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kingabzpro/wav2vec2-large-xls-r-1b-Swedish | e88c671af0d195339661c0c3afe8e806e0af353f | 2022-03-24T11:58:17.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"sv-SE",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | kingabzpro | null | kingabzpro/wav2vec2-large-xls-r-1b-Swedish | 11 | null | transformers | 11,100 | ---
language:
- sv-SE
license: apache-2.0
tags:
- automatic-speech-recognition
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
metrics:
- wer
- cer
model-index:
- name: wav2vec2-large-xls-r-1b-Swedish
results:
- task:
type: automatic-speech-recognition
name: Speech Recognition
dataset:
type: mozilla-foundation/common_voice_8_0
name: Common Voice sv-SE
args: sv-SE
metrics:
- type: wer
value: 14.04
name: Test WER With LM
args:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
- type: cer
value: 4.86
name: Test CER With LM
args:
- learning_rate: 7.5e-05
- train_batch_size: 32
- eval_batch_size: 8
- 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_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: sv
metrics:
- name: Test WER
type: wer
value: 29.69
- name: Test CER
type: cer
value: 12.59
---
<!-- 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-large-xls-r-1b-Swedish
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice dataset.
It achieves the following results on the evaluation set:
**Without LM**
- Loss: 0.3370
- Wer: 18.44
- Cer: 5.75
**With LM**
- Loss: 0.3370
- Wer: 14.04
- Cer: 4.86
#### Evaluation Commands
1. To evaluate on `mozilla-foundation/common_voice_8_0` with split `test`
```bash
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset mozilla-foundation/common_voice_8_0 --config sv-SE --split test
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py --model_id kingabzpro/wav2vec2-large-xls-r-1b-Swedish --dataset speech-recognition-community-v2/dev_data --config sv --split validation --chunk_length_s 5.0 --stride_length_s 1.0
```
### Inference With LM
```python
import torch
from datasets import load_dataset
from transformers import AutoModelForCTC, AutoProcessor
import torchaudio.functional as F
model_id = "kingabzpro/wav2vec2-large-xls-r-1b-Swedish"
sample_iter = iter(load_dataset("mozilla-foundation/common_voice_8_0", "sv-SE", split="test", streaming=True, use_auth_token=True))
sample = next(sample_iter)
resampled_audio = F.resample(torch.tensor(sample["audio"]["array"]), 48_000, 16_000).numpy()
model = AutoModelForCTC.from_pretrained(model_id)
processor = AutoProcessor.from_pretrained(model_id)
input_values = processor(resampled_audio, return_tensors="pt").input_values
with torch.no_grad():
logits = model(input_values).logits
transcription = processor.batch_decode(logits.numpy()).text
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 3.1562 | 11.11 | 500 | 0.4830 | 0.3729 | 0.1169 |
| 0.5655 | 22.22 | 1000 | 0.3553 | 0.2381 | 0.0743 |
| 0.3376 | 33.33 | 1500 | 0.3359 | 0.2179 | 0.0696 |
| 0.2419 | 44.44 | 2000 | 0.3232 | 0.1844 | 0.0575 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
ktrapeznikov/scibert_scivocab_uncased_squad_v2 | 2a507379876427c3b1ddbea6ef7825c36c5a7ddb | 2021-05-19T21:11:07.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | ktrapeznikov | null | ktrapeznikov/scibert_scivocab_uncased_squad_v2 | 11 | null | transformers | 11,101 | ### Model
**[`allenai/scibert_scivocab_uncased`](https://huggingface.co/allenai/scibert_scivocab_uncased)** fine-tuned on **[`SQuAD V2`](https://rajpurkar.github.io/SQuAD-explorer/)** using **[`run_squad.py`](https://github.com/huggingface/transformers/blob/master/examples/question-answering/run_squad.py)**
### Training Parameters
Trained on 4 NVIDIA GeForce RTX 2080 Ti 11Gb
```bash
BASE_MODEL=allenai/scibert_scivocab_uncased
python run_squad.py \
--version_2_with_negative \
--model_type albert \
--model_name_or_path $BASE_MODEL \
--output_dir $OUTPUT_MODEL \
--do_eval \
--do_lower_case \
--train_file $SQUAD_DIR/train-v2.0.json \
--predict_file $SQUAD_DIR/dev-v2.0.json \
--per_gpu_train_batch_size 18 \
--per_gpu_eval_batch_size 64 \
--learning_rate 3e-5 \
--num_train_epochs 3.0 \
--max_seq_length 384 \
--doc_stride 128 \
--save_steps 2000 \
--threads 24 \
--warmup_steps 550 \
--gradient_accumulation_steps 1 \
--fp16 \
--logging_steps 50 \
--do_train
```
### Evaluation
Evaluation on the dev set. I did not sweep for best threshold.
| | val |
|-------------------|-------------------|
| exact | 75.07790785816559 |
| f1 | 78.47735207283013 |
| total | 11873.0 |
| HasAns_exact | 70.76585695006747 |
| HasAns_f1 | 77.57449412292718 |
| HasAns_total | 5928.0 |
| NoAns_exact | 79.37762825904122 |
| NoAns_f1 | 79.37762825904122 |
| NoAns_total | 5945.0 |
| best_exact | 75.08633032931863 |
| best_exact_thresh | 0.0 |
| best_f1 | 78.48577454398324 |
| best_f1_thresh | 0.0 |
### Usage
See [huggingface documentation](https://huggingface.co/transformers/model_doc/bert.html#bertforquestionanswering). Training on `SQuAD V2` allows the model to score if a paragraph contains an answer:
```python
start_scores, end_scores = model(input_ids)
span_scores = start_scores.softmax(dim=1).log()[:,:,None] + end_scores.softmax(dim=1).log()[:,None,:]
ignore_score = span_scores[:,0,0] #no answer scores
```
|
kurianbenoy/bert-finetuned-ner | c0c6c18649ac5e30ad860ef747854c8645939d04 | 2022-02-23T11:48:55.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | kurianbenoy | null | kurianbenoy/bert-finetuned-ner | 11 | null | transformers | 11,102 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9304777594728171
- name: Recall
type: recall
value: 0.9505217098619994
- name: F1
type: f1
value: 0.9403929403929404
- name: Accuracy
type: accuracy
value: 0.9861070230176017
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0611
- Precision: 0.9305
- Recall: 0.9505
- F1: 0.9404
- Accuracy: 0.9861
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0869 | 1.0 | 1756 | 0.0680 | 0.9174 | 0.9342 | 0.9257 | 0.9827 |
| 0.0334 | 2.0 | 3512 | 0.0620 | 0.9305 | 0.9470 | 0.9387 | 0.9853 |
| 0.0233 | 3.0 | 5268 | 0.0611 | 0.9305 | 0.9505 | 0.9404 | 0.9861 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
l3cube-pune/hate-roberta-hasoc-hindi | ee9573aeb024582b097a46522825dc0b9a7e544f | 2021-10-30T18:05:58.000Z | [
"pytorch",
"tf",
"roberta",
"text-classification",
"hi",
"dataset:HASOC 2021",
"arxiv:2110.12200",
"transformers",
"license:cc-by-4.0"
]
| text-classification | false | l3cube-pune | null | l3cube-pune/hate-roberta-hasoc-hindi | 11 | null | transformers | 11,103 | ---
language: hi
tags:
- roberta
license: cc-by-4.0
datasets:
- HASOC 2021
widget:
- text: "I like you. </s></s> I love you."
