siyong/MT_RN_LM
|
Siyong
| 2022-07-20T03:25:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-20T01:38:19Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: run1
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. -->
# run1
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: 1.6666
- Wer: 0.6375
- Cer: 0.3170
## 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: 2000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|
| 1.0564 | 2.36 | 2000 | 2.3456 | 0.9628 | 0.5549 |
| 0.5071 | 4.73 | 4000 | 2.0652 | 0.9071 | 0.5115 |
| 0.3952 | 7.09 | 6000 | 2.3649 | 0.9108 | 0.4628 |
| 0.3367 | 9.46 | 8000 | 1.7615 | 0.8253 | 0.4348 |
| 0.2765 | 11.82 | 10000 | 1.6151 | 0.7937 | 0.4087 |
| 0.2493 | 14.18 | 12000 | 1.4976 | 0.7881 | 0.3905 |
| 0.2318 | 16.55 | 14000 | 1.6731 | 0.8160 | 0.3925 |
| 0.2074 | 18.91 | 16000 | 1.5822 | 0.7658 | 0.3913 |
| 0.1825 | 21.28 | 18000 | 1.5442 | 0.7361 | 0.3704 |
| 0.1824 | 23.64 | 20000 | 1.5988 | 0.7621 | 0.3711 |
| 0.1699 | 26.0 | 22000 | 1.4261 | 0.7119 | 0.3490 |
| 0.158 | 28.37 | 24000 | 1.7482 | 0.7658 | 0.3648 |
| 0.1385 | 30.73 | 26000 | 1.4103 | 0.6784 | 0.3348 |
| 0.1199 | 33.1 | 28000 | 1.5214 | 0.6636 | 0.3273 |
| 0.116 | 35.46 | 30000 | 1.4288 | 0.7212 | 0.3486 |
| 0.1071 | 37.83 | 32000 | 1.5344 | 0.7138 | 0.3411 |
| 0.1007 | 40.19 | 34000 | 1.4501 | 0.6691 | 0.3237 |
| 0.0943 | 42.55 | 36000 | 1.5367 | 0.6859 | 0.3265 |
| 0.0844 | 44.92 | 38000 | 1.5321 | 0.6599 | 0.3273 |
| 0.0762 | 47.28 | 40000 | 1.6721 | 0.6264 | 0.3142 |
| 0.0778 | 49.65 | 42000 | 1.6666 | 0.6375 | 0.3170 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.12.0+cu113
- Datasets 2.0.0
- Tokenizers 0.12.1
|
Willaim/Bl00m
|
Willaim
| 2022-07-20T02:53:53Z | 0 | 0 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-07-20T02:32:19Z |
---
license: bigscience-bloom-rail-1.0
---
import requests
API_URL = "https://api-inference.huggingface.co/models/bigscience/bloom"
headers = {"Authorization": "Bearer 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"}
def query(payload):
response = requests.post(API_URL, headers=headers, json=payload)
return response.json()
output = query({
"inputs": "Can you please let us know more details about your ",
})
|
commanderstrife/bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
|
commanderstrife
| 2022-07-20T02:51:04Z | 17 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:bc2gm_corpus",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-20T02:00:12Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- bc2gm_corpus
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: bc2gm_corpus
type: bc2gm_corpus
args: bc2gm_corpus
metrics:
- name: Precision
type: precision
value: 0.7853881278538812
- name: Recall
type: recall
value: 0.8158102766798419
- name: F1
type: f1
value: 0.8003101977510663
- name: Accuracy
type: accuracy
value: 0.9758965601366187
---
<!-- 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. -->
# bc2gm_corpus-Bio_ClinicalBERT-finetuned-ner
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the bc2gm_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1505
- Precision: 0.7854
- Recall: 0.8158
- F1: 0.8003
- Accuracy: 0.9759
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0981 | 1.0 | 782 | 0.0712 | 0.7228 | 0.7948 | 0.7571 | 0.9724 |
| 0.0509 | 2.0 | 1564 | 0.0687 | 0.7472 | 0.8199 | 0.7818 | 0.9746 |
| 0.0121 | 3.0 | 2346 | 0.0740 | 0.7725 | 0.8011 | 0.7866 | 0.9747 |
| 0.0001 | 4.0 | 3128 | 0.1009 | 0.7618 | 0.8251 | 0.7922 | 0.9741 |
| 0.0042 | 5.0 | 3910 | 0.1106 | 0.7757 | 0.8185 | 0.7965 | 0.9754 |
| 0.0015 | 6.0 | 4692 | 0.1182 | 0.7812 | 0.8111 | 0.7958 | 0.9758 |
| 0.0001 | 7.0 | 5474 | 0.1283 | 0.7693 | 0.8275 | 0.7973 | 0.9753 |
| 0.0072 | 8.0 | 6256 | 0.1376 | 0.7863 | 0.8158 | 0.8008 | 0.9762 |
| 0.0045 | 9.0 | 7038 | 0.1468 | 0.7856 | 0.8180 | 0.8015 | 0.9761 |
| 0.0 | 10.0 | 7820 | 0.1505 | 0.7854 | 0.8158 | 0.8003 | 0.9759 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
luomingshuang/icefall_asr_tedlium3_transducer_stateless
|
luomingshuang
| 2022-07-20T02:44:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-03T02:56:02Z |
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/233
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
The model was trained on full [TEDLium3](https://www.openslr.org/51) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/tedlium3/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir transducer_stateless/exp \
--max-duration 300
```
## Evaluation results
The decoding results (WER%) on TEDLium3 (dev and test) are listed below, we got this result by averaging models from epoch 19 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 7.19 | 6.70 | --epoch 29, --avg 11, --max-duration 100 |
| beam search (beam size 4) | 7.02 | 6.36 | --epoch 29, --avg 11, --max-duration 100 |
| modified beam search (beam size 4) | 6.91 | 6.33 | --epoch 29, --avg 11, --max-duration 100 |
|
luomingshuang/icefall_asr_tedlium3_pruned_transducer_stateless
|
luomingshuang
| 2022-07-20T01:56:54Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-03-21T03:45:41Z |
<<<<<<< HEAD
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/261
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless models for the TEDLium3 dataset with icefall.
The model was trained on full [TEDLium3](https://www.openslr.org/51) with the scripts in [icefall](https://github.com/k2-fsa/icefall).
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/tedlium3/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1,2,3"
./pruned_transducer_stateless/train.py \
--world-size 4 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless/exp \
--max-duration 300
```
## Evaluation results
The decoding results (WER%) on TEDLium3 (dev and test) are listed below, we got this result by averaging models from epoch 17 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 7.27 | 6.69 | --epoch 29, --avg 13, --max-duration 100 |
| beam search (beam size 4) | 6.70 | 6.04 | --epoch 29, --avg 13, --max-duration 100 |
| modified beam search (beam size 4) | 6.77 | 6.14 | --epoch 29, --avg 13, --max-duration 100 |
|
bigmorning/distilbert_oscarth_0040
|
bigmorning
| 2022-07-20T01:27:25Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-20T01:27:11Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: distilbert_oscarth_0040
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. -->
# distilbert_oscarth_0040
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.2890
- Validation Loss: 1.2296
- Epoch: 39
## 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 |
|:----------:|:---------------:|:-----:|
| 4.1327 | 2.9983 | 0 |
| 2.7813 | 2.4562 | 1 |
| 2.4194 | 2.2066 | 2 |
| 2.2231 | 2.0562 | 3 |
| 2.0894 | 1.9450 | 4 |
| 1.9905 | 1.8621 | 5 |
| 1.9148 | 1.7941 | 6 |
| 1.8508 | 1.7363 | 7 |
| 1.7976 | 1.6909 | 8 |
| 1.7509 | 1.6488 | 9 |
| 1.7126 | 1.6124 | 10 |
| 1.6764 | 1.5835 | 11 |
| 1.6450 | 1.5521 | 12 |
| 1.6175 | 1.5282 | 13 |
| 1.5919 | 1.5045 | 14 |
| 1.5679 | 1.4833 | 15 |
| 1.5476 | 1.4627 | 16 |
| 1.5271 | 1.4498 | 17 |
| 1.5098 | 1.4270 | 18 |
| 1.4909 | 1.4161 | 19 |
| 1.4760 | 1.3995 | 20 |
| 1.4609 | 1.3864 | 21 |
| 1.4475 | 1.3717 | 22 |
| 1.4333 | 1.3590 | 23 |
| 1.4203 | 1.3478 | 24 |
| 1.4093 | 1.3403 | 25 |
| 1.3980 | 1.3296 | 26 |
| 1.3875 | 1.3176 | 27 |
| 1.3773 | 1.3094 | 28 |
| 1.3674 | 1.3011 | 29 |
| 1.3579 | 1.2920 | 30 |
| 1.3497 | 1.2826 | 31 |
| 1.3400 | 1.2764 | 32 |
| 1.3326 | 1.2694 | 33 |
| 1.3236 | 1.2635 | 34 |
| 1.3169 | 1.2536 | 35 |
| 1.3096 | 1.2477 | 36 |
| 1.3024 | 1.2408 | 37 |
| 1.2957 | 1.2364 | 38 |
| 1.2890 | 1.2296 | 39 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
steven123/Check_Aligned_Teeth
|
steven123
| 2022-07-20T00:59:05Z | 57 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-20T00:58:54Z |
---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: Check_Aligned_Teeth
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9473684430122375
---
# Check_Aligned_Teeth
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Aligned Teeth
#### Crooked Teeth
|
frgfm/cspdarknet53
|
frgfm
| 2022-07-20T00:57:40Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1911.11929",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
datasets:
- frgfm/imagenette
---
# CSP-Darknet-53 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The CSP-Darknet-53 architecture was introduced in [this paper](https://arxiv.org/pdf/1911.11929.pdf).
## Model description
The core idea of the author is to change the convolutional stage by adding cross stage partial blocks in the architecture.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/cspdarknet53").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-1911-11929,
author = {Chien{-}Yao Wang and
Hong{-}Yuan Mark Liao and
I{-}Hau Yeh and
Yueh{-}Hua Wu and
Ping{-}Yang Chen and
Jun{-}Wei Hsieh},
title = {CSPNet: {A} New Backbone that can Enhance Learning Capability of {CNN}},
journal = {CoRR},
volume = {abs/1911.11929},
year = {2019},
url = {http://arxiv.org/abs/1911.11929},
eprinttype = {arXiv},
eprint = {1911.11929},
timestamp = {Tue, 03 Dec 2019 20:41:07 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-1911-11929.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/darknet53
|
frgfm
| 2022-07-20T00:57:28Z | 67 | 0 |
transformers
|
[
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1804.02767",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
datasets:
- frgfm/imagenette
---
# Darknet-53 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The Darknet-53 architecture was introduced in [this paper](https://pjreddie.com/media/files/papers/YOLOv3.pdf).
## Model description
The core idea of the author is to increase the depth of the Darknet-19 architecture, and adding shortcut connections to ease the gradient propagation.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/darknet53").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-1804-02767,
author = {Joseph Redmon and
Ali Farhadi},
title = {YOLOv3: An Incremental Improvement},
journal = {CoRR},
volume = {abs/1804.02767},
year = {2018},
url = {http://arxiv.org/abs/1804.02767},
eprinttype = {arXiv},
eprint = {1804.02767},
timestamp = {Mon, 13 Aug 2018 16:48:24 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1804-02767.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/darknet19
|
frgfm
| 2022-07-20T00:57:15Z | 35 | 0 |
transformers
|
[
"transformers",
"pytorch",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1612.08242",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
datasets:
- frgfm/imagenette
---
# Darknet-19 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The Darknet-19 architecture was introduced in [this paper](https://pjreddie.com/media/files/papers/YOLO9000.pdf).
## Model description
The core idea of the author is to combine high throughput of a highway net with performance gains using better activations (Leaky ReLU) and batch normalization. This architecture is used as a backbone for YOLOv2.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/darknet19").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/RedmonF16,
author = {Joseph Redmon and
Ali Farhadi},
title = {{YOLO9000:} Better, Faster, Stronger},
journal = {CoRR},
volume = {abs/1612.08242},
year = {2016},
url = {http://arxiv.org/abs/1612.08242},
eprinttype = {arXiv},
eprint = {1612.08242},
timestamp = {Mon, 13 Aug 2018 16:48:25 +0200},
biburl = {https://dblp.org/rec/journals/corr/RedmonF16.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/resnet34
|
frgfm
| 2022-07-20T00:57:04Z | 47 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# ResNet-34 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ResNet architecture was introduced in [this paper](https://arxiv.org/pdf/1512.03385.pdf).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/resnet34").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
eprinttype = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/repvgg_a2
|
frgfm
| 2022-07-20T00:56:20Z | 34 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# RepVGG-A2 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training architecture can be reparametrized into a simple sequence of convolutions and non-linear activations.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/repvgg_a2").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-2101-03697,
author = {Xiaohan Ding and
Xiangyu Zhang and
Ningning Ma and
Jungong Han and
Guiguang Ding and
Jian Sun},
title = {RepVGG: Making VGG-style ConvNets Great Again},
journal = {CoRR},
volume = {abs/2101.03697},
year = {2021},
url = {https://arxiv.org/abs/2101.03697},
eprinttype = {arXiv},
eprint = {2101.03697},
timestamp = {Tue, 09 Feb 2021 15:29:34 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-03697.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/repvgg_a1
|
frgfm
| 2022-07-20T00:56:06Z | 35 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2101.03697",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# RepVGG-A1 model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The RepVGG architecture was introduced in [this paper](https://arxiv.org/pdf/2101.03697.pdf).