---
## hate-roberta-hasoc-hindi
hate-roberta-hasoc-hindi is a binary hate speech model fine-tuned on Hindi Hasoc Hate Speech Dataset 2021.
The label mappings are 0 -> None, 1 -> Hate.
More details on the dataset, models, and baseline results can be found in our [paper] (https://arxiv.org/abs/2110.12200)
```
@article{velankar2021hate,
title={Hate and Offensive Speech Detection in Hindi and Marathi},
author={Velankar, Abhishek and Patil, Hrushikesh and Gore, Amol and Salunke, Shubham and Joshi, Raviraj},
journal={arXiv preprint arXiv:2110.12200},
year={2021}
}
``` |
lewtun/xlm-roberta-base-finetuned-marc-de | fa4a1282887d16b28a4dad4b4a6b7645b2b5b3cc | 2021-10-16T11:38:18.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"dataset:amazon_reviews_multi",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index"
]
| text-classification | false | lewtun | null | lewtun/xlm-roberta-base-finetuned-marc-de | 11 | null | transformers | 11,104 | ---
license: mit
tags:
- generated_from_trainer
datasets:
- amazon_reviews_multi
model-index:
- name: xlm-roberta-base-finetuned-marc-de
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-roberta-base-finetuned-marc-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the amazon_reviews_multi dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9934
- Mae: 0.4867
## 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 | Mae |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1514 | 1.0 | 308 | 1.0455 | 0.5221 |
| 0.9997 | 2.0 | 616 | 0.9934 | 0.4867 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.9.0+cu111
- Datasets 1.13.3
- Tokenizers 0.10.3
|
log0/wav2vec2-base-lang-id | 9828f4fd957e470cc9d536d4cdda2917ead0eaf8 | 2022-02-18T14:36:19.000Z | [
"pytorch",
"tensorboard",
"hubert",
"audio-classification",
"transformers"
]
| audio-classification | false | log0 | null | log0/wav2vec2-base-lang-id | 11 | null | transformers | 11,105 | Entry not found |
luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_5e6_bb_lr_5e6_grad | d10a675e306e33514b54b10ce90d7ca4ad1940ca | 2021-10-29T01:09:24.000Z | [
"pytorch",
"roberta",
"transformers"
]
| null | false | luffycodes | null | luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_5e6_bb_lr_5e6_grad | 11 | null | transformers | 11,106 | Entry not found |
luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_5e6_bb_lr_5e6_wu_25k_ep_10_grad_adam | c75e98654ff02a47f5388c3edfb4616fe5bc63ec | 2021-10-31T07:44:13.000Z | [
"pytorch",
"roberta",
"transformers"
]
| null | false | luffycodes | null | luffycodes/bb_narataka_roberta_large_nli_bsz_16_bb_bsz_16_nli_lr_5e6_bb_lr_5e6_wu_25k_ep_10_grad_adam | 11 | null | transformers | 11,107 | Entry not found |
m3hrdadfi/albert-fa-base-v2-clf-digimag | 96f4b588dee49f8e41df85e83d3042872a5db952 | 2020-12-26T08:28:59.000Z | [
"pytorch",
"tf",
"albert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | false | m3hrdadfi | null | m3hrdadfi/albert-fa-base-v2-clf-digimag | 11 | null | transformers | 11,108 | ---
language: fa
license: apache-2.0
---
# ALBERT Persian
A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language
> میتونی بهش بگی برت_کوچولو
[ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) is the first attempt on ALBERT for the Persian Language. The model was trained based on Google's ALBERT BASE Version 2.0 over various writing styles from numerous subjects (e.g., scientific, novels, news) with more than 3.9M documents, 73M sentences, and 1.3B words, like the way we did for ParsBERT.
Please follow the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo for the latest information about previous and current models.
## Persian Text Classification [DigiMag, Persian News]
The task target is labeling texts in a supervised manner in both existing datasets `DigiMag` and `Persian News`.
### DigiMag
A total of 8,515 articles scraped from [Digikala Online Magazine](https://www.digikala.com/mag/). This dataset includes seven different classes.
1. Video Games
2. Shopping Guide
3. Health Beauty
4. Science Technology
5. General
6. Art Cinema
7. Books Literature
| Label | # |
|:------------------:|:----:|
| Video Games | 1967 |
| Shopping Guide | 125 |
| Health Beauty | 1610 |
| Science Technology | 2772 |
| General | 120 |
| Art Cinema | 1667 |
| Books Literature | 254 |
**Download**
You can download the dataset from [here](https://drive.google.com/uc?id=1YgrCYY-Z0h2z0-PfWVfOGt1Tv0JDI-qz)
## Results
The following table summarizes the F1 score obtained by ParsBERT as compared to other models and architectures.
| Dataset | ALBERT-fa-base-v2 | ParsBERT-v1 | mBERT |
|:-----------------:|:-----------------:|:-----------:|:-----:|
| Digikala Magazine | 92.33 | 93.59 | 90.72 |
### BibTeX entry and citation info
Please cite in publications as the following:
```bibtex
@misc{ALBERTPersian,
author = {Mehrdad Farahani},
title = {ALBERT-Persian: A Lite BERT for Self-supervised Learning of Language Representations for the Persian Language},
year = {2020},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/m3hrdadfi/albert-persian}},
}
@article{ParsBERT,
title={ParsBERT: Transformer-based Model for Persian Language Understanding},
author={Mehrdad Farahani, Mohammad Gharachorloo, Marzieh Farahani, Mohammad Manthouri},
journal={ArXiv},
year={2020},
volume={abs/2005.12515}
}
```
## Questions?
Post a Github issue on the [ALBERT-Persian](https://github.com/m3hrdadfi/albert-persian) repo. |
m3hrdadfi/albert-zwnj-wnli-mean-tokens | 3b8686736842a2228fcdc2329c5dea18114c5784 | 2021-06-28T17:42:32.000Z | [
"pytorch",
"albert",
"feature-extraction",
"sentence-transformers",
"sentence-similarity",
"transformers"
]
| feature-extraction | false | m3hrdadfi | null | m3hrdadfi/albert-zwnj-wnli-mean-tokens | 11 | null | sentence-transformers | 11,109 | ---
pipeline_tag: feature-extraction
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# Sentence Embeddings with `albert-zwnj-wnli-mean-tokens`
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
pip install -U sentencepiece
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = [
'اولین حکمران شهر بابل کی بود؟',
'در فصل زمستان چه اتفاقی افتاد؟',
'میراث کوروش'
]
model = SentenceTransformer('m3hrdadfi/albert-zwnj-wnli-mean-tokens')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
# Max Pooling - Take the max value over time for every dimension.
def max_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
token_embeddings[input_mask_expanded == 0] = -1e9 # Set padding tokens to large negative value
return torch.mean(token_embeddings, 1)[0]
# Sentences we want sentence embeddings for
sentences = [
'اولین حکمران شهر بابل کی بود؟',
'در فصل زمستان چه اتفاقی افتاد؟',
'میراث کوروش'
]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('m3hrdadfi/albert-zwnj-wnli-mean-tokens')
model = AutoModel.from_pretrained('m3hrdadfi/albert-zwnj-wnli-mean-tokens')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, max pooling.
sentence_embeddings = max_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Questions?