## Model description
The core idea of the author is to distinguish the training architecture (with shortcut connections), from the inference one (a pure highway network). By designing the residual block, the training architecture can be reparametrized into a simple sequence of convolutions and non-linear activations.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/repvgg_a1").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-2101-03697,
author = {Xiaohan Ding and
Xiangyu Zhang and
Ningning Ma and
Jungong Han and
Guiguang Ding and
Jian Sun},
title = {RepVGG: Making VGG-style ConvNets Great Again},
journal = {CoRR},
volume = {abs/2101.03697},
year = {2021},
url = {https://arxiv.org/abs/2101.03697},
eprinttype = {arXiv},
eprint = {2101.03697},
timestamp = {Tue, 09 Feb 2021 15:29:34 +0100},
biburl = {https://dblp.org/rec/journals/corr/abs-2101-03697.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
frgfm/rexnet1_5x
|
frgfm
| 2022-07-20T00:54:55Z | 63 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:frgfm/imagenette",
"arxiv:2007.00992",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-03-02T23:29:05Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- frgfm/imagenette
---
# ReXNet-1.5x model
Pretrained on [ImageNette](https://github.com/fastai/imagenette). The ReXNet architecture was introduced in [this paper](https://arxiv.org/pdf/2007.00992.pdf).
## Model description
The core idea of the author is to add a customized Squeeze-Excitation layer in the residual blocks that will prevent channel redundancy.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install Holocron.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pylocron/) as follows:
```shell
pip install pylocron
```
or using [conda](https://anaconda.org/frgfm/pylocron):
```shell
conda install -c frgfm pylocron
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/frgfm/Holocron.git
pip install -e Holocron/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from holocron.models import model_from_hf_hub
model = model_from_hf_hub("frgfm/rexnet1_5x").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-2007-00992,
author = {Dongyoon Han and
Sangdoo Yun and
Byeongho Heo and
Young Joon Yoo},
title = {ReXNet: Diminishing Representational Bottleneck on Convolutional Neural
Network},
journal = {CoRR},
volume = {abs/2007.00992},
year = {2020},
url = {https://arxiv.org/abs/2007.00992},
eprinttype = {arXiv},
eprint = {2007.00992},
timestamp = {Mon, 06 Jul 2020 15:26:01 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-2007-00992.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{Fernandez_Holocron_2020,
author = {Fernandez, François-Guillaume},
month = {5},
title = {{Holocron}},
url = {https://github.com/frgfm/Holocron},
year = {2020}
}
```
|
jonaskoenig/topic_classification_03
|
jonaskoenig
| 2022-07-19T20:57:39Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T19:33:22Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: topic_classification_03
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. -->
# topic_classification_03
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.0459
- Train Sparse Categorical Accuracy: 0.6535
- Validation Loss: 1.1181
- Validation Sparse Categorical Accuracy: 0.6354
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 5e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Sparse Categorical Accuracy | Validation Loss | Validation Sparse Categorical Accuracy | Epoch |
|:----------:|:---------------------------------:|:---------------:|:--------------------------------------:|:-----:|
| 1.2710 | 0.5838 | 1.1683 | 0.6156 | 0 |
| 1.1546 | 0.6193 | 1.1376 | 0.6259 | 1 |
| 1.1163 | 0.6314 | 1.1247 | 0.6292 | 2 |
| 1.0888 | 0.6400 | 1.1253 | 0.6323 | 3 |
| 1.0662 | 0.6473 | 1.1182 | 0.6344 | 4 |
| 1.0459 | 0.6535 | 1.1181 | 0.6354 | 5 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
t-bank-ai/ruDialoGPT-small
|
t-bank-ai
| 2022-07-19T20:27:35Z | 1,187 | 5 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"conversational",
"text-generation",
"ru",
"arxiv:2001.09977",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-12T14:24:39Z |
---
license: mit
pipeline_tag: text-generation
widget:
- text: "@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@"
example_title: "how r u"
- text: "@@ПЕРВЫЙ@@ что ты делал на выходных? @@ВТОРОЙ@@"
example_title: "wyd"
language:
- ru
tags:
- conversational
---
This generation model is based on [sberbank-ai/rugpt3small_based_on_gpt2](https://huggingface.co/sberbank-ai/rugpt3small_based_on_gpt2). It's trained on large corpus of dialog data and can be used for buildning generative conversational agents
The model was trained with context size 3
On a private validation set we calculated metrics introduced in [this paper](https://arxiv.org/pdf/2001.09977.pdf):
- Sensibleness: Crowdsourcers were asked whether model's response makes sense given the context
- Specificity: Crowdsourcers were asked whether model's response is specific for given context, in other words we don't want our model to give general and boring responses
- SSA which is the average of two metrics above (Sensibleness Specificity Average)
| | sensibleness | specificity | SSA |
|:----------------------------------------------------|---------------:|--------------:|------:|
| [tinkoff-ai/ruDialoGPT-small](https://huggingface.co/tinkoff-ai/ruDialoGPT-small) | 0.64 | 0.5 | 0.57 |
| [tinkoff-ai/ruDialoGPT-medium](https://huggingface.co/tinkoff-ai/ruDialoGPT-medium) | 0.78 | 0.69 | 0.735 |
How to use:
```python
import torch
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('tinkoff-ai/ruDialoGPT-small')
model = AutoModelWithLMHead.from_pretrained('tinkoff-ai/ruDialoGPT-small')
inputs = tokenizer('@@ПЕРВЫЙ@@ привет @@ВТОРОЙ@@ привет @@ПЕРВЫЙ@@ как дела? @@ВТОРОЙ@@', return_tensors='pt')
generated_token_ids = model.generate(
**inputs,
top_k=10,
top_p=0.95,
num_beams=3,
num_return_sequences=3,
do_sample=True,
no_repeat_ngram_size=2,
temperature=1.2,
repetition_penalty=1.2,
length_penalty=1.0,
eos_token_id=50257,
max_new_tokens=40
)
context_with_response = [tokenizer.decode(sample_token_ids) for sample_token_ids in generated_token_ids]
context_with_response
```
|
huggingtweets/angelinacho-stillconor-touchofray
|
huggingtweets
| 2022-07-19T19:52:38Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-05-02T19:59:55Z |
---
language: en
thumbnail: http://www.huggingtweets.com/angelinacho-stillconor-touchofray/1658260354212/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/859423506592808961/VurGQ0Hk_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1485398297984389121/DmUfFheN_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1375088662589939717/nd6wgtKM_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">✨ nacho // 조혜미 ✨ & conor & ray</div>
<div style="text-align: center; font-size: 14px;">@angelinacho-stillconor-touchofray</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 ✨ nacho // 조혜미 ✨ & conor & ray.
| Data | ✨ nacho // 조혜미 ✨ | conor | ray |
| --- | --- | --- | --- |
| Tweets downloaded | 3210 | 3250 | 3208 |
| Retweets | 575 | 100 | 1737 |
| Short tweets | 307 | 443 | 246 |
| Tweets kept | 2328 | 2707 | 1225 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3q995qld/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 @angelinacho-stillconor-touchofray's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/37ez663h) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/37ez663h/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/angelinacho-stillconor-touchofray')
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)
|
jonaskoenig/topic_classification_02
|
jonaskoenig
| 2022-07-19T19:24:21Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T14:37:24Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: jonaskoenig/topic_classification_02
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. -->
# jonaskoenig/topic_classification_02
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0189
- Train Binary Crossentropy: 0.3299
- Epoch: 5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Binary Crossentropy | Epoch |
|:----------:|:-------------------------:|:-----:|
| 0.0250 | 0.4229 | 0 |
| 0.0214 | 0.3684 | 1 |
| 0.0204 | 0.3530 | 2 |
| 0.0198 | 0.3433 | 3 |
| 0.0193 | 0.3359 | 4 |
| 0.0189 | 0.3299 | 5 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
rapid3/gpt2-wikitext2
|
rapid3
| 2022-07-19T19:15:42Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-19T18:29:03Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: gpt2-wikitext2
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. -->
# gpt2-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.1100
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5578 | 1.0 | 2249 | 6.4697 |
| 6.1907 | 2.0 | 4498 | 6.1998 |
| 6.0152 | 3.0 | 6747 | 6.1100 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
kamangir/image-classifier
|
kamangir
| 2022-07-19T18:45:03Z | 0 | 0 |
tf-keras
|
[
"tf-keras",
"license:cc",
"region:us"
] | null | 2022-07-12T19:36:45Z |
---
license: cc
---
# Image Classifier
`image-classifier` is an extendable TensorFlow image classifier w/ a Bash cli and Hugging Face integration - to see the list of `image-classifier` commands complete [installation](#Installation) and type in:
```
image_classifier ?
```
## Installation
To install `image-classifier` first [install and configure awesome-bash-cli](https://github.com/kamangir/awesome-bash-cli) then run:
```
abcli huggingface clone image-classifier
```
To see the list of `image-classifier` saved models type in
```
image_classifier list
```
You should see the following items:
1. [fashion-mnist](#fashion-mnist)
1. intel-image-classifier 🚧
1. vegetable-classifier 🚧
## fashion-mnist
`fashion-mnist` is an `image-classifier` trained on [Fashion-MNIST](https://github.com/zalandoresearch/fashion-mnist).
To retrain `fashion-mnist` type in:
```
abcli select
fashion_mnist train
abcli upload
image_classifier list . browser=1,model=object
```
You should now see the structure of the network (left) and the [content of the model](https://github.com/kamangir/browser) (right).
|  |  |
|---|---|
You can save this model under a new name by typing in:
```
fashion_mnist save new_name_1
```
/ END
|
Evelyn18/roberta-base-spanish-squades-modelo-robertav1
|
Evelyn18
| 2022-07-19T18:29:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:becasv2",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-18T18:53:50Z |
---
tags:
- generated_from_trainer
datasets:
- becasv2
model-index:
- name: roberta-base-spanish-squades-modelo-robertav1
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. -->
# roberta-base-spanish-squades-modelo-robertav1
This model is a fine-tuned version of [IIC/roberta-base-spanish-squades](https://huggingface.co/IIC/roberta-base-spanish-squades) on the becasv2 dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4358
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 11
- eval_batch_size: 11
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 6 | 1.8825 |
| No log | 2.0 | 12 | 1.7787 |
| No log | 3.0 | 18 | 2.0521 |
| No log | 4.0 | 24 | 2.2991 |
| No log | 5.0 | 30 | 2.4029 |
| No log | 6.0 | 36 | 2.4358 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
himal/swin-tiny-patch4-window7-224-finetuned-eurosat
|
himal
| 2022-07-19T17:44:49Z | 71 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-19T17:17:24Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9755555555555555
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0738
- Accuracy: 0.9756
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2469 | 1.0 | 190 | 0.1173 | 0.9622 |
| 0.1471 | 2.0 | 380 | 0.0806 | 0.9748 |
| 0.1588 | 3.0 | 570 | 0.0738 | 0.9756 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
sam34738/xlm-kabita
|
sam34738
| 2022-07-19T17:36:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T17:15:52Z |
---
tags:
- generated_from_trainer
model-index:
- name: xlm-kabita
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-kabita
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-emotion](https://huggingface.co/cardiffnlp/twitter-roberta-base-emotion) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0929 | 1.0 | 460 | 0.5814 |
| 0.4287 | 2.0 | 920 | 0.4984 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
bigmorning/oscarth_54321
|
bigmorning
| 2022-07-19T16:15:29Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-19T15:49:28Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: oscarth_54321
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. -->
# oscarth_54321
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 4.5784
- Validation Loss: 4.5266
- Epoch: 1
## 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 |
|:----------:|:---------------:|:-----:|
| 4.6206 | 4.5583 | 0 |
| 4.5784 | 4.5266 | 1 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jordyvl/bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
|
jordyvl
| 2022-07-19T15:32:43Z | 1 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2022-07-19T15:10:34Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-portuguese-cased_harem-selective-lowC-CRF-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0687
- Precision: 0.8030
- Recall: 0.8933
- F1: 0.8457
- Accuracy: 0.9748
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0646 | 1.0 | 2517 | 0.0924 | 0.7822 | 0.8876 | 0.8316 | 0.9670 |
| 0.0263 | 2.0 | 5034 | 0.0644 | 0.7598 | 0.8708 | 0.8115 | 0.9685 |
| 0.0234 | 3.0 | 7551 | 0.0687 | 0.8030 | 0.8933 | 0.8457 | 0.9748 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
scottykwok/wav2vec2-large-xlsr-cantonese
|
scottykwok
| 2022-07-19T15:22:01Z | 43 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"zh",
"dataset:common_voice",
"license:cc-by-sa-4.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:05Z |
---
language: zh
tags:
- automatic-speech-recognition
license: cc-by-sa-4.0
datasets:
- common_voice
metrics:
- cer
---
# Wav2vec2-large-xlsr-cantonese
This model was based on [wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53), finetuned using Common Voice/zh-HK/6.1.0.
The training code is similar to [user ctl](https://huggingface.co/ctl/wav2vec2-large-xlsr-cantonese), except that the number of training epochs was 80 (doubled) and fp16_backend is apex. The model was trained using a single RTX 3090 and docker image is nvidia/cuda:11.1-cudnn8-devel.
CER is 15.11% when evaluate against common voice zh-HK test set.
# Result (CER)
15.11%
# Source Code
See this GitHub Repo [cantonese-selfish-project](https://github.com/scottykwok/cantonese-selfish-project/) and [demo video](https://youtu.be/k_9RQ-ilGEc).
# Usage
```python
import soundfile as sf
import torch
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
# load pretrained model
processor = Wav2Vec2Processor.from_pretrained("scottykwok/wav2vec2-large-xlsr-cantonese")
model = Wav2Vec2ForCTC.from_pretrained("scottykwok/wav2vec2-large-xlsr-cantonese")
# load audio - must be 16kHz mono
audio_input, sample_rate = sf.read('audio.wav')
# pad input values and return pt tensor
input_values = processor(audio_input, sampling_rate=sample_rate, return_tensors="pt").input_values
# INFERENCE
# retrieve logits & take argmax
logits = model(input_values).logits
predicted_ids = torch.argmax(logits, dim=-1)
# transcribe
transcription = processor.decode(predicted_ids[0])
print("-" *20)
print("Transcription:\n", transcription.lower())
print("-" *20)
```
|
jordyvl/bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
|
jordyvl
| 2022-07-19T15:08:00Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-19T14:51:42Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-selective-lowC-sm-first-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: harem
type: harem
args: selective
metrics:
- name: Precision
type: precision
value: 0.8
- name: Recall
type: recall
value: 0.8764044943820225
- name: F1
type: f1
value: 0.8364611260053619
- name: Accuracy
type: accuracy
value: 0.9764089121887287
---
<!-- 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-portuguese-cased_harem-selective-lowC-sm-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1160
- Precision: 0.8
- Recall: 0.8764
- F1: 0.8365
- Accuracy: 0.9764
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.055 | 1.0 | 2517 | 0.0934 | 0.81 | 0.9101 | 0.8571 | 0.9699 |
| 0.0236 | 2.0 | 5034 | 0.0883 | 0.8307 | 0.8820 | 0.8556 | 0.9751 |
| 0.0129 | 3.0 | 7551 | 0.1160 | 0.8 | 0.8764 | 0.8365 | 0.9764 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Rocketknight1/gpt2-finetuned-wikitext2
|
Rocketknight1
| 2022-07-19T14:02:31Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"gpt2",
"text-generation",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:04Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: Rocketknight1/gpt2-finetuned-wikitext2
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. -->
# Rocketknight1/gpt2-finetuned-wikitext2
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 7.3062
- Validation Loss: 6.7676
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 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 |
|:----------:|:---------------:|:-----:|
| 7.3062 | 6.7676 | 0 |
### Framework versions
- Transformers 4.21.0.dev0
- TensorFlow 2.9.1
- Datasets 2.3.3.dev0
- Tokenizers 0.11.0
|
Eleven/bart-large-mnli-finetuned-emotion
|
Eleven
| 2022-07-19T13:17:53Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T19:19:13Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: bart-large-mnli-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-large-mnli-finetuned-emotion
This model is a fine-tuned version of [facebook/bart-large-mnli](https://huggingface.co/facebook/bart-large-mnli) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 6
- eval_batch_size: 6
- 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 1.12.0+cu113
- Tokenizers 0.12.1
|
saadob12/t5_C2T_autochart
|
saadob12
| 2022-07-19T13:03:11Z | 18 | 3 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"arxiv:2108.06897",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-08T15:50:39Z |
# Training Data
**Autochart:** Zhu, J., Ran, J., Lee, R. K. W., Choo, K., & Li, Z. (2021). AutoChart: A Dataset for Chart-to-Text Generation Task. arXiv preprint arXiv:2108.06897.
**Gitlab Link for the data**: https://gitlab.com/bottle_shop/snlg/chart/autochart
Train split for this model: Train 8000, Validation 1297, Test 1296
# Example use:
Append ```C2T: ``` before every input to the model
```
tokenizer = AutoTokenizer.from_pretrained(saadob12/t5_C2T_autochart)
model = AutoModelForSeq2SeqLM.from_pretrained(saadob12/t5_C2T_autochart)
data = 'Trade statistics of Qatar with developing economies in North Africa bar_chart Year-Trade with economies of Middle East & North Africa(%)(Merchandise exports,Merchandise imports) x-y1-y2 values 2000 0.591869968616745 3.59339030672154 , 2001 0.53415012207203 3.25371165779341 , 2002 3.07769793440318 1.672796364224 , 2003 0.6932513078579471 1.62522475477827 , 2004 1.17635914189321 1.80540331396412'
prefix = 'C2T: '
tokens = tokenizer.encode(prefix + data, truncation=True, padding='max_length', return_tensors='pt')
generated = model.generate(tokens, num_beams=4, max_length=256)
tgt_text = tokenizer.decode(generated[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)
summary = str(tgt_text).strip('[]""')
#Summary: This barchart shows the number of trade statistics of qatar with developing economies in north africa from 2000 through 2004. The unit of measurement in this graph is Trade with economies of Middle East & North Africa(%) as shown on the y-axis. The first group data denotes the change of Merchandise exports. There is a go up and down trend of the number. The peak of the number is found in 2002 and the lowest number is found in 2001. The changes in the number may be related to the conuntry's national policies. The second group data denotes the change of Merchandise imports. There is a go up and down trend of the number. The number in 2000 being the peak, and the lowest number is found in 2003. The changes in the number may be related to the conuntry's national policies.
```
# Limitations
You can use the model to generate summaries of data files.
Works well for general statistics like the following:
| Year | Children born per woman |
|:---:|:---:|
| 2018 | 1.14 |
| 2017 | 1.45 |
| 2016 | 1.49 |
| 2015 | 1.54 |
| 2014 | 1.6 |
| 2013 | 1.65 |
May or may not generate an **okay** summary at best for the following kind of data:
| Model | BLEU score | BLEURT|
|:---:|:---:|:---:|
| t5-small | 25.4 | -0.11 |
| t5-base | 28.2 | 0.12 |
| t5-large | 35.4 | 0.34 |
# Citation
Kindly cite my work. Thank you.
```
@misc{obaid ul islam_2022,
title={saadob12/t5_C2T_autochart Hugging Face},
url={https://huggingface.co/saadob12/t5_C2T_autochart},
journal={Huggingface.co},
author={Obaid ul Islam, Saad},
year={2022}
}
```
|
kabelomalapane/En-Nso_update
|
kabelomalapane
| 2022-07-19T12:44:05Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-19T12:12:14Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: En-Nso_update
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. -->
# En-Nso_update
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-nso](https://huggingface.co/Helsinki-NLP/opus-mt-en-nso) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.8782
- Bleu: 31.2967
## 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: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 4 | 7.2950 | 0.0088 |
| No log | 2.0 | 8 | 5.9614 | 0.6848 |
| No log | 3.0 | 12 | 5.0695 | 4.9050 |
| No log | 4.0 | 16 | 4.5523 | 9.1757 |
| No log | 5.0 | 20 | 4.2355 | 10.4744 |
| No log | 6.0 | 24 | 4.0106 | 14.6163 |
| No log | 7.0 | 28 | 3.8427 | 15.8379 |
| No log | 8.0 | 32 | 3.7264 | 15.6158 |
| No log | 9.0 | 36 | 3.6338 | 16.3562 |
| No log | 10.0 | 40 | 3.5555 | 21.1011 |
| No log | 11.0 | 44 | 3.4839 | 21.5754 |
| No log | 12.0 | 48 | 3.4180 | 22.7155 |
| No log | 13.0 | 52 | 3.3620 | 23.1592 |
| No log | 14.0 | 56 | 3.3115 | 24.3886 |
| No log | 15.0 | 60 | 3.2676 | 24.1278 |
| No log | 16.0 | 64 | 3.2285 | 24.2245 |
| No log | 17.0 | 68 | 3.1974 | 23.9716 |
| No log | 18.0 | 72 | 3.1695 | 24.2395 |
| No log | 19.0 | 76 | 3.1441 | 23.3442 |
| No log | 20.0 | 80 | 3.1235 | 21.3332 |
| No log | 21.0 | 84 | 3.1029 | 21.8410 |
| No log | 22.0 | 88 | 3.0849 | 22.4065 |
| No log | 23.0 | 92 | 3.0666 | 22.3016 |
| No log | 24.0 | 96 | 3.0534 | 22.9616 |
| No log | 25.0 | 100 | 3.0423 | 23.3971 |
| No log | 26.0 | 104 | 3.0306 | 23.5443 |
| No log | 27.0 | 108 | 3.0183 | 23.3348 |
| No log | 28.0 | 112 | 3.0051 | 23.4077 |
| No log | 29.0 | 116 | 2.9947 | 24.1791 |
| No log | 30.0 | 120 | 2.9855 | 24.1265 |
| No log | 31.0 | 124 | 2.9777 | 23.9860 |
| No log | 32.0 | 128 | 2.9691 | 24.7301 |
| No log | 33.0 | 132 | 2.9597 | 25.1896 |
| No log | 34.0 | 136 | 2.9521 | 24.5893 |
| No log | 35.0 | 140 | 2.9457 | 24.5229 |
| No log | 36.0 | 144 | 2.9409 | 24.6232 |
| No log | 37.0 | 148 | 2.9354 | 24.2830 |
| No log | 38.0 | 152 | 2.9322 | 26.1404 |
| No log | 39.0 | 156 | 2.9306 | 25.9425 |
| No log | 40.0 | 160 | 2.9288 | 30.5432 |
| No log | 41.0 | 164 | 2.9261 | 29.4635 |
| No log | 42.0 | 168 | 2.9215 | 28.4787 |
| No log | 43.0 | 172 | 2.9182 | 28.9082 |
| No log | 44.0 | 176 | 2.9151 | 29.3171 |
| No log | 45.0 | 180 | 2.9132 | 28.3602 |
| No log | 46.0 | 184 | 2.9126 | 28.9583 |
| No log | 47.0 | 188 | 2.9104 | 26.0269 |
| No log | 48.0 | 192 | 2.9086 | 29.6904 |
| No log | 49.0 | 196 | 2.9052 | 29.2881 |
| No log | 50.0 | 200 | 2.9020 | 29.6063 |
| No log | 51.0 | 204 | 2.8994 | 29.5224 |
| No log | 52.0 | 208 | 2.8960 | 29.3913 |
| No log | 53.0 | 212 | 2.8930 | 30.5451 |
| No log | 54.0 | 216 | 2.8889 | 32.1862 |
| No log | 55.0 | 220 | 2.8869 | 31.9423 |
| No log | 56.0 | 224 | 2.8859 | 30.7244 |
| No log | 57.0 | 228 | 2.8846 | 30.8172 |
| No log | 58.0 | 232 | 2.8837 | 30.5376 |
| No log | 59.0 | 236 | 2.8826 | 31.1454 |
| No log | 60.0 | 240 | 2.8813 | 30.9049 |
| No log | 61.0 | 244 | 2.8802 | 30.6363 |
| No log | 62.0 | 248 | 2.8802 | 31.3739 |
| No log | 63.0 | 252 | 2.8799 | 30.9776 |
| No log | 64.0 | 256 | 2.8793 | 29.8283 |
| No log | 65.0 | 260 | 2.8795 | 29.6912 |
| No log | 66.0 | 264 | 2.8804 | 29.7654 |
| No log | 67.0 | 268 | 2.8810 | 29.1586 |
| No log | 68.0 | 272 | 2.8822 | 28.8888 |
| No log | 69.0 | 276 | 2.8819 | 29.7222 |
| No log | 70.0 | 280 | 2.8810 | 29.9932 |
| No log | 71.0 | 284 | 2.8811 | 30.2492 |
| No log | 72.0 | 288 | 2.8802 | 29.9644 |
| No log | 73.0 | 292 | 2.8791 | 30.3378 |
| No log | 74.0 | 296 | 2.8790 | 29.8055 |
| No log | 75.0 | 300 | 2.8794 | 29.0100 |
| No log | 76.0 | 304 | 2.8795 | 30.7968 |
| No log | 77.0 | 308 | 2.8790 | 31.5414 |
| No log | 78.0 | 312 | 2.8783 | 31.5060 |
| No log | 79.0 | 316 | 2.8775 | 31.4376 |
| No log | 80.0 | 320 | 2.8766 | 31.6005 |
| No log | 81.0 | 324 | 2.8767 | 31.3697 |
| No log | 82.0 | 328 | 2.8769 | 31.6108 |
| No log | 83.0 | 332 | 2.8770 | 31.4214 |
| No log | 84.0 | 336 | 2.8772 | 31.6039 |
| No log | 85.0 | 340 | 2.8776 | 32.0254 |
| No log | 86.0 | 344 | 2.8779 | 31.4024 |
| No log | 87.0 | 348 | 2.8783 | 32.0279 |
| No log | 88.0 | 352 | 2.8786 | 31.8914 |
| No log | 89.0 | 356 | 2.8788 | 31.6500 |
| No log | 90.0 | 360 | 2.8791 | 31.7698 |
| No log | 91.0 | 364 | 2.8793 | 31.6137 |
| No log | 92.0 | 368 | 2.8793 | 31.8244 |
| No log | 93.0 | 372 | 2.8790 | 31.5626 |
| No log | 94.0 | 376 | 2.8786 | 31.3743 |
| No log | 95.0 | 380 | 2.8785 | 31.4160 |
| No log | 96.0 | 384 | 2.8784 | 31.6682 |
| No log | 97.0 | 388 | 2.8782 | 31.8335 |
| No log | 98.0 | 392 | 2.8782 | 31.7143 |
| No log | 99.0 | 396 | 2.8782 | 31.7143 |
| No log | 100.0 | 400 | 2.8782 | 31.7143 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
spacestar1705/testpyramidsrnd
|
spacestar1705
| 2022-07-19T12:20:07Z | 4 | 0 |
ml-agents
|
[
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] |
reinforcement-learning
| 2022-07-19T12:20:02Z |
---
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: spacestar1705/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
luomingshuang/icefall_asr_aidatatang-200zh_pruned_transducer_stateless2
|
luomingshuang
| 2022-07-19T11:56:33Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-05-16T08:24:41Z |
Note: This recipe is trained with the codes from this PR https://github.com/k2-fsa/icefall/pull/355
And the SpecAugment codes from this PR https://github.com/lhotse-speech/lhotse/pull/604.