Post a Github issue from [HERE](https://github.com/m3hrdadfi/sentence-transformers). |
m3hrdadfi/bert-fa-base-uncased-farstail | 43915c0a3b8fe90b679368e8f66c56bbbd473d82 | 2021-05-28T06:02:52.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"fa",
"transformers",
"license:apache-2.0"
]
| text-classification | false | m3hrdadfi | null | m3hrdadfi/bert-fa-base-uncased-farstail | 11 | null | transformers | 11,110 | ---
language: fa
license: apache-2.0
---
# FarsTail + ParsBERT
Please follow the [FarsTail](https://github.com/dml-qom/FarsTail) repo for the latest information about the dataset. For accessing the beneficiary models from this dataset, check out the [Sentence-Transformer](https://github.com/m3hrdadfi/sentence-transformers) repo
```bibtex
@article{amirkhani2020farstail,
title={FarsTail: A Persian Natural Language Inference Dataset},
author={Hossein Amirkhani, Mohammad Azari Jafari, Azadeh Amirak, Zohreh Pourjafari, Soroush Faridan Jahromi, and Zeinab Kouhkan},
journal={arXiv preprint arXiv:2009.08820},
year={2020}
}
``` |
macedonizer/blaze-koneski | d871ee88171960c3b99ba84a63a6bb8e93fbffa8 | 2021-09-22T08:58:34.000Z | [
"pytorch",
"gpt2",
"text-generation",
"mk",
"dataset:wiki-mk",
"dataset:blaze-koneski-poetry",
"transformers",
"license:apache-2.0"
]
| text-generation | false | macedonizer | null | macedonizer/blaze-koneski | 11 | null | transformers | 11,111 | ---
language:
- mk
thumbnail: https://huggingface.co/macedonizer/blaze-koneski/blaze-koneski.jpg
license: apache-2.0
datasets:
- wiki-mk
- blaze-koneski-poetry
---
# blaze-koneski
GPT-2 type of model. We finetuned macedonizer/mk-gpt-2 with Blaze Koneski's poetry.
## About Blaze Koneski
Born in a village near Prilep in 1921. Studied philology at Skopje University and worked there as a professor. Was the first chairman of the Macedonian Academy of Sciences and Arts, corresponding member of the Yugoslav Academy of Sciences and Arts, as well as of the Serbian and Slovene Academies, and honorary doctor of the Universities of Chicago and Krakow.
Wrote poetry, short stories, and essays, as well as scholarly works, many of them on the Macedonian language. Editor of the Dictionarv of the Macedonian Language, translator of Heine and Shakespeare. His works have been translated into Serbian, Croatian, Slovene, Albanian, Turkish, Hungarian, French, Russian, Italian, Greek, Polish, Romanian, German, and English.
Winner of numerous prizes, including the Golden Wreath of the Struga Poetry Evenings.
### How to use
Here is how to use this model to get the features of a given text in PyTorch:
import random
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained('macedonizer/blaze-koneski')
nmodel = AutoModelWithLMHead.from_pretrained('macedonizer/blaze-koneski')
input_text = 'Москва '
if len(input_text) == 0: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
) \
else: \
encoded_input = tokenizer(input_text, return_tensors="pt") \
output = model.generate( \
**encoded_input, \
bos_token_id=random.randint(1, 50000), \
do_sample=True, \
top_k=50, \
max_length=1024, \
top_p=0.95, \
num_return_sequences=1, \
)
decoded_output = [] \
for sample in output: \
decoded_output.append(tokenizer.decode(sample, skip_special_tokens=True))
print(decoded_output) |
malay-huggingface/t5-super-tiny-bahasa-cased | 0254fae6ae8e24db9806111d5db979e54bc69352 | 2021-09-05T13:17:40.000Z | [
"pytorch",
"t5",
"feature-extraction",
"ms",
"transformers"
]
| feature-extraction | false | malay-huggingface | null | malay-huggingface/t5-super-tiny-bahasa-cased | 11 | null | transformers | 11,112 | ---
language: ms
---
# t5-super-tiny-bahasa-cased
Pretrained T5 super-tiny language model for Malay.
## Pretraining Corpus
`t5-super-tiny-bahasa-cased` model was pretrained on multiple tasks. Below is list of tasks we trained on,
1. Language masking task on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and translated The Pile.
2. News title prediction on bahasa news.
3. Next sentence prediction on bahasa news, bahasa Wikipedia, bahasa Academia.edu, bahasa parliament and translated The Pile.
4. Translated QA Natural.
5. Text Similarity task on translated SNLI and translated MNLI.
6. EN-MS translation.
7. MS-EN translation.
8. Abstractive Summarization.
9. Knowledge Graph triples generation.
10. Paraphrase.
Preparing steps can reproduce at https://github.com/huseinzol05/malaya/tree/master/pretrained-model/t5/prepare
## Pretraining details
- This model was trained using Google T5 repository https://github.com/google-research/text-to-text-transfer-transformer, on v3-8 TPU.
- All steps can reproduce from here, https://github.com/huseinzol05/Malaya/tree/master/pretrained-model/t5
## Load Pretrained Model
You can use this model by installing `torch` or `tensorflow` and Huggingface library `transformers`. And you can use it directly by initializing it like this:
```python
from transformers import T5Tokenizer, T5Model
model = T5Model.from_pretrained('malay-huggingface/t5-super-tiny-bahasa-cased')
tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-super-tiny-bahasa-cased')
```
## Example using T5ForConditionalGeneration
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
tokenizer = T5Tokenizer.from_pretrained('malay-huggingface/t5-super-tiny-bahasa-cased')
model = T5ForConditionalGeneration.from_pretrained('malay-huggingface/t5-super-tiny-bahasa-cased')
input_ids = tokenizer.encode('soalan: siapakah perdana menteri malaysia?', return_tensors = 'pt')
outputs = model.generate(input_ids)
print(tokenizer.decode(outputs[0]))
```
Output is,
```
'Mahathir Mohamad'
```
## Supported prefix
1. `soalan: {string}`, trained using Natural QA.
2. `ringkasan: {string}`, for abstractive summarization.
3. `tajuk: {string}`, for abstractive title.
4. `parafrasa: {string}`, for abstractive paraphrase.
5. `terjemah Inggeris ke Melayu: {string}`, for EN-MS translation.
6. `terjemah Melayu ke Inggeris: {string}`, for MS-EN translation.
7. `grafik pengetahuan: {string}`, for MS text to EN Knowledge Graph triples format.
8. `ayat1: {string1} ayat2: {string2}`, semantic similarity. |
mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili | 478845b9ef072a42229850adfe6ed5838d33030a | 2021-11-25T09:03:58.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"sw",
"dataset:masakhaner",
"arxiv:2103.11811",
"transformers",
"NER",
"autotrain_compatible"
]
| token-classification | false | mbeukman | null | mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili | 11 | null | transformers | 11,113 | ---
language:
- sw
tags:
- NER
datasets:
- masakhaner
metrics:
- f1
- precision
- recall
widget:
- text: "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."
---
# xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-hausa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Swahili part.
More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
## About
This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Contact & More information
For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
### Training Resources
In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1.
## Data
The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
The motivation for the use of this data is that it is the "first large, publicly available, high quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
## Intended Use
This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
## Limitations
This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer.
Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data).
As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often.
Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to.
### Privacy & Ethical Considerations
The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
No explicit ethical considerations or adjustments were made during fine-tuning of this model.
## Metrics
The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
## Caveats and Recommendations
In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data.
## Model Structure
Here are some performance details on this specific model, compared to others we trained.
All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
| Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) |
| -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- |
| [xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili) (This model) | [hau](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-hausa) | swa | 88.36 | 86.95 | 89.82 | 86.00 | 91.00 | 77.00 | 94.00 |
| [xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-igbo-finetuned-ner-swahili) | [ibo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-igbo) | swa | 87.75 | 86.55 | 88.97 | 85.00 | 92.00 | 77.00 | 91.00 |
| [xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-kinyarwanda-finetuned-ner-swahili) | [kin](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-kinyarwanda) | swa | 87.26 | 85.15 | 89.48 | 83.00 | 91.00 | 75.00 | 93.00 |
| [xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-luganda-finetuned-ner-swahili) | [lug](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luganda) | swa | 88.93 | 87.64 | 90.25 | 83.00 | 92.00 | 79.00 | 95.00 |
| [xlm-roberta-base-finetuned-luo-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-luo-finetuned-ner-swahili) | [luo](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-luo) | swa | 87.93 | 86.91 | 88.97 | 83.00 | 91.00 | 76.00 | 94.00 |
| [xlm-roberta-base-finetuned-naija-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-naija-finetuned-ner-swahili) | [pcm](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-naija) | swa | 87.26 | 85.15 | 89.48 | 83.00 | 91.00 | 75.00 | 93.00 |
| [xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-swahili) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | swa | 90.36 | 88.59 | 92.20 | 86.00 | 93.00 | 79.00 | 96.00 |
| [xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-swahili) | [wol](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) | swa | 87.80 | 86.50 | 89.14 | 86.00 | 90.00 | 78.00 | 93.00 |
| [xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-yoruba-finetuned-ner-swahili) | [yor](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-yoruba) | swa | 87.73 | 86.67 | 88.80 | 85.00 | 91.00 | 75.00 | 93.00 |
| [xlm-roberta-base-finetuned-ner-swahili](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-swahili) | [base](https://huggingface.co/xlm-roberta-base) | swa | 88.71 | 86.84 | 90.67 | 83.00 | 91.00 | 79.00 | 95.00 |
## Usage
To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = 'mbeukman/xlm-roberta-base-finetuned-hausa-finetuned-ner-swahili'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "Wizara ya afya ya Tanzania imeripoti Jumatatu kuwa , watu takriban 14 zaidi wamepata maambukizi ya Covid - 19 ."
ner_results = nlp(example)
print(ner_results)
```
|
mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof | 3e4fa1bb16d8ed422e603817c06dee81269479c0 | 2021-11-25T09:05:13.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"wo",
"dataset:masakhaner",
"arxiv:2103.11811",
"transformers",
"NER",
"autotrain_compatible"
]
| token-classification | false | mbeukman | null | mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof | 11 | null | transformers | 11,114 | ---
language:
- wo
tags:
- NER
datasets:
- masakhaner
metrics:
- f1
- precision
- recall
widget:
- text: "SAFIYETU BÉEY Céy Koronaa !"
---
# xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof
This is a token classification (specifically NER) model that fine-tuned [xlm-roberta-base-finetuned-wolof](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) on the [MasakhaNER](https://arxiv.org/abs/2103.11811) dataset, specifically the Wolof part.
More information, and other similar models can be found in the [main Github repository](https://github.com/Michael-Beukman/NERTransfer).
## About
This model is transformer based and was fine-tuned on the MasakhaNER dataset. It is a named entity recognition dataset, containing mostly news articles in 10 different African languages.
The model was fine-tuned for 50 epochs, with a maximum sequence length of 200, 32 batch size, 5e-5 learning rate. This process was repeated 5 times (with different random seeds), and this uploaded model performed the best out of those 5 seeds (aggregate F1 on test set).
This model was fine-tuned by me, Michael Beukman while doing a project at the University of the Witwatersrand, Johannesburg. This is version 1, as of 20 November 2021.
This model is licensed under the [Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0).
### Contact & More information
For more information about the models, including training scripts, detailed results and further resources, you can visit the the [main Github repository](https://github.com/Michael-Beukman/NERTransfer). You can contact me by filing an issue on this repository.
### Training Resources
In the interest of openness, and reporting resources used, we list here how long the training process took, as well as what the minimum resources would be to reproduce this. Fine-tuning each model on the NER dataset took between 10 and 30 minutes, and was performed on a NVIDIA RTX3090 GPU. To use a batch size of 32, at least 14GB of GPU memory was required, although it was just possible to fit these models in around 6.5GB's of VRAM when using a batch size of 1.
## Data
The train, evaluation and test datasets were taken directly from the MasakhaNER [Github](https://github.com/masakhane-io/masakhane-ner) repository, with minimal to no preprocessing, as the original dataset is already of high quality.
The motivation for the use of this data is that it is the "first large, publicly available, high quality dataset for named entity recognition (NER) in ten African languages" ([source](https://arxiv.org/pdf/2103.11811.pdf)). The high-quality data, as well as the groundwork laid by the paper introducing it are some more reasons why this dataset was used. For evaluation, the dedicated test split was used, which is from the same distribution as the training data, so this model may not generalise to other distributions, and further testing would need to be done to investigate this. The exact distribution of the data is covered in detail [here](https://arxiv.org/abs/2103.11811).
## Intended Use
This model are intended to be used for NLP research into e.g. interpretability or transfer learning. Using this model in production is not supported, as generalisability and downright performance is limited. In particular, this is not designed to be used in any important downstream task that could affect people, as harm could be caused by the limitations of the model, described next.
## Limitations
This model was only trained on one (relatively small) dataset, covering one task (NER) in one domain (news articles) and in a set span of time. The results may not generalise, and the model may perform badly, or in an unfair / biased way if used on other tasks. Although the purpose of this project was to investigate transfer learning, the performance on languages that the model was not trained for does suffer.
Because this model used xlm-roberta-base as its starting point (potentially with domain adaptive fine-tuning on specific languages), this model's limitations can also apply here. These can include being biased towards the hegemonic viewpoint of most of its training data, being ungrounded and having subpar results on other languages (possibly due to unbalanced training data).
As [Adelani et al. (2021)](https://arxiv.org/abs/2103.11811) showed, the models in general struggled with entities that were either longer than 3 words and entities that were not contained in the training data. This could bias the models towards not finding, e.g. names of people that have many words, possibly leading to a misrepresentation in the results. Similarly, names that are uncommon, and may not have been found in the training data (due to e.g. different languages) would also be predicted less often.
Additionally, this model has not been verified in practice, and other, more subtle problems may become prevalent if used without any verification that it does what it is supposed to.