# Pre-trained Transducer-Stateless2 models for the Aidatatang_200zh dataset with icefall.
The model was trained on full [Aidatatang_200zh](https://www.openslr.org/62) with the scripts in [icefall](https://github.com/k2-fsa/icefall) based on the latest version k2.
## Training procedure
The main repositories are list below, we will update the training and decoding scripts with the update of version.
k2: https://github.com/k2-fsa/k2
icefall: https://github.com/k2-fsa/icefall
lhotse: https://github.com/lhotse-speech/lhotse
* Install k2 and lhotse, k2 installation guide refers to https://k2.readthedocs.io/en/latest/installation/index.html, lhotse refers to https://lhotse.readthedocs.io/en/latest/getting-started.html#installation. I think the latest version would be ok. And please also install the requirements listed in icefall.
* Clone icefall(https://github.com/k2-fsa/icefall) and check to the commit showed above.
```
git clone https://github.com/k2-fsa/icefall
cd icefall
```
* Preparing data.
```
cd egs/aidatatang_200zh/ASR
bash ./prepare.sh
```
* Training
```
export CUDA_VISIBLE_DEVICES="0,1"
./pruned_transducer_stateless2/train.py \
--world-size 2 \
--num-epochs 30 \
--start-epoch 0 \
--exp-dir pruned_transducer_stateless2/exp \
--lang-dir data/lang_char \
--max-duration 250
```
## Evaluation results
The decoding results (WER%) on Aidatatang_200zh(dev and test) are listed below, we got this result by averaging models from epoch 11 to 29.
The WERs are
| | dev | test | comment |
|------------------------------------|------------|------------|------------------------------------------|
| greedy search | 5.53 | 6.59 | --epoch 29, --avg 19, --max-duration 100 |
| modified beam search (beam size 4) | 5.28 | 6.32 | --epoch 29, --avg 19, --max-duration 100 |
| fast beam search (set as default) | 5.29 | 6.33 | --epoch 29, --avg 19, --max-duration 1500|
|
kabelomalapane/Nso-En_update
|
kabelomalapane
| 2022-07-19T11:40:40Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-19T11:31:18Z |
---
license: apache-2.0
tags:
- translation
- generated_from_trainer
metrics:
- bleu
model-index:
- name: Nso-En_update
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. -->
# Nso-En_update
This model is a fine-tuned version of [kabelomalapane/En-Nso](https://huggingface.co/kabelomalapane/En-Nso) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9219
- Bleu: 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: 32
- eval_batch_size: 64
- 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 | Bleu |
|:-------------:|:-----:|:----:|:---------------:|:----:|
| No log | 1.0 | 108 | 2.0785 | 0.0 |
| No log | 2.0 | 216 | 1.9015 | 0.0 |
| No log | 3.0 | 324 | 1.8730 | 0.0 |
| No log | 4.0 | 432 | 1.8626 | 0.0 |
| 2.1461 | 5.0 | 540 | 1.8743 | 0.0 |
| 2.1461 | 6.0 | 648 | 1.8903 | 0.0 |
| 2.1461 | 7.0 | 756 | 1.9018 | 0.0 |
| 2.1461 | 8.0 | 864 | 1.9236 | 0.0 |
| 2.1461 | 9.0 | 972 | 1.9210 | 0.0 |
| 1.2781 | 10.0 | 1080 | 1.9219 | 0.0 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
robingeibel/reformer-finetuned-big_patent-wikipedia-arxiv-16384
|
robingeibel
| 2022-07-19T10:13:35Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"reformer",
"fill-mask",
"generated_from_trainer",
"dataset:wikipedia",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-14T15:11:09Z |
---
tags:
- generated_from_trainer
datasets:
- wikipedia
model-index:
- name: reformer-finetuned-big_patent-wikipedia-arxiv-16384
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. -->
# reformer-finetuned-big_patent-wikipedia-arxiv-16384
This model is a fine-tuned version of [robingeibel/reformer-finetuned-big_patent-wikipedia-arxiv-16384](https://huggingface.co/robingeibel/reformer-finetuned-big_patent-wikipedia-arxiv-16384) on the wikipedia dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5256
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 8.0368 | 1.0 | 3785 | 6.7392 |
| 6.7992 | 2.0 | 7570 | 6.5576 |
| 6.6926 | 3.0 | 11355 | 6.5256 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Malanga/finetuning-sentiment-model-3000-samples
|
Malanga
| 2022-07-19T09:49:05Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T09:30:43Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.87
- name: F1
type: f1
value: 0.8712871287128714
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3104
- Accuracy: 0.87
- F1: 0.8713
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Tomas23/twitter-roberta-base-mar2022-finetuned-emotion
|
Tomas23
| 2022-07-19T09:48:08Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:tweet_eval",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T08:54:06Z |
---
tags:
- generated_from_trainer
datasets:
- tweet_eval
metrics:
- accuracy
- f1
model-index:
- name: twitter-roberta-base-mar2022-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: tweet_eval
type: tweet_eval
args: emotion
metrics:
- name: Accuracy
type: accuracy
value: 0.8191414496833216
- name: F1
type: f1
value: 0.8170974933422602
---
<!-- 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-mar2022-finetuned-emotion
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-mar2022](https://huggingface.co/cardiffnlp/twitter-roberta-base-mar2022) on the tweet_eval dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5146
- Accuracy: 0.8191
- F1: 0.8171
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8945 | 1.0 | 102 | 0.5831 | 0.7995 | 0.7887 |
| 0.5176 | 2.0 | 204 | 0.5266 | 0.8235 | 0.8200 |
### Framework versions
- Transformers 4.19.3
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
sketchai/sketch-gnn
|
sketchai
| 2022-07-19T09:46:36Z | 0 | 0 | null |
[
"tensorboard",
"license:lgpl-3.0",
"region:us"
] | null | 2022-06-17T08:30:03Z |
---
license: lgpl-3.0
---
- `v00_5pc_given` refers to a model trained with 0 to 10% given constraints
- `v00_80pc_given` refers to a model trained with 70 to 90% given constraints
|
spacestar1705/dqn-SpaceInvadersNoFrameskip-v4
|
spacestar1705
| 2022-07-19T09:41:56Z | 2 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-19T09:41:17Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 508.50 +/- 105.36
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 spacestar1705 -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 spacestar1705
```
## 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', 10000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 100),
('train_freq', 4),
('normalize', False)])
```
|
AliMMZ/q-Taxi-v3
|
AliMMZ
| 2022-07-19T08:20:57Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-19T08:00:13Z |
---
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="AliMMZ/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"])
```
|
hirohiroz/wav2vec2-base-timit-demo-google-colab-tryjpn
|
hirohiroz
| 2022-07-19T08:16:37Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-14T03:11:46Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-timit-demo-google-colab-tryjpn
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-timit-demo-google-colab-tryjpn
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.1527
- Wer: 1.0
## 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: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---:|
| 48.3474 | 6.67 | 100 | 68.0887 | 1.0 |
| 7.601 | 13.33 | 200 | 8.3667 | 1.0 |
| 4.9107 | 20.0 | 300 | 5.6991 | 1.0 |
| 4.379 | 26.67 | 400 | 5.1527 | 1.0 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cu102
- Datasets 1.18.3
- Tokenizers 0.12.1
|
Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-256
|
Kayvane
| 2022-07-19T06:29:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-19T05:06:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-base-uncased-wandb-week-3-complaints-classifier-256
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: consumer-finance-complaints
type: consumer-finance-complaints
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8234544620559604
- name: F1
type: f1
value: 0.8176243580045963
- name: Recall
type: recall
value: 0.8234544620559604
- name: Precision
type: precision
value: 0.8171438106054644
---
<!-- 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-wandb-week-3-complaints-classifier-256
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5453
- Accuracy: 0.8235
- F1: 0.8176
- Recall: 0.8235
- Precision: 0.8171
## 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: 4.097565552226687e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 256
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.6691 | 0.61 | 1500 | 0.6475 | 0.7962 | 0.7818 | 0.7962 | 0.7875 |
| 0.5361 | 1.22 | 3000 | 0.5794 | 0.8161 | 0.8080 | 0.8161 | 0.8112 |
| 0.4659 | 1.83 | 4500 | 0.5453 | 0.8235 | 0.8176 | 0.8235 | 0.8171 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jonaskoenig/topic_classification_01
|
jonaskoenig
| 2022-07-19T06:15:47Z | 5 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T17:58:13Z |
---
license: mit
tags:
- generated_from_keras_callback
model-index:
- name: topic_classification_01
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. -->
# topic_classification_01
This model is a fine-tuned version of [microsoft/xtremedistil-l6-h256-uncased](https://huggingface.co/microsoft/xtremedistil-l6-h256-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0306
- Train Binary Crossentropy: 0.5578
- Epoch: 9
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': 3e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train Binary Crossentropy | Epoch |
|:----------:|:-------------------------:|:-----:|
| 0.0397 | 0.7274 | 0 |
| 0.0352 | 0.6392 | 1 |
| 0.0339 | 0.6142 | 2 |
| 0.0330 | 0.5989 | 3 |
| 0.0324 | 0.5882 | 4 |
| 0.0319 | 0.5799 | 5 |
| 0.0315 | 0.5730 | 6 |
| 0.0312 | 0.5672 | 7 |
| 0.0309 | 0.5623 | 8 |
| 0.0306 | 0.5578 | 9 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.9.1
- Datasets 2.3.2
- Tokenizers 0.12.1
|
dafraile/Clini-dialog-sum-BART
|
dafraile
| 2022-07-19T05:12:30Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-13T03:49:10Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: tst-summarization
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. -->
# tst-summarization
This model is a fine-tuned version of [philschmid/bart-large-cnn-samsum](https://huggingface.co/philschmid/bart-large-cnn-samsum) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9975
- Rouge1: 56.239
- Rouge2: 28.9873
- Rougel: 38.5242
- Rougelsum: 53.7902
- Gen Len: 105.2973
## 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: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0
- Datasets 1.18.4
- Tokenizers 0.11.6
|
gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1
|
gary109
| 2022-07-19T03:23:28Z | 5 | 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
| 2022-07-08T00:35:14Z |
---
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-v1
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-v1
This model is a fine-tuned version of [gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1](https://huggingface.co/gary109/ai-light-dance_singing3_ft_wav2vec2-large-xlsr-53-v1) on the GARY109/AI_LIGHT_DANCE - ONSET-SINGING3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5459
- Wer: 0.2463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 1000
- num_epochs: 50.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:------:|:---------------:|:------:|
| 0.3909 | 1.0 | 2309 | 0.5615 | 0.2459 |
| 0.4094 | 2.0 | 4618 | 0.5654 | 0.2439 |
| 0.326 | 3.0 | 6927 | 0.5568 | 0.2470 |
| 0.4577 | 4.0 | 9236 | 0.5795 | 0.2474 |
| 0.3628 | 5.0 | 11545 | 0.5459 | 0.2463 |
| 0.3135 | 6.0 | 13854 | 0.5582 | 0.2473 |
| 0.5058 | 7.0 | 16163 | 0.5677 | 0.2439 |
| 0.3188 | 8.0 | 18472 | 0.5646 | 0.2445 |
| 0.3589 | 9.0 | 20781 | 0.5626 | 0.2479 |
| 0.4021 | 10.0 | 23090 | 0.5722 | 0.2452 |
| 0.4362 | 11.0 | 25399 | 0.5659 | 0.2431 |
| 0.3215 | 12.0 | 27708 | 0.5658 | 0.2445 |
| 0.3646 | 13.0 | 30017 | 0.5785 | 0.2459 |
| 0.3757 | 14.0 | 32326 | 0.5757 | 0.2418 |
| 0.3311 | 15.0 | 34635 | 0.5672 | 0.2455 |
| 0.3709 | 16.0 | 36944 | 0.5669 | 0.2434 |
| 0.3342 | 17.0 | 39253 | 0.5610 | 0.2455 |
| 0.3236 | 18.0 | 41562 | 0.5652 | 0.2436 |
| 0.3566 | 19.0 | 43871 | 0.5773 | 0.2407 |
| 0.2912 | 20.0 | 46180 | 0.5764 | 0.2453 |
| 0.3652 | 21.0 | 48489 | 0.5732 | 0.2423 |
| 0.3785 | 22.0 | 50798 | 0.5696 | 0.2423 |
| 0.3968 | 23.0 | 53107 | 0.5690 | 0.2429 |
| 0.2968 | 24.0 | 55416 | 0.5800 | 0.2427 |
| 0.428 | 25.0 | 57725 | 0.5704 | 0.2441 |
| 0.383 | 26.0 | 60034 | 0.5739 | 0.2450 |
| 0.3694 | 27.0 | 62343 | 0.5791 | 0.2437 |
| 0.3449 | 28.0 | 64652 | 0.5780 | 0.2451 |
| 0.3008 | 29.0 | 66961 | 0.5749 | 0.2418 |
| 0.3939 | 30.0 | 69270 | 0.5737 | 0.2424 |
| 0.3451 | 31.0 | 71579 | 0.5805 | 0.2402 |
| 0.3513 | 32.0 | 73888 | 0.5670 | 0.2379 |
| 0.3866 | 33.0 | 76197 | 0.5706 | 0.2389 |
| 0.3831 | 34.0 | 78506 | 0.5635 | 0.2401 |
| 0.3641 | 35.0 | 80815 | 0.5708 | 0.2405 |
| 0.3345 | 36.0 | 83124 | 0.5699 | 0.2405 |
| 0.2902 | 37.0 | 85433 | 0.5711 | 0.2373 |
| 0.2868 | 38.0 | 87742 | 0.5713 | 0.2389 |
| 0.3232 | 39.0 | 90051 | 0.5702 | 0.2392 |
| 0.3277 | 40.0 | 92360 | 0.5658 | 0.2393 |
| 0.3234 | 41.0 | 94669 | 0.5732 | 0.2412 |
| 0.3625 | 42.0 | 96978 | 0.5740 | 0.2396 |
| 0.4075 | 43.0 | 99287 | 0.5733 | 0.2389 |
| 0.3473 | 44.0 | 101596 | 0.5735 | 0.2394 |
| 0.3157 | 45.0 | 103905 | 0.5721 | 0.2391 |
| 0.3866 | 46.0 | 106214 | 0.5715 | 0.2381 |
| 0.4062 | 47.0 | 108523 | 0.5711 | 0.2380 |
| 0.3871 | 48.0 | 110832 | 0.5716 | 0.2380 |
| 0.2924 | 49.0 | 113141 | 0.5723 | 0.2374 |
| 0.3655 | 50.0 | 115450 | 0.5709 | 0.2379 |
### Framework versions
- Transformers 4.21.0.dev0
- Pytorch 1.9.1+cu102
- Datasets 2.3.3.dev0
- Tokenizers 0.12.1
|
shivaniNK8/t5-small-finetuned-cnn-news
|
shivaniNK8
| 2022-07-19T02:37:27Z | 30 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"summarization",
"generated_from_trainer",
"dataset:cnn_dailymail",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
summarization
| 2022-07-19T01:48:34Z |
---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
datasets:
- cnn_dailymail
metrics:
- rouge
model-index:
- name: t5-small-finetuned-cnn-news
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: cnn_dailymail
type: cnn_dailymail
args: 3.0.0
metrics:
- name: Rouge1
type: rouge
value: 24.7231
---
<!-- 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-cnn-news
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the cnn_dailymail dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8412
- Rouge1: 24.7231
- Rouge2: 12.292
- Rougel: 20.5347
- Rougelsum: 23.4668
## 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: 0.00056
- 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|
| 2.0318 | 1.0 | 718 | 1.8028 | 24.5415 | 12.0907 | 20.5343 | 23.3386 |
| 1.8307 | 2.0 | 1436 | 1.8028 | 24.0965 | 11.6367 | 20.2078 | 22.8138 |
| 1.6881 | 3.0 | 2154 | 1.8136 | 25.0822 | 12.6509 | 20.9523 | 23.8303 |
| 1.5778 | 4.0 | 2872 | 1.8269 | 24.4271 | 11.8443 | 20.2281 | 23.0941 |
| 1.501 | 5.0 | 3590 | 1.8412 | 24.7231 | 12.292 | 20.5347 | 23.4668 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
KaliYuga/rpgitem
|
KaliYuga
| 2022-07-19T02:33:01Z | 0 | 1 | null |
[
"license:cc-by-4.0",
"region:us"
] | null | 2022-07-12T07:31:28Z |
---
license: cc-by-4.0
---
CURRENTLY UNRELEASED!! FINAL VERSION MY VARY. This model is really only supposed to be for my [patreon patrons](https://www.patreon.com/kaliyuga_ai). I ask that, unless you *truly* can't afford to pay $5 to access this model, you not use it without being a patron. Regardless, you must give attribution if you use this model in any product/app/game, etc
|
huggingtweets/yashar
|
huggingtweets
| 2022-07-19T02:12:11Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-19T01:50:50Z |
---
language: en
thumbnail: http://www.huggingtweets.com/yashar/1658196662556/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/1475314622332764161/tzLI4Zeb_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">Yashar Ali 🐘</div>
<div style="text-align: center; font-size: 14px;">@yashar</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 Yashar Ali 🐘.
| Data | Yashar Ali 🐘 |
| --- | --- |
| Tweets downloaded | 3230 |
| Retweets | 1355 |
| Short tweets | 332 |
| Tweets kept | 1543 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3n7cco99/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 @yashar's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ms5g8tc6) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ms5g8tc6/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/yashar')
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)
|
helpmefindaname/mini-sequence-tagger-conll03
|
helpmefindaname
| 2022-07-19T00:53:03Z | 4 | 0 |
flair
|
[
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"en",
"dataset:conll2003",
"region:us"
] |
token-classification
| 2022-07-14T23:30:10Z |
---
tags:
- flair
- token-classification
- sequence-tagger-model
language: en
datasets:
- conll2003
widget:
- text: "George Washington went to Washington"
---
This is a very small model I use for testing my [ner eval dashboard](https://github.com/helpmefindaname/ner-eval-dashboard)
F1-Score: **48,73** (CoNLL-03)
Predicts 4 tags:
| **tag** | **meaning** |
|---------------------------------|-----------|
| PER | person name |
| LOC | location name |
| ORG | organization name |
| MISC | other name |
Based on huggingface minimal testing embeddings
---
### Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("helpmefindaname/mini-sequence-tagger-conll03")
# make example sentence
sentence = Sentence("George Washington went to Washington")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Span [1,2]: "George Washington" [− Labels: PER (1.0)]
Span [5]: "Washington" [− Labels: LOC (1.0)]
```
So, the entities "*George Washington*" (labeled as a **person**) and "*Washington*" (labeled as a **location**) are found in the sentence "*George Washington went to Washington*".
---
### Training: Script to train this model
The following command was used to train this model:
where `examples\ner\run_ner.py` refers to [this script](https://github.com/flairNLP/flair/blob/master/examples/ner/run_ner.py)
```
python examples\ner\run_ner.py --model_name_or_path hf-internal-testing/tiny-random-bert --dataset_name CONLL_03 --learning_rate 0.002 --mini_batch_chunk_size 1024 --batch_size 64 --num_epochs 100
```
---
|
Kayvane/distilroberta-base-wandb-week-3-complaints-classifier-1024
|
Kayvane
| 2022-07-19T00:52:23Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T17:43:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilroberta-base-wandb-week-3-complaints-classifier-1024
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: consumer-finance-complaints
type: consumer-finance-complaints
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8279904184292339
- name: F1
type: f1
value: 0.8236604095677945
- name: Recall
type: recall
value: 0.8279904184292339
- name: Precision
type: precision
value: 0.8235526237070518
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilroberta-base-wandb-week-3-complaints-classifier-1024
This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5351
- Accuracy: 0.8280
- F1: 0.8237
- Recall: 0.8280
- Precision: 0.8236
## 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: 9.027176214786854e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1024
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 0.7756 | 0.61 | 1500 | 0.7411 | 0.7647 | 0.7375 | 0.7647 | 0.7606 |
| 0.5804 | 1.22 | 3000 | 0.6140 | 0.8088 | 0.8052 | 0.8088 | 0.8077 |
| 0.5008 | 1.83 | 4500 | 0.5351 | 0.8280 | 0.8237 | 0.8280 | 0.8236 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
jordyvl/bert-base-portuguese-cased_harem-selective-sm-first-ner
|
jordyvl
| 2022-07-18T22:12:54Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:harem",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-18T21:25:04Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- harem
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-portuguese-cased_harem-sm-first-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: harem
type: harem
args: selective
metrics:
- name: Precision
type: precision
value: 0.7455830388692579
- name: Recall
type: recall
value: 0.8053435114503816
- name: F1
type: f1
value: 0.7743119266055045
- name: Accuracy
type: accuracy
value: 0.964875491480996
---
<!-- 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-portuguese-cased_harem-sm-first-ner
This model is a fine-tuned version of [neuralmind/bert-base-portuguese-cased](https://huggingface.co/neuralmind/bert-base-portuguese-cased) on the harem dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1952
- Precision: 0.7456
- Recall: 0.8053
- F1: 0.7743
- Accuracy: 0.9649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1049 | 1.0 | 2517 | 0.1955 | 0.6601 | 0.7710 | 0.7113 | 0.9499 |
| 0.0622 | 2.0 | 5034 | 0.2097 | 0.7314 | 0.7901 | 0.7596 | 0.9554 |
| 0.0318 | 3.0 | 7551 | 0.1952 | 0.7456 | 0.8053 | 0.7743 | 0.9649 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-512
|
Kayvane
| 2022-07-18T21:55:04Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T20:33:45Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: distilbert-base-uncased-wandb-week-3-complaints-classifier-512
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: consumer-finance-complaints
type: consumer-finance-complaints
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.6745323887671373
- name: F1
type: f1
value: 0.6355967633316707
- name: Recall
type: recall
value: 0.6745323887671373
- name: Precision
type: precision
value: 0.6122130681567332
---
<!-- 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-wandb-week-3-complaints-classifier-512
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0839
- Accuracy: 0.6745
- F1: 0.6356
- Recall: 0.6745
- Precision: 0.6122
## 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: 0.0007879237562376572
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 512
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Recall | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:---------:|
| 1.2707 | 0.61 | 1500 | 1.3009 | 0.6381 | 0.5848 | 0.6381 | 0.5503 |
| 1.1348 | 1.22 | 3000 | 1.1510 | 0.6610 | 0.6178 | 0.6610 | 0.5909 |
| 1.0649 | 1.83 | 4500 | 1.0839 | 0.6745 | 0.6356 | 0.6745 | 0.6122 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
NimaBoscarino/efficientformer-l3-300
|
NimaBoscarino
| 2022-07-18T20:44:05Z | 2 | 0 |
timm
|
[
"timm",
"coreml",
"onnx",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-07-18T20:31:10Z |
---
language:
- en
license: apache-2.0
library_name: timm
tags:
- mobile
- vison
- image-classification
datasets:
- imagenet-1k
metrics:
- accuracy
---
# EfficientFormer-L3
## Table of Contents
- [EfficientFormer-L3](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Citation Information](#citation-information)
<model_details>
## Model Details
<!-- Give an overview of your model, the relevant research paper, who trained it, etc. -->
EfficientFormer-L3, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
This checkpoint of EfficientFormer-L3 was trained for 300 epochs.
- Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren
- Language(s): English
- License: This model is licensed under the apache-2.0 license
- Resources for more information:
- [Research Paper](https://arxiv.org/abs/2206.01191)
- [GitHub Repo](https://github.com/snap-research/EfficientFormer/)
</model_details>
<how_to_start>
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# A nice code snippet here that describes how to use the model...