### Privacy & Ethical Considerations
The data comes from only publicly available news sources, the only available data should cover public figures and those that agreed to be reported on. See the original MasakhaNER paper for more details.
No explicit ethical considerations or adjustments were made during fine-tuning of this model.
## Metrics
The language adaptive models achieve (mostly) superior performance over starting with xlm-roberta-base. Our main metric was the aggregate F1 score for all NER categories.
These metrics are on the test set for MasakhaNER, so the data distribution is similar to the training set, so these results do not directly indicate how well these models generalise.
We do find large variation in transfer results when starting from different seeds (5 different seeds were tested), indicating that the fine-tuning process for transfer might be unstable.
The metrics used were chosen to be consistent with previous work, and to facilitate research. Other metrics may be more appropriate for other purposes.
## Caveats and Recommendations
In general, this model performed worse on the 'date' category compared to others, so if dates are a critical factor, then that might need to be taken into account and addressed, by for example collecting and annotating more data.
## Model Structure
Here are some performance details on this specific model, compared to others we trained.
All of these metrics were calculated on the test set, and the seed was chosen that gave the best overall F1 score. The first three result columns are averaged over all categories, and the latter 4 provide performance broken down by category.
This model can predict the following label for a token ([source](https://huggingface.co/Davlan/xlm-roberta-large-masakhaner)):
Abbreviation|Description
-|-
O|Outside of a named entity
B-DATE |Beginning of a DATE entity right after another DATE entity
I-DATE |DATE entity
B-PER |Beginning of a person’s name right after another person’s name
I-PER |Person’s name
B-ORG |Beginning of an organisation right after another organisation
I-ORG |Organisation
B-LOC |Beginning of a location right after another location
I-LOC |Location
| Model Name | Staring point | Evaluation / Fine-tune Language | F1 | Precision | Recall | F1 (DATE) | F1 (LOC) | F1 (ORG) | F1 (PER) |
| -------------------------------------------------- | -------------------- | -------------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- | -------------- |
| [xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof) (This model) | [wol](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-wolof) | wol | 69.02 | 67.60 | 70.51 | 30.00 | 84.00 | 44.00 | 71.00 |
| [xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-swahili-finetuned-ner-wolof) | [swa](https://huggingface.co/Davlan/xlm-roberta-base-finetuned-swahili) | wol | 69.01 | 73.25 | 65.23 | 27.00 | 85.00 | 52.00 | 67.00 |
| [xlm-roberta-base-finetuned-ner-wolof](https://huggingface.co/mbeukman/xlm-roberta-base-finetuned-ner-wolof) | [base](https://huggingface.co/xlm-roberta-base) | wol | 66.12 | 69.46 | 63.09 | 30.00 | 84.00 | 54.00 | 59.00 |
## Usage
To use this model (or others), you can do the following, just changing the model name ([source](https://huggingface.co/dslim/bert-base-NER)):
```
from transformers import AutoTokenizer, AutoModelForTokenClassification
from transformers import pipeline
model_name = 'mbeukman/xlm-roberta-base-finetuned-wolof-finetuned-ner-wolof'
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForTokenClassification.from_pretrained(model_name)
nlp = pipeline("ner", model=model, tokenizer=tokenizer)
example = "SAFIYETU BÉEY Céy Koronaa !"
ner_results = nlp(example)
print(ner_results)
```
|
michaelrglass/bert-large-uncased-sspt | 440130f131d0b86f4af1baa423b5d1813f20506a | 2021-05-19T23:26:01.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | michaelrglass | null | michaelrglass/bert-large-uncased-sspt | 11 | null | transformers | 11,115 | Entry not found |
monsoon-nlp/ar-seq2seq-gender-decoder | 03e1e51b88556e6cddba1da871345354f8d20b97 | 2021-05-19T23:53:24.000Z | [
"pytorch",
"bert",
"text-generation",
"ar",
"transformers"
]
| text-generation | false | monsoon-nlp | null | monsoon-nlp/ar-seq2seq-gender-decoder | 11 | null | transformers | 11,116 | ---
language: ar
---
# ar-seq2seq-gender (decoder)
This is a seq2seq model (decoder half) to "flip" gender in **first-person** Arabic sentences.
The model can augment your existing Arabic data, or generate counterfactuals
to test a model's decisions (would changing the gender of the subject or speaker change output?).
Intended Examples:
- 'أنا سعيد' <=> 'انا سعيدة'
- 'ركض إلى المتجر' <=> 'ركضت إلى المتجر'
People's names, gender pronouns, gendered words (father, mother), and many other values are currently unchanged by this model. Future versions may be trained on more data.
## Sample Code
```
import torch
from transformers import AutoTokenizer, EncoderDecoderModel
model = EncoderDecoderModel.from_encoder_decoder_pretrained(
"monsoon-nlp/ar-seq2seq-gender-encoder",
"monsoon-nlp/ar-seq2seq-gender-decoder",
min_length=40
)
tokenizer = AutoTokenizer.from_pretrained('monsoon-nlp/ar-seq2seq-gender-decoder') # same as MARBERT original
input_ids = torch.tensor(tokenizer.encode("أنا سعيدة")).unsqueeze(0)
generated = model.generate(input_ids, decoder_start_token_id=model.config.decoder.pad_token_id)
tokenizer.decode(generated.tolist()[0][1 : len(input_ids[0]) - 1])
> 'انا سعيد'
```
https://colab.research.google.com/drive/1S0kE_2WiV82JkqKik_sBW-0TUtzUVmrV?usp=sharing
## Training
I originally developed
<a href="https://github.com/MonsoonNLP/el-la">a gender flip Python script</a>
for Spanish sentences, using
<a href="https://huggingface.co/dccuchile/bert-base-spanish-wwm-uncased">BETO</a>,
and spaCy. More about this project: https://medium.com/ai-in-plain-english/gender-bias-in-spanish-bert-1f4d76780617
The Arabic model encoder and decoder started with weights and vocabulary from
<a href="https://github.com/UBC-NLP/marbert">MARBERT from UBC-NLP</a>,
and was trained on the
<a href="https://camel.abudhabi.nyu.edu/arabic-parallel-gender-corpus/">Arabic Parallel Gender Corpus</a>
from NYU Abu Dhabi. The text is first-person sentences from OpenSubtitles, with parallel
gender-reinflected sentences generated by Arabic speakers.