```
</how_to_start>
<uses>
## Uses
#### Direct Use
This model can be used for image classification and semantic segmentation. On mobile devices (the model was tested on iPhone 12), the CoreML checkpoints will perform these tasks with low latency.
<Limitations_and_Biases>
## Limitations and Biases
Though most designs in EfficientFormer are general-purposed, e.g., dimension- consistent design and 4D block with CONV-BN fusion, the actual speed of EfficientFormer may vary on other platforms. For instance, if GeLU is not well supported while HardSwish is efficiently implemented on specific hardware and compiler, the operator may need to be modified accordingly. The proposed latency-driven slimming is simple and fast. However, better results may be achieved if search cost is not a concern and an enumeration-based brute search is performed.
Since the model was trained on Imagenet-1K, the [biases embedded in that dataset](https://huggingface.co/datasets/imagenet-1k#considerations-for-using-the-data) will be reflected in the EfficientFormer models.
</Limitations_and_Biases>
<Training>
## Training
#### Training Data
This model was trained on ImageNet-1K.
See the [data card](https://huggingface.co/datasets/imagenet-1k) for additional information.
#### Training Procedure
* Parameters: 31.3 M
* GMACs: 3.9
* Train. Epochs: 300
Trained on a cluster with NVIDIA A100 and V100 GPUs.
</Training>
<Eval_Results>
## Evaluation Results
Top-1 Accuracy: 82.4% on ImageNet 10K
Latency: 3.0 ms
</Eval_Results>
<Cite>
## Citation Information
```bibtex
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
```
</Cite>
|
NimaBoscarino/efficientformer-l1-1000
|
NimaBoscarino
| 2022-07-18T20:14:47Z | 4 | 0 |
timm
|
[
"timm",
"pytorch",
"mobile",
"vison",
"image-classification",
"en",
"dataset:imagenet-1k",
"arxiv:2206.01191",
"license:apache-2.0",
"region:us"
] |
image-classification
| 2022-07-14T08:16:26Z |
---
language:
- en
license: apache-2.0
library_name: timm
tags:
- mobile
- vison
- image-classification
datasets:
- imagenet-1k
metrics:
- accuracy
---
# EfficientFormer-L1
## Table of Contents
- [EfficientFormer-L1](#-model_id--defaultmymodelname-true)
- [Table of Contents](#table-of-contents)
- [Model Details](#model-details)
- [How to Get Started with the Model](#how-to-get-started-with-the-model)
- [Uses](#uses)
- [Direct Use](#direct-use)
- [Downstream Use](#downstream-use)
- [Misuse and Out-of-scope Use](#misuse-and-out-of-scope-use)
- [Limitations and Biases](#limitations-and-biases)
- [Training](#training)
- [Training Data](#training-data)
- [Training Procedure](#training-procedure)
- [Evaluation Results](#evaluation-results)
- [Environmental Impact](#environmental-impact)
- [Citation Information](#citation-information)
<model_details>
## Model Details
<!-- Give an overview of your model, the relevant research paper, who trained it, etc. -->
EfficientFormer-L1, developed by [Snap Research](https://github.com/snap-research), is one of three EfficientFormer models. The EfficientFormer models were released as part of an effort to prove that properly designed transformers can reach extremely low latency on mobile devices while maintaining high performance.
This checkpoint of EfficientFormer-L1 was trained for 1000 epochs.
- Developed by: Yanyu Li, Geng Yuan, Yang Wen, Eric Hu, Georgios Evangelidis, Sergey Tulyakov, Yanzhi Wang, Jian Ren
- Language(s): English
- License: This model is licensed under the apache-2.0 license
- Resources for more information:
- [Research Paper](https://arxiv.org/abs/2206.01191)
- [GitHub Repo](https://github.com/snap-research/EfficientFormer/)
</model_details>
<how_to_start>
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# A nice code snippet here that describes how to use the model...
```
</how_to_start>
<uses>
## Uses
#### Direct Use
This model can be used for image classification and semantic segmentation. On mobile devices (the model was tested on iPhone 12), the CoreML checkpoints will perform these tasks with low latency.
<Limitations_and_Biases>
## Limitations and Biases
Though most designs in EfficientFormer are general-purposed, e.g., dimension- consistent design and 4D block with CONV-BN fusion, the actual speed of EfficientFormer may vary on other platforms. For instance, if GeLU is not well supported while HardSwish is efficiently implemented on specific hardware and compiler, the operator may need to be modified accordingly. The proposed latency-driven slimming is simple and fast. However, better results may be achieved if search cost is not a concern and an enumeration-based brute search is performed.
Since the model was trained on Imagenet-1K, the [biases embedded in that dataset](https://huggingface.co/datasets/imagenet-1k#considerations-for-using-the-data) will be reflected in the EfficientFormer models.
</Limitations_and_Biases>
<Training>
## Training
#### Training Data
This model was trained on ImageNet-1K.
See the [data card](https://huggingface.co/datasets/imagenet-1k) for additional information.
#### Training Procedure
* Parameters: 12.3 M
* GMACs: 1.3
* Train. Epochs: 1000
Trained on a cluster with NVIDIA A100 and V100 GPUs.
</Training>
<Eval_Results>
## Evaluation Results
Top-1 Accuracy: 80.2% on ImageNet 10K
</Eval_Results>
<Cite>
## Citation Information
```bibtex
@article{li2022efficientformer,
title={EfficientFormer: Vision Transformers at MobileNet Speed},
author={Li, Yanyu and Yuan, Geng and Wen, Yang and Hu, Eric and Evangelidis, Georgios and Tulyakov, Sergey and Wang, Yanzhi and Ren, Jian},
journal={arXiv preprint arXiv:2206.01191},
year={2022}
}
```
</Cite>
|
domenicrosati/opus-mt-en-es-scielo
|
domenicrosati
| 2022-07-18T20:09:57Z | 34 | 2 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:scielo",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-07-15T15:25:52Z |
---
tags:
- translation
- generated_from_trainer
datasets:
- scielo
metrics:
- bleu
model-index:
- name: opus-mt-en-es-scielo
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: scielo
type: scielo
args: en-es
metrics:
- name: Bleu
type: bleu
value: 41.53733801247958
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opus-mt-en-es-scielo
This model is a fine-tuned version of [domenicrosati/opus-mt-en-es-scielo](https://huggingface.co/domenicrosati/opus-mt-en-es-scielo) on the scielo dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2189
- Bleu: 41.5373
## 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: 5.6e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|
| 1.0943 | 1.0 | 10001 | 1.2189 | 41.5373 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
elliotthwang/mt5-small-finetuned-tradition-zh
|
elliotthwang
| 2022-07-18T16:44:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"mt5",
"text2text-generation",
"generated_from_trainer",
"dataset:xlsum",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-06-29T13:09:13Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xlsum
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-tradition-zh
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xlsum
type: xlsum
args: chinese_traditional
metrics:
- name: Rouge1
type: rouge
value: 5.7806
---
<!-- 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. -->
# mt5-small-finetuned-tradition-zh
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the xlsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9218
- Rouge1: 5.7806
- Rouge2: 1.266
- Rougel: 5.761
- Rougelsum: 5.7833
## 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: 5.6e-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: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|
| 4.542 | 1.0 | 2336 | 3.1979 | 4.8334 | 1.025 | 4.8142 | 4.8326 |
| 3.7542 | 2.0 | 4672 | 3.0662 | 5.2155 | 1.0978 | 5.2025 | 5.2158 |
| 3.5706 | 3.0 | 7008 | 3.0070 | 5.5471 | 1.3397 | 5.5386 | 5.5391 |
| 3.4668 | 4.0 | 9344 | 2.9537 | 5.5865 | 1.1558 | 5.5816 | 5.5964 |
| 3.4082 | 5.0 | 11680 | 2.9391 | 5.8061 | 1.3462 | 5.7944 | 5.812 |
| 3.375 | 6.0 | 14016 | 2.9218 | 5.7806 | 1.266 | 5.761 | 5.7833 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
silviacamplani/distilbert-uncase-finetuned-ai-ner
|
silviacamplani
| 2022-07-18T15:56:55Z | 8 | 0 |
transformers
|
[
"transformers",
"tf",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-08T09:55:39Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: silviacamplani/distilbert-uncase-finetuned-ai-ner
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. -->
# silviacamplani/distilbert-uncase-finetuned-ai-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.5704
- Validation Loss: 2.5380
- Epoch: 2
## 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: {'inner_optimizer': {'class_name': 'AdamWeightDecay', 'config': {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 18, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}}, 'dynamic': True, 'initial_scale': 32768.0, 'dynamic_growth_steps': 2000}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2918 | 3.0479 | 0 |
| 2.8526 | 2.6902 | 1 |
| 2.5704 | 2.5380 | 2 |
### Framework versions
- Transformers 4.18.0
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Kayvane/distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
|
Kayvane
| 2022-07-18T15:32:30Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:consumer-finance-complaints",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T08:15:34Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- consumer-finance-complaints
model-index:
- name: distilbert-base-uncased-wandb-week-3-complaints-classifier-1500
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-wandb-week-3-complaints-classifier-1500
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the consumer-finance-complaints dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
OATML-Markslab/Tranception_Large
|
OATML-Markslab
| 2022-07-18T15:25:35Z | 10 | 6 |
transformers
|
[
"transformers",
"pytorch",
"tranception",
"fill-mask",
"arxiv:2205.13760",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-14T08:54:44Z |
# Tranception model
This Hugging Face Hub repo contains the model checkpoint for the Tranception model as described in our paper ["Tranception: protein fitness prediction with autoregressive transformers and inference-time retrieval"](https://arxiv.org/abs/2205.13760). The official GitHub repository can be accessed [here](https://github.com/OATML-Markslab/Tranception). This project is a joint collaboration between the [Marks lab](https://www.deboramarkslab.com/) and the [OATML group](https://oatml.cs.ox.ac.uk/).
## Abstract
The ability to accurately model the fitness landscape of protein sequences is critical to a wide range of applications, from quantifying the effects of human variants on disease likelihood, to predicting immune-escape mutations in viruses and designing novel biotherapeutic proteins. Deep generative models of protein sequences trained on multiple sequence alignments have been the most successful approaches so far to address these tasks. The performance of these methods is however contingent on the availability of sufficiently deep and diverse alignments for reliable training. Their potential scope is thus limited by the fact many protein families are hard, if not impossible, to align. Large language models trained on massive quantities of non-aligned protein sequences from diverse families address these problems and show potential to eventually bridge the performance gap. We introduce Tranception, a novel transformer architecture leveraging autoregressive predictions and retrieval of homologous sequences at inference to achieve state-of-the-art fitness prediction performance. Given its markedly higher performance on multiple mutants, robustness to shallow alignments and ability to score indels, our approach offers significant gain of scope over existing approaches. To enable more rigorous model testing across a broader range of protein families, we develop ProteinGym -- an extensive set of multiplexed assays of variant effects, substantially increasing both the number and diversity of assays compared to existing benchmarks.
## License
This project is available under the MIT license.
## Reference
If you use Tranception or other files provided through our GitHub repository, please cite the following paper:
```
Notin, P., Dias, M., Frazer, J., Marchena-Hurtado, J., Gomez, A., Marks, D.S., Gal, Y. (2022). Tranception: Protein Fitness Prediction with Autoregressive Transformers and Inference-time Retrieval. ICML.
```
## Links
Pre-print: https://arxiv.org/abs/2205.13760
GitHub: https://github.com/OATML-Markslab/Tranception
|
andreaschandra/pegasus-samsum
|
andreaschandra
| 2022-07-18T14:47:25Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"pegasus",
"text2text-generation",
"generated_from_trainer",
"dataset:samsum",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-18T13:58:11Z |
---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
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. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.19.4
- Pytorch 1.11.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
bndgyawali/switch-transformer
|
bndgyawali
| 2022-07-18T14:27:14Z | 0 | 0 |
keras
|
[
"keras",
"tensorboard",
"tf-keras",
"switch-transformer",
"region:us"
] | null | 2022-06-13T18:58:21Z |
---
library_name: keras
tags:
- switch-transformer
---
## 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:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|----|-------------|-----|------|------|-------|-------|------------------|
|Adam|0.0010000000474974513|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32|
## Model Plot
<details>
<summary>View Model Plot</summary>
</details>
|
Lurunchik/nf-cats
|
Lurunchik
| 2022-07-18T14:16:02Z | 18 | 4 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"custom_code",
"en",
"license:mit",
"region:us"
] |
text-classification
| 2022-07-13T12:15:59Z |
---
language:
- en
license: mit
tags:
- text-classification
inference: false
widget:
- text: "Why do we need an NFQA taxonomy?"
---
# Non Factoid Question Category classification in English
## NFQA model
Repository: [https://github.com/Lurunchik/NF-CATS](https://github.com/Lurunchik/NF-CATS)
Model trained with NFQA dataset. Base model is [roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2), a RoBERTa-based model for the task of Question Answering, fine-tuned using the SQuAD2.0 dataset.
Uses `NOT-A-QUESTION`, `FACTOID`, `DEBATE`, `EVIDENCE-BASED`, `INSTRUCTION`, `REASON`, `EXPERIENCE`, `COMPARISON` labels.
## How to use NFQA cat with HuggingFace
##### Load NFQA cat and its tokenizer:
```python
from transformers import AutoTokenizer
from nfqa_model import RobertaNFQAClassification
nfqa_model = RobertaNFQAClassification.from_pretrained("Lurunchik/nf-cats")
nfqa_tokenizer = AutoTokenizer.from_pretrained("deepset/roberta-base-squad2")
```
##### Make prediction using helper function:
```python
def get_nfqa_category_prediction(text):
output = nfqa_model(**nfqa_tokenizer(text, return_tensors="pt"))
index = output.logits.argmax()
return nfqa_model.config.id2label[int(index)]
get_nfqa_category_prediction('how to assign category?')
# result
#'INSTRUCTION'
```
## Demo
You can test the model via [hugginface space](https://huggingface.co/spaces/Lurunchik/nf-cats).
[](https://huggingface.co/spaces/Lurunchik/nf-cats)
## Citation
If you use `NFQA-cats` in your work, please cite [this paper](https://dl.acm.org/doi/10.1145/3477495.3531926)
```
@misc{bolotova2022nfcats,
author = {Bolotova, Valeriia and Blinov, Vladislav and Scholer, Falk and Croft, W. Bruce and Sanderson, Mark},
title = {A Non-Factoid Question-Answering Taxonomy},
year = {2022},
isbn = {9781450387323},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3477495.3531926},
doi = {10.1145/3477495.3531926},
booktitle = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
pages = {1196–1207},
numpages = {12},
keywords = {question taxonomy, non-factoid question-answering, editorial study, dataset analysis},
location = {Madrid, Spain},
series = {SIGIR '22}
}
```
Enjoy! 🤗
|
svenstahlmann/finetuned-distilbert-needmining
|
svenstahlmann
| 2022-07-18T13:15:23Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"needmining",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T12:50:37Z |
---
language: en
tags:
- distilbert
- needmining
license: apache-2.0
metric:
- f1
---
# Finetuned-Distilbert-needmining (uncased)
This model is a finetuned version of the [Distilbert base model](https://huggingface.co/distilbert-base-uncased). It was
trained to predict need-containing sentences from amazon product reviews.
## Model description
This mode is part of ongoing research, after the publication of the research more information will be added.
## Intended uses & limitations
You can use this model to identify sentences that contain customer needs in user-generated content. This can act as a filtering process to remove uninformative content for market research.
### How to use
You can use this model directly with a pipeline for text classification:
```python
>>> from transformers import pipeline
>>> classifier = pipeline("text-classification", model="svenstahlmann/finetuned-distilbert-needmining")
>>> classifier("the plasic feels super cheap.")
[{'label': 'contains need', 'score': 0.9397542476654053}]
```
### Limitations and bias
We are not aware of any bias in the training data.
## Training data
The training was done on a dataset of 6400 sentences. The sentences were taken from product reviews off amazon and coded if they express customer needs.
## Training procedure
For the training, we used [Population Based Training (PBT)](https://www.deepmind.com/blog/population-based-training-of-neural-networks) and optimized for f1 score on a validation set of 1600 sentences.
### Preprocessing
The preprocessing follows the [Distilbert base model](https://huggingface.co/distilbert-base-uncased).
### Pretraining
The model was trained on a titan RTX for 1 hour.
## Evaluation results
Results on the validation set:
| F1 |
|:----:|
| 76.0 |
### BibTeX entry and citation info
coming soon
|
MMVos/distilbert-base-uncased-finetuned-squad
|
MMVos
| 2022-07-18T12:16:01Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad_v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-07-18T09:52:16Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: distilbert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4214
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1814 | 1.0 | 8235 | 1.2488 |
| 0.9078 | 2.0 | 16470 | 1.3127 |
| 0.7439 | 3.0 | 24705 | 1.4214 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
pritoms/opt-350m-finetuned-stack
|
pritoms
| 2022-07-18T11:14:18Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"opt",
"text-generation",
"generated_from_trainer",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-07-18T10:53:56Z |
---
license: other
tags:
- generated_from_trainer
model-index:
- name: opt-350m-finetuned-stack
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. -->
# opt-350m-finetuned-stack
This model is a fine-tuned version of [facebook/opt-350m](https://huggingface.co/facebook/opt-350m) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
julmarti/ppo-LunarLander-v2
|
julmarti
| 2022-07-18T11:06:51Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-18T11:06:23Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 246.73 +/- 23.48
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
...
```
|
dpovedano/distilbert-base-uncased-finetuned-ner
|
dpovedano
| 2022-07-18T10:13:45Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-18T10:05:44Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: dpovedano/distilbert-base-uncased-finetuned-ner
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. -->
# dpovedano/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0285
- Validation Loss: 0.0612
- Train Precision: 0.9222
- Train Recall: 0.9358
- Train F1: 0.9289
- Train Accuracy: 0.9834
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.0289 | 0.0612 | 0.9222 | 0.9358 | 0.9289 | 0.9834 | 0 |
| 0.0284 | 0.0612 | 0.9222 | 0.9358 | 0.9289 | 0.9834 | 1 |
| 0.0285 | 0.0612 | 0.9222 | 0.9358 | 0.9289 | 0.9834 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
fumakurata/distilbert-base-uncased-finetuned-emotion
|
fumakurata
| 2022-07-18T10:12:18Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T07:22:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.834
- name: F1
type: f1
value: 0.8171742650957551
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5401
- Accuracy: 0.834
- F1: 0.8172
## 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: 192
- eval_batch_size: 192
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 84 | 0.7993 | 0.74 | 0.6827 |
| No log | 2.0 | 168 | 0.5401 | 0.834 | 0.8172 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
TestZee/t5-small-finetuned-kaggle-data-t5-v2.0
|
TestZee
| 2022-07-18T09:43:21Z | 3 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-07-18T09:03:47Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: TestZee/t5-small-finetuned-kaggle-data-t5-v2.0
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-kaggle-data-t5-v2.0
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: 1.8648
- Validation Loss: 1.7172
- Train Rouge1: 24.1639
- Train Rouge2: 13.1314
- Train Rougel: 20.8170
- Train Rougelsum: 22.3549
- Train Gen Len: 19.0
- Epoch: 4
## 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 | Train Rouge1 | Train Rouge2 | Train Rougel | Train Rougelsum | Train Gen Len | Epoch |
|:----------:|:---------------:|:------------:|:------------:|:------------:|:---------------:|:-------------:|:-----:|
| 2.1107 | 1.8251 | 23.3472 | 12.6240 | 20.0726 | 21.6297 | 19.0 | 0 |
| 1.9759 | 1.7755 | 23.8048 | 13.0368 | 20.4876 | 22.1022 | 19.0 | 1 |
| 1.9275 | 1.7466 | 23.9713 | 13.1351 | 20.5833 | 22.1610 | 19.0 | 2 |
| 1.8931 | 1.7291 | 24.0628 | 13.1243 | 20.7427 | 22.2890 | 19.0 | 3 |
| 1.8648 | 1.7172 | 24.1639 | 13.1314 | 20.8170 | 22.3549 | 19.0 | 4 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Livingwithmachines/bert_1875_1890
|
Livingwithmachines
| 2022-07-18T09:37:54Z | 76 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-18T09:35:12Z |
# Neural Language Models for Nineteenth-Century English: bert_1875_1890
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1875-1890 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-with-machines/histLM
## License
The models are released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode.
## Funding Statement
This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1).
## Dataset creators
Kasra Hosseini, Kaspar Beelen and Mariona Coll Ardanuy (The Alan Turing Institute) preprocessed the text, created a database, trained and fine-tuned language models as described in the accompanying paper. Giovanni Colavizza (University of Amsterdam), David Beavan (The Alan Turing Institute) and James Hetherington (University College London) helped with planning, accessing the datasets and designing the experiments.
|
Livingwithmachines/bert_1850_1875
|
Livingwithmachines
| 2022-07-18T09:33:56Z | 27 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-18T09:31:40Z |
# Neural Language Models for Nineteenth-Century English: bert_1850_1875
## Introduction
BERT model trained on a large historical dataset of books in English, published between 1850-1875 and comprised of ~1.3 billion tokens.
- Data paper: http://doi.org/10.5334/johd.48
- Github repository: https://github.com/Living-with-machines/histLM
## License
The models are released under open license CC BY 4.0, available at https://creativecommons.org/licenses/by/4.0/legalcode.
## Funding Statement
This work was supported by Living with Machines (AHRC grant AH/S01179X/1) and The Alan Turing Institute (EPSRC grant EP/N510129/1).
## Dataset creators
Kasra Hosseini, Kaspar Beelen and Mariona Coll Ardanuy (The Alan Turing Institute) preprocessed the text, created a database, trained and fine-tuned language models as described in the accompanying paper. Giovanni Colavizza (University of Amsterdam), David Beavan (The Alan Turing Institute) and James Hetherington (University College London) helped with planning, accessing the datasets and designing the experiments.
|
rsuwaileh/IDRISI-LMR-HD-TB-partition
|
rsuwaileh
| 2022-07-18T09:17:11Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-11T20:32:05Z |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
The model is trained using Hurricane Dorian 2019 event (only the training data is used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-based LMR mode and using the random version of the data.
You can download this data in BILOU format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/hurricane_dorian_2019).
* Different variants of the model are available through HuggingFace:
- [rsuwaileh/IDRISI-LMR-HD-TB](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB)
- [rsuwaileh/IDRISI-LMR-HD-TL](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL)
- [rsuwaileh/IDRISI-LMR-HD-TL-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL-partition/)
* Larger models are available at [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
* Models trained on the entire IDRISI-R dataset:
- [rsuwaileh/IDRISI-LMR-EN-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typebased/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typebased/)
To cite this model:
```
@article{suwaileh2022tlLMR4disaster,
title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan},
journal={International Journal of Disaster Risk Reduction},
year={2022}
}
@inproceedings{suwaileh2020tlLMR4disaster,
title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6252--6263},
year={2020}
}
```
To cite the IDRISI-R dataset:
```
@article{rsuwaileh2022Idrisi-r,
title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad},
journal={...},
volume={...},
pages={...},
year={2022},
publisher={...}
}
```
|
rsuwaileh/IDRISI-LMR-HD-TL
|
rsuwaileh
| 2022-07-18T09:16:29Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-11T20:30:24Z |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
The model is trained using Hurricane Dorian 2019 event (training, development, and test data are used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-less LMR mode and using the random version of the data.
You can download this data in BILOU format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/hurricane_dorian_2019).
* Different variants of the model are available through HuggingFace:
- [rsuwaileh/IDRISI-LMR-HD-TB](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB)
- [rsuwaileh/IDRISI-LMR-HD-TB-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB-partition/)
- [rsuwaileh/IDRISI-LMR-HD-TL-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL-partition)
* Larger models are available at [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
* Models trained on the entire IDRISI-R dataset:
- [rsuwaileh/IDRISI-LMR-EN-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typebased/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typebased/)
To cite this model:
```
@article{suwaileh2022tlLMR4disaster,
title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan},
journal={International Journal of Disaster Risk Reduction},
year={2022}
}
@inproceedings{suwaileh2020tlLMR4disaster,
title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6252--6263},
year={2020}
}
```
To cite the IDRISI-R dataset:
```
@article{rsuwaileh2022Idrisi-r,
title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad},
journal={...},
volume={...},
pages={...},
year={2022},
publisher={...}
}
```
|
rsuwaileh/IDRISI-LMR-HD-TL-partition
|
rsuwaileh
| 2022-07-18T09:16:12Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-06-11T20:30:51Z |
This model is a BERT-based Location Mention Recognition model that is adopted from the [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
The model is trained using Hurricane Dorian 2019 event (training data is used for training) from [IDRISI-R dataset](https://github.com/rsuwaileh/IDRISI) under the Type-less LMR mode and using the random version of the data.
You can download this data in BILOU format from [here](https://github.com/rsuwaileh/IDRISI/tree/main/data/LMR/EN/gold-random-bilou/hurricane_dorian_2019).
* Different variants of the model are available through HuggingFace:
- [rsuwaileh/IDRISI-LMR-HD-TB](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB)
- [rsuwaileh/IDRISI-LMR-HD-TB-partition](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TB-partition/)
- [rsuwaileh/IDRISI-LMR-HD-TL](https://huggingface.co/rsuwaileh/IDRISI-LMR-HD-TL)
* Larger models are available at [TLLMR4CM GitHub](https://github.com/rsuwaileh/TLLMR4CM/).