Training notebook: https://colab.research.google.com/drive/1TuDfnV2gQ-WsDtHkF52jbn699bk6vJZV
## Non-binary gender
This model is useful to generate male and female text samples, but falls
short of capturing gender diversity in the world and in the Arabic
language. This subject is discussed in the bias statement of the
<a href="https://www.aclweb.org/anthology/2020.gebnlp-1.12/">Gender Reinflection paper</a>.
|
moussaKam/frugalscore_small_deberta_bert-score | 2035337fab8e3bbfd6063a0993cfc0902cc42de7 | 2022-01-28T13:19:20.000Z | [
"pytorch",
"bert",
"text-classification",
"arxiv:2110.08559",
"transformers"
]
| text-classification | false | moussaKam | null | moussaKam/frugalscore_small_deberta_bert-score | 11 | null | transformers | 11,117 | # FrugalScore
FrugalScore is an approach to learn a fixed, low cost version of any expensive NLG metric, while retaining most of its original performance
Paper: https://arxiv.org/abs/2110.08559?context=cs
Project github: https://github.com/moussaKam/FrugalScore
The pretrained checkpoints presented in the paper :
| FrugalScore | Student | Teacher | Method |
|----------------------------------------------------|-------------|----------------|------------|
| [moussaKam/frugalscore_tiny_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_bert-score) | BERT-tiny | BERT-Base | BERTScore |
| [moussaKam/frugalscore_small_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_bert-score) | BERT-small | BERT-Base | BERTScore |
| [moussaKam/frugalscore_medium_bert-base_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_bert-score) | BERT-medium | BERT-Base | BERTScore |
| [moussaKam/frugalscore_tiny_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_roberta_bert-score) | BERT-tiny | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_small_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_roberta_bert-score) | BERT-small | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_medium_roberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_roberta_bert-score) | BERT-medium | RoBERTa-Large | BERTScore |
| [moussaKam/frugalscore_tiny_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_tiny_deberta_bert-score) | BERT-tiny | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_small_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_small_deberta_bert-score) | BERT-small | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_medium_deberta_bert-score](https://huggingface.co/moussaKam/frugalscore_medium_deberta_bert-score) | BERT-medium | DeBERTa-XLarge | BERTScore |
| [moussaKam/frugalscore_tiny_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_tiny_bert-base_mover-score) | BERT-tiny | BERT-Base | MoverScore |
| [moussaKam/frugalscore_small_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_small_bert-base_mover-score) | BERT-small | BERT-Base | MoverScore |
| [moussaKam/frugalscore_medium_bert-base_mover-score](https://huggingface.co/moussaKam/frugalscore_medium_bert-base_mover-score) | BERT-medium | BERT-Base | MoverScore | |
mrm8488/ManuERT-for-xqua | ef690580807cb651a7bc5545d52cfb7b9c1b919a | 2021-05-20T00:16:59.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | mrm8488 | null | mrm8488/ManuERT-for-xqua | 11 | null | transformers | 11,118 | Entry not found |
mse30/bart-base-finetuned-xsum | 605fd05ebfb0e6744c9f1844fe33cac1a3948e11 | 2021-10-27T22:13:08.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | mse30 | null | mse30/bart-base-finetuned-xsum | 11 | null | transformers | 11,119 | Entry not found |
mujeensung/albert-base-v2_mnli_bc | f7c73a6b7cac7c8123a1a5354e7d90031c5ee8d6 | 2022-02-13T05:23:40.000Z | [
"pytorch",
"albert",
"text-classification",
"en",
"dataset:glue",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | mujeensung | null | mujeensung/albert-base-v2_mnli_bc | 11 | null | transformers | 11,120 | ---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: albert-base-v2_mnli_bc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: GLUE MNLI
type: glue
args: mnli
metrics:
- name: Accuracy
type: accuracy
value: 0.9398776667163956
---
<!-- 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. -->
# albert-base-v2_mnli_bc
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the GLUE MNLI dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2952
- Accuracy: 0.9399
## 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: 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2159 | 1.0 | 16363 | 0.2268 | 0.9248 |
| 0.1817 | 2.0 | 32726 | 0.2335 | 0.9347 |
| 0.0863 | 3.0 | 49089 | 0.3014 | 0.9401 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.10.1+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
nimanpra/Fine_Tuned_Spiritual | 82a866e1a9c7cb937fcf6c5962a179e2796ab958 | 2021-06-17T16:09:05.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | nimanpra | null | nimanpra/Fine_Tuned_Spiritual | 11 | null | transformers | 11,121 | Entry not found |
paintingpeter/distilbert-base-uncased-finetuned-clinc | edfb6047f8c7da3d6b60e6b1ab95ded1789112d6 | 2022-01-31T21:55:25.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:clinc_oos",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | paintingpeter | null | paintingpeter/distilbert-base-uncased-finetuned-clinc | 11 | null | transformers | 11,122 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9174193548387096
---
<!-- 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7713
- Accuracy: 0.9174
## 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: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2892 | 1.0 | 318 | 3.2831 | 0.7426 |
| 2.6244 | 2.0 | 636 | 1.8739 | 0.8335 |
| 1.5442 | 3.0 | 954 | 1.1525 | 0.8926 |
| 1.0096 | 4.0 | 1272 | 0.8569 | 0.91 |
| 0.793 | 5.0 | 1590 | 0.7713 | 0.9174 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
peterchou/unilm-chinese-base | 4a4995e1a5d5fefe6df73749f89bff8a34a48b12 | 2021-05-20T02:33:23.000Z | [
"pytorch",
"jax",
"bert",
"transformers"
]
| null | false | peterchou | null | peterchou/unilm-chinese-base | 11 | null | transformers | 11,123 | Entry not found |
phueb/BabyBERTa-2 | 6c88fd5c30fb24189728efc3e9bdebf1d593c9c7 | 2022-01-18T14:44:44.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"dataset:CHILDES",
"transformers",
"BabyBERTa",
"autotrain_compatible"
]
| fill-mask | false | phueb | null | phueb/BabyBERTa-2 | 11 | null | transformers | 11,124 | ---
language: en
tags:
- BabyBERTa
datasets:
- CHILDES
widget:
- text: "Look here. What is that <mask> ?"
- text: "Do you like your <mask> ?"
---
## BabyBERTA
### Overview
BabyBERTa is a light-weight version of RoBERTa trained on 5M words of American-English child-directed input.
It is intended for language acquisition research, on a single desktop with a single GPU - no high-performance computing infrastructure needed.
The three provided models are randomly selected from 10 that were trained and reported in the paper.
## Loading the tokenizer
BabyBERTa was trained with `add_prefix_space=True`, so it will not work properly with the tokenizer defaults.
For instance, to load the tokenizer for BabyBERTa-1, load it as follows:
```python
tokenizer = RobertaTokenizerFast.from_pretrained("phueb/BabyBERTa-1",
add_prefix_space=True)
```
### Hyper-Parameters
See the paper for details.
All provided models were trained for 400K steps with a batch size of 16.
Importantly, BabyBERTa never predicts unmasked tokens during training - `unmask_prob` is set to zero.
### Performance
BabyBerta was developed for learning grammatical knowledge from child-directed input.
Its grammatical knowledge was evaluated using the [Zorro](https://github.com/phueb/Zorro) test suite.
The best model achieves an overall accuracy of 80.3,
comparable to RoBERTa-base, which achieves an overall accuracy of 82.6 on the latest version of Zorro (as of October, 2021).
Both values differ slightly from those reported in the [CoNLL 2021 paper](https://aclanthology.org/2021.conll-1.49/).
There are two reasons for this:
1. Performance of RoBERTa-base is slightly larger because the authors previously lower-cased all words in Zorro before evaluation.
Lower-casing of proper nouns is detrimental to RoBERTa-base because RoBERTa-base has likely been trained on proper nouns that are primarily title-cased.
In contrast, because BabyBERTa is not case-sensitive, its performance is not influenced by this change.
2. The latest version of Zorro no longer contains ambiguous content words such as "Spanish" which can be both a noun and an adjective.
this resulted in a small reduction in the performance of BabyBERTa.
Overall Accuracy on Zorro:
| Model Name | Accuracy (holistic scoring) | Accuracy (MLM-scoring) |
|----------------------------------------|------------------------------|------------|
| [BabyBERTa-1][link-BabyBERTa-1] | 80.3 | 79.9 |
| [BabyBERTa-2][link-BabyBERTa-2] | 78.6 | 78.2 |
| [BabyBERTa-3][link-BabyBERTa-3] | 74.5 | 78.1 |
### Additional Information
This model was trained by [Philip Huebner](https://philhuebner.com), currently at the [UIUC Language and Learning Lab](http://www.learninglanguagelab.org).
More info can be found [here](https://github.com/phueb/BabyBERTa).
[link-BabyBERTa-1]: https://huggingface.co/phueb/BabyBERTa-1
[link-BabyBERTa-2]: https://huggingface.co/phueb/BabyBERTa-2
[link-BabyBERTa-3]: https://huggingface.co/phueb/BabyBERTa-3
|
pierreguillou/bert-large-cased-pt-lenerbr | 02425bad92d1762aeaaf050996991893caf13a89 | 2022-01-04T08:52:43.000Z | [
"pytorch",
"bert",
"fill-mask",
"pt",
"dataset:pierreguillou/lener_br_finetuning_language_model",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | pierreguillou | null | pierreguillou/bert-large-cased-pt-lenerbr | 11 | 2 | transformers | 11,125 | ---
language:
- pt
tags:
- generated_from_trainer
datasets:
- pierreguillou/lener_br_finetuning_language_model
model-index:
- name: checkpoints
results:
- task:
name: Fill Mask
type: fill-mask
dataset:
name: pierreguillou/lener_br_finetuning_language_model
type: pierreguillou/lener_br_finetuning_language_model
metrics:
- name: Loss
type: loss
value: 1.127950
widget:
- text: "Com efeito, se tal fosse possível, o Poder [MASK] – que não dispõe de função legislativa – passaria a desempenhar atribuição que lhe é institucionalmente estranha (a de legislador positivo), usurpando, desse modo, no contexto de um sistema de poderes essencialmente limitados, competência que não lhe pertence, com evidente transgressão ao princípio constitucional da separação de poderes."
---
## (BERT large) Language modeling in the legal domain in Portuguese (LeNER-Br)
**bert-large-cased-pt-lenerbr** is a Language Model in the legal domain in Portuguese that was finetuned on 20/12/2021 in Google Colab from the model [BERTimbau large](https://huggingface.co/neuralmind/bert-large-portuguese-cased) on the dataset [LeNER-Br language modeling](https://huggingface.co/datasets/pierreguillou/lener_br_finetuning_language_model) by using a MASK objective.
You can check as well the [version base of this model](https://huggingface.co/pierreguillou/bert-base-cased-pt-lenerbr).
## Widget & APP
You can test this model into the widget of this page.
## Blog post
This language model is used to get a NER model on the Portuguese judicial domain. You can check the fine-tuned NER model at [pierreguillou/ner-bert-large-cased-pt-lenerbr](https://huggingface.co/pierreguillou/ner-bert-large-cased-pt-lenerbr).
All informations and links are in this blog post: [NLP | Modelos e Web App para Reconhecimento de Entidade Nomeada (NER) no domínio jurídico brasileiro](https://medium.com/@pierre_guillou/nlp-modelos-e-web-app-para-reconhecimento-de-entidade-nomeada-ner-no-dom%C3%ADnio-jur%C3%ADdico-b658db55edfb) (29/12/2021)
## Using the model for inference in production
````
# install pytorch: check https://pytorch.org/
# !pip install transformers
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("pierreguillou/bert-large-cased-pt-lenerbr")
model = AutoModelForMaskedLM.from_pretrained("pierreguillou/bert-large-cased-pt-lenerbr")
````
## Training procedure
## Notebook
The notebook of finetuning ([Finetuning_language_model_BERtimbau_LeNER_Br.ipynb](https://github.com/piegu/language-models/blob/master/Finetuning_language_model_BERtimbau_LeNER_Br.ipynb)) is in github.
### Training results
````
Num examples = 3227
Num Epochs = 5
Instantaneous batch size per device = 2
Total train batch size (w. parallel, distributed & accumulation) = 8
Gradient Accumulation steps = 4
Total optimization steps = 2015
Step Training Loss Validation Loss
100 1.616700 1.366015
200 1.452000 1.312473
300 1.431100 1.253055
400 1.407500 1.264705
500 1.301900 1.243277
600 1.317800 1.233684
700 1.319100 1.211826
800 1.303800 1.190818
900 1.262800 1.171898
1000 1.235900 1.146275
1100 1.221900 1.149027
1200 1.226200 1.127950
1300 1.201700 1.172729
1400 1.198200 1.145363
```` |
pmthangk09/bert-base-uncased-esnli | f83f679d8fa2d84b82be44b7cf6672ecfb40e7c6 | 2021-05-20T02:46:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | pmthangk09 | null | pmthangk09/bert-base-uncased-esnli | 11 | null | transformers | 11,126 | Entry not found |
ponteineptique/latin-classical-small | a1d55b6a26d3fb06c9b50c61df2813a39864cb72 | 2020-04-24T16:05:14.000Z | [
"pytorch",
"xlm",
"feature-extraction",
"transformers"
]
| feature-extraction | false | ponteineptique | null | ponteineptique/latin-classical-small | 11 | null | transformers | 11,127 | Entry not found |
proycon/bert-ner-cased-conll2002-nld | a2485b58ec02d0c038ce079ab3e7c85010148672 | 2021-05-20T03:05:15.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | proycon | null | proycon/bert-ner-cased-conll2002-nld | 11 | null | transformers | 11,128 | Entry not found |
proycon/bert-pos-cased-deepfrog-nld | 4515aae9f28dc24164f18926086a031376f0586d | 2021-05-20T03:07:09.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | proycon | null | proycon/bert-pos-cased-deepfrog-nld | 11 | null | transformers | 11,129 | Entry not found |
pysentimiento/robertuito-irony | 7e819c8fe295df4d2fdc591ab1b4b9a49e580b15 | 2021-11-22T16:16:45.000Z | [
"pytorch",
"roberta",
"text-classification",
"arxiv:2106.09462",
"arxiv:2111.09453",
"transformers"
]
| text-classification | false | pysentimiento | null | pysentimiento/robertuito-irony | 11 | 1 | transformers | 11,130 | # Irony detection in Spanish
## robertuito-irony
Repository: [https://github.com/pysentimiento/pysentimiento/](https://github.com/finiteautomata/pysentimiento/)
Model trained with IRosVA 2019 dataset for irony detection. Base model is [RoBERTuito](https://github.com/pysentimiento/robertuito), a RoBERTa model trained in Spanish tweets.
The positive class marks irony, the negative class marks not irony.