* Models trained on the entire IDRISI-R dataset:
- [rsuwaileh/IDRISI-LMR-EN-random-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-random-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-random-typebased/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typeless](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typeless/)
- [rsuwaileh/IDRISI-LMR-EN-timebased-typebased](https://huggingface.co/rsuwaileh/IDRISI-LMR-EN-timebased-typebased/)
To cite this model:
```
@article{suwaileh2022tlLMR4disaster,
title={When a Disaster Happens, We Are Ready: Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad and Sajjad, Hassan},
journal={International Journal of Disaster Risk Reduction},
year={2022}
}
@inproceedings{suwaileh2020tlLMR4disaster,
title={Are We Ready for this Disaster? Towards Location Mention Recognition from Crisis Tweets},
author={Suwaileh, Reem and Imran, Muhammad and Elsayed, Tamer and Sajjad, Hassan},
booktitle={Proceedings of the 28th International Conference on Computational Linguistics},
pages={6252--6263},
year={2020}
}
```
To cite the IDRISI-R dataset:
```
@article{rsuwaileh2022Idrisi-r,
title={IDRISI-R: Large-scale English and Arabic Location Mention Recognition Datasets for Disaster Response over Twitter},
author={Suwaileh, Reem and Elsayed, Tamer and Imran, Muhammad},
journal={...},
volume={...},
pages={...},
year={2022},
publisher={...}
}
```
|
aatmasidha/distilbert-base-uncased-newsmodelclassification
|
aatmasidha
| 2022-07-18T09:04:59Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-12T09:10:54Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-newsmodelclassification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.928
- name: F1
type: f1
value: 0.9278415074713384
---
<!-- 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-newsmodelclassification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2177
- Accuracy: 0.928
- F1: 0.9278
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8104 | 1.0 | 250 | 0.3057 | 0.9105 | 0.9084 |
| 0.2506 | 2.0 | 500 | 0.2177 | 0.928 | 0.9278 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.11.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
Prafuld3/distilbert-base-uncased-finetuned-emotion
|
Prafuld3
| 2022-07-18T08:17:49Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-07-18T07:29:28Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.923
- name: F1
type: f1
value: 0.9232089605669606
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2185
- Accuracy: 0.923
- F1: 0.9232
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8274 | 1.0 | 250 | 0.3172 | 0.907 | 0.9036 |
| 0.2501 | 2.0 | 500 | 0.2185 | 0.923 | 0.9232 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
MadridMaverick/phobert-base-finetuned-imdb
|
MadridMaverick
| 2022-07-18T04:04:21Z | 3 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-07-18T03:55:03Z |
---
tags:
- generated_from_trainer
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 None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2510
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7146 | 1.0 | 2500 | 1.4245 |
| 1.3821 | 2.0 | 5000 | 1.2666 |
| 1.3308 | 3.0 | 7500 | 1.2564 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu113
- Datasets 2.3.2
- Tokenizers 0.12.1
|
namwoo/distilbert-base-uncased-finetuned-ner
|
namwoo
| 2022-07-18T00:38:09Z | 4 | 0 |
transformers
|
[
"transformers",
"tf",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-07-18T00:35:17Z |
---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: namwoo/distilbert-base-uncased-finetuned-ner
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. -->
# namwoo/distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0339
- Validation Loss: 0.0623
- Train Precision: 0.9239
- Train Recall: 0.9335
- Train F1: 0.9287
- Train Accuracy: 0.9829
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 2631, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Precision | Train Recall | Train F1 | Train Accuracy | Epoch |
|:----------:|:---------------:|:---------------:|:------------:|:--------:|:--------------:|:-----:|
| 0.1982 | 0.0715 | 0.9040 | 0.9218 | 0.9128 | 0.9799 | 0 |
| 0.0537 | 0.0618 | 0.9202 | 0.9305 | 0.9254 | 0.9827 | 1 |
| 0.0339 | 0.0623 | 0.9239 | 0.9335 | 0.9287 | 0.9829 | 2 |
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.8.2
- Datasets 2.3.2
- Tokenizers 0.12.1
|
pyronear/mobilenet_v3_large
|
pyronear
| 2022-07-17T23:48:57Z | 23 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1905.02244",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-17T20:31:41Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- pyronear/openfire
---
# MobileNet V3 - Large model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf).
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and-Excite blocks larger.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroVision.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:
```shell
pip install pyrovision
```
or using [conda](https://anaconda.org/pyronear/pyrovision):
```shell
conda install -c pyronear pyrovision
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from pyrovision.models import model_from_hf_hub
model = model_from_hf_hub("pyronear/mobilenet_v3_large").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-1905-02244,
author = {Andrew Howard and
Mark Sandler and
Grace Chu and
Liang{-}Chieh Chen and
Bo Chen and
Mingxing Tan and
Weijun Wang and
Yukun Zhu and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le and
Hartwig Adam},
title = {Searching for MobileNetV3},
journal = {CoRR},
volume = {abs/1905.02244},
year = {2019},
url = {http://arxiv.org/abs/1905.02244},
eprinttype = {arXiv},
eprint = {1905.02244},
timestamp = {Thu, 27 May 2021 16:20:51 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{chintala_torchvision_2017,
author = {Chintala, Soumith},
month = {4},
title = {{Torchvision}},
url = {https://github.com/pytorch/vision},
year = {2017}
}
```
|
pyronear/mobilenet_v3_small
|
pyronear
| 2022-07-17T23:48:39Z | 29 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1905.02244",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-13T23:53:41Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- pyronear/openfire
---
# MobileNet V3 - Small model
Pretrained on a dataset for wildfire binary classification (soon to be shared). The MobileNet V3 architecture was introduced in [this paper](https://arxiv.org/pdf/1905.02244.pdf).
## Model description
The core idea of the author is to simplify the final stage, while using SiLU as activations and making Squeeze-and-Excite blocks larger.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroVision.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:
```shell
pip install pyrovision
```
or using [conda](https://anaconda.org/pyronear/pyrovision):
```shell
conda install -c pyronear pyrovision
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from pyrovision.models import model_from_hf_hub
model = model_from_hf_hub("pyronear/mobilenet_v3_small").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/abs-1905-02244,
author = {Andrew Howard and
Mark Sandler and
Grace Chu and
Liang{-}Chieh Chen and
Bo Chen and
Mingxing Tan and
Weijun Wang and
Yukun Zhu and
Ruoming Pang and
Vijay Vasudevan and
Quoc V. Le and
Hartwig Adam},
title = {Searching for MobileNetV3},
journal = {CoRR},
volume = {abs/1905.02244},
year = {2019},
url = {http://arxiv.org/abs/1905.02244},
eprinttype = {arXiv},
eprint = {1905.02244},
timestamp = {Thu, 27 May 2021 16:20:51 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1905-02244.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{chintala_torchvision_2017,
author = {Chintala, Soumith},
month = {4},
title = {{Torchvision}},
url = {https://github.com/pytorch/vision},
year = {2017}
}
```
|
pyronear/resnet34
|
pyronear
| 2022-07-17T23:48:22Z | 25 | 0 |
transformers
|
[
"transformers",
"pytorch",
"onnx",
"image-classification",
"dataset:pyronear/openfire",
"arxiv:1512.03385",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2022-07-17T21:07:12Z |
---
license: apache-2.0
tags:
- image-classification
- pytorch
- onnx
datasets:
- pyronear/openfire
---
# ResNet-34 model
Pretrained on a dataset for wildfire binary classification (soon to be shared).
## Model description
The core idea of the author is to help the gradient propagation through numerous layers by adding a skip connection.
## Installation
### Prerequisites
Python 3.6 (or higher) and [pip](https://pip.pypa.io/en/stable/)/[conda](https://docs.conda.io/en/latest/miniconda.html) are required to install PyroVision.
### Latest stable release
You can install the last stable release of the package using [pypi](https://pypi.org/project/pyrovision/) as follows:
```shell
pip install pyrovision
```
or using [conda](https://anaconda.org/pyronear/pyrovision):
```shell
conda install -c pyronear pyrovision
```
### Developer mode
Alternatively, if you wish to use the latest features of the project that haven't made their way to a release yet, you can install the package from source *(install [Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git) first)*:
```shell
git clone https://github.com/pyronear/pyro-vision.git
pip install -e pyro-vision/.
```
## Usage instructions
```python
from PIL import Image
from torchvision.transforms import Compose, ConvertImageDtype, Normalize, PILToTensor, Resize
from torchvision.transforms.functional import InterpolationMode
from pyrovision.models import model_from_hf_hub
model = model_from_hf_hub("pyronear/resnet34").eval()
img = Image.open(path_to_an_image).convert("RGB")
# Preprocessing
config = model.default_cfg
transform = Compose([
Resize(config['input_shape'][1:], interpolation=InterpolationMode.BILINEAR),
PILToTensor(),
ConvertImageDtype(torch.float32),
Normalize(config['mean'], config['std'])
])
input_tensor = transform(img).unsqueeze(0)
# Inference
with torch.inference_mode():
output = model(input_tensor)
probs = output.squeeze(0).softmax(dim=0)
```
## Citation
Original paper
```bibtex
@article{DBLP:journals/corr/HeZRS15,
author = {Kaiming He and
Xiangyu Zhang and
Shaoqing Ren and
Jian Sun},
title = {Deep Residual Learning for Image Recognition},
journal = {CoRR},
volume = {abs/1512.03385},
year = {2015},
url = {http://arxiv.org/abs/1512.03385},
eprinttype = {arXiv},
eprint = {1512.03385},
timestamp = {Wed, 17 Apr 2019 17:23:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/HeZRS15.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
Source of this implementation
```bibtex
@software{chintala_torchvision_2017,
author = {Chintala, Soumith},
month = {4},
title = {{Torchvision}},
url = {https://github.com/pytorch/vision},
year = {2017}
}
```
|
csalcedo/ppo-LunarLander-v2
|
csalcedo
| 2022-07-17T17:54:22Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-16T20:58:29Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 241.17 +/- 21.24
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
...
```
|
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese
|
Edresson
| 2022-07-17T17:43:02Z | 20 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: pt
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 20.39
---
# Wav2vec2 Large 100k Voxpopuli fine-tuned with Common Voice and TTS-Portuguese Corpus in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using the Common Voice 7.0 and TTS-Portuguese Corpus.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common-Voice_plus_TTS-Dataset-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-6.1-2020-12-11")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
|
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese
|
Edresson
| 2022-07-17T17:39:10Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: pt
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 33.96
---
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset plus a data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-plus-data-augmentation-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
|
Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian
|
Edresson
| 2022-07-17T17:37:45Z | 20 | 2 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"Russian-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-03-02T23:29:04Z |
---
language: pt
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- Russian-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 19.46
---
# Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, MAILABS plus data augmentation
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) Wav2vec2 Large 100k Voxpopuli fine-tuned in Russian using the Common Voice 7.0, M-AILABS plus data augmentation method based on TTS and voice conversion.
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-Common_Voice_plus_TTS-Dataset_plus_Data_Augmentation-russian")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "ru", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
|
Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese
|
Edresson
| 2022-07-17T17:37:08Z | 9 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"pt",
"portuguese-speech-corpus",
"PyTorch",
"arxiv:2204.00618",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-07-17T17:18:45Z |
---
language: pt
datasets:
- Common Voice
metrics:
- wer
tags:
- audio
- speech
- wav2vec2
- pt
- portuguese-speech-corpus
- automatic-speech-recognition
- speech
- PyTorch
license: apache-2.0
model-index:
- name: Edresson Casanova Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset in Portuguese
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
metrics:
- name: Test Common Voice 7.0 WER
type: wer
value: 63.90
---
# Wav2vec2 Large 100k Voxpopuli fine-tuned with a single-speaker dataset plus Data Augmentation in Portuguese
[Wav2vec2 Large 100k Voxpopuli](https://huggingface.co/facebook/wav2vec2-large-100k-voxpopuli) fine-tuned in Portuguese using a single-speaker dataset (TTS-Portuguese Corpus).
# Use this model
```python
from transformers import AutoTokenizer, Wav2Vec2ForCTC
tokenizer = AutoTokenizer.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese")
model = Wav2Vec2ForCTC.from_pretrained("Edresson/wav2vec2-large-100k-voxpopuli-ft-TTS-Dataset-portuguese")
```
# Results
For the results check the [paper](https://arxiv.org/abs/2204.00618)
# Example test with Common Voice Dataset
```python
dataset = load_dataset("common_voice", "pt", split="test", data_dir="./cv-corpus-7.0-2021-07-21")
resampler = torchaudio.transforms.Resample(orig_freq=48_000, new_freq=16_000)
def map_to_array(batch):
speech, _ = torchaudio.load(batch["path"])
batch["speech"] = resampler.forward(speech.squeeze(0)).numpy()
batch["sampling_rate"] = resampler.new_freq
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower().replace("’", "'")
return batch
```
```python
ds = dataset.map(map_to_array)
result = ds.map(map_to_pred, batched=True, batch_size=1, remove_columns=list(ds.features.keys()))
print(wer.compute(predictions=result["predicted"], references=result["target"]))
```
|
meln1k/Reinforce-Pixelcopter-PLE-v0
|
meln1k
| 2022-07-17T16:16:38Z | 0 | 0 | null |
[
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-07-17T14:59:41Z |
---
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: 9.60 +/- 8.33
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
|
Subsets and Splits
Filtered Qwen2.5 Distill Models
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