## Results
Results for the four tasks evaluated in `pysentimiento`. Results are expressed as Macro F1 scores
| model | emotion | hate_speech | irony | sentiment |
|:--------------|:--------------|:--------------|:--------------|:--------------|
| robertuito | 0.560 ± 0.010 | 0.759 ± 0.007 | 0.739 ± 0.005 | 0.705 ± 0.003 |
| roberta | 0.527 ± 0.015 | 0.741 ± 0.012 | 0.721 ± 0.008 | 0.670 ± 0.006 |
| bertin | 0.524 ± 0.007 | 0.738 ± 0.007 | 0.713 ± 0.012 | 0.666 ± 0.005 |
| beto_uncased | 0.532 ± 0.012 | 0.727 ± 0.016 | 0.701 ± 0.007 | 0.651 ± 0.006 |
| beto_cased | 0.516 ± 0.012 | 0.724 ± 0.012 | 0.705 ± 0.009 | 0.662 ± 0.005 |
| mbert_uncased | 0.493 ± 0.010 | 0.718 ± 0.011 | 0.681 ± 0.010 | 0.617 ± 0.003 |
| biGRU | 0.264 ± 0.007 | 0.592 ± 0.018 | 0.631 ± 0.011 | 0.585 ± 0.011 |
Note that for Hate Speech, these are the results for Semeval 2019, Task 5 Subtask B (HS+TR+AG detection)
## Citation
If you use this model in your research, please cite pysentimiento and RoBERTuito papers:
```
@misc{perez2021pysentimiento,
title={pysentimiento: A Python Toolkit for Sentiment Analysis and SocialNLP tasks},
author={Juan Manuel Pérez and Juan Carlos Giudici and Franco Luque},
year={2021},
eprint={2106.09462},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
@misc{perez2021robertuito,
title={RoBERTuito: a pre-trained language model for social media text in Spanish},
author={Juan Manuel Pérez and Damián A. Furman and Laura Alonso Alemany and Franco Luque},
year={2021},
eprint={2111.09453},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
quincyqiang/tesla2 | 90abad32892c3fb43cbb87bef2c64453d253e47e | 2021-05-20T03:52:00.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | quincyqiang | null | quincyqiang/tesla2 | 11 | null | transformers | 11,131 | Entry not found |
raynardj/pmc-med-bio-mlm-roberta-large | c70f6bfb352375382808dfa500a56af7413a4de1 | 2021-11-28T13:57:31.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | raynardj | null | raynardj/pmc-med-bio-mlm-roberta-large | 11 | 1 | transformers | 11,132 | ---
language:
- en
tags:
- fill-mask
- roberta
widget:
- text: "Polymerase <mask> Reaction"
---
# PMC pretrained RoBERTa large model
Pretrained on PMC fulltext paragraphs on masked language modeling task, it's mostly biology/ medical papers |
rsvp-ai/bertserini-bert-base-cmrc | 7805b05ac989fed6ed7e30f01ab28b0f90f572b8 | 2021-05-19T00:38:49.000Z | [
"pytorch",
"jax",
"bert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | rsvp-ai | null | rsvp-ai/bertserini-bert-base-cmrc | 11 | null | transformers | 11,133 | Entry not found |
sackoh/bert-base-multilingual-cased-nsmc | 768a6323c657f8184deb554ff976b29dbef2ebde | 2021-05-19T00:50:32.000Z | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
]
| text-classification | false | sackoh | null | sackoh/bert-base-multilingual-cased-nsmc | 11 | null | transformers | 11,134 | Entry not found |
sagorsarker/codeswitch-nepeng-lid-lince | 37989eb40861b1c6147cabb9a083c33fdb8761c4 | 2021-05-19T01:11:01.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"ne",
"en",
"dataset:lince",
"transformers",
"codeswitching",
"nepali-english",
"language-identification",
"license:mit",
"autotrain_compatible"
]
| token-classification | false | sagorsarker | null | sagorsarker/codeswitch-nepeng-lid-lince | 11 | null | transformers | 11,135 | ---
language:
- ne
- en
datasets:
- lince
license: mit
tags:
- codeswitching
- nepali-english
- language-identification
---
# codeswitch-nepeng-lid-lince
This is a pretrained model for **language identification** of `nepali-english` code-mixed data used from [LinCE](https://ritual.uh.edu/lince/home).
This model is trained for this below repository.
[https://github.com/sagorbrur/codeswitch](https://github.com/sagorbrur/codeswitch)
To install codeswitch:
```
pip install codeswitch
```
## Identify Language
* **Method-1**
```py
from transformers import AutoTokenizer, AutoModelForTokenClassification, pipeline
tokenizer = AutoTokenizer.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince")
model = AutoModelForTokenClassification.from_pretrained("sagorsarker/codeswitch-nepeng-lid-lince")
lid_model = pipeline('ner', model=model, tokenizer=tokenizer)
lid_model("put any nepali english code-mixed sentence")
```
* **Method-2**
```py
from codeswitch.codeswitch import LanguageIdentification
lid = LanguageIdentification('nep-eng')
text = "" # your code-mixed sentence
result = lid.identify(text)
print(result)
```
|
samitizerxu/wav2vec2-xls-r-300m-fr | bbcb338de18d0bfa3cb111078e182a8df7c54a36 | 2022-03-23T18:33:04.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"fr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | samitizerxu | null | samitizerxu/wav2vec2-xls-r-300m-fr | 11 | null | transformers | 11,136 | ---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- common_voice
- fr
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
model-index:
- name: wav2vec2-cls-r-300m-fr
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 56.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: fr
metrics:
- name: Test WER
type: wer
value: 58.22
---
<!-- 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-cls-r-300m-fr
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the COMMON_VOICE - FR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6521
- Wer: 0.4330
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.6773 | 0.8 | 500 | 1.3907 | 0.9864 |
| 0.9526 | 1.6 | 1000 | 0.7760 | 0.6448 |
| 0.6418 | 2.4 | 1500 | 0.7605 | 0.6194 |
| 0.5028 | 3.2 | 2000 | 0.6516 | 0.5322 |
| 0.4133 | 4.0 | 2500 | 0.6303 | 0.5097 |
| 0.3285 | 4.8 | 3000 | 0.6422 | 0.5062 |
| 0.2764 | 5.6 | 3500 | 0.5936 | 0.4748 |
| 0.2361 | 6.4 | 4000 | 0.6486 | 0.4683 |
| 0.2049 | 7.2 | 4500 | 0.6321 | 0.4532 |
| 0.176 | 8.0 | 5000 | 0.6230 | 0.4482 |
| 0.1393 | 8.8 | 5500 | 0.6595 | 0.4403 |
| 0.1141 | 9.6 | 6000 | 0.6552 | 0.4348 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2.dev0
- Tokenizers 0.11.0
|
seduerr/paraphrase | 0ff7d8bd97071e8f5a3d25697984854f343b3598 | 2021-06-23T14:17:58.000Z | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | seduerr | null | seduerr/paraphrase | 11 | null | transformers | 11,137 | Entry not found |
seongju/klue-mrc-roberta-base | 967a1cc8fe384411c22a166cc6c659c16add16d6 | 2021-08-09T08:06:23.000Z | [
"pytorch",
"xlm-roberta",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | seongju | null | seongju/klue-mrc-roberta-base | 11 | null | transformers | 11,138 | Entry not found |
sergiyvl/ParaPhraserPlus_1epoch | 7b7869921bdeafa33470c0fc4986f17517e54055 | 2021-05-20T05:30:51.000Z | [
"pytorch",
"jax",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | sergiyvl | null | sergiyvl/ParaPhraserPlus_1epoch | 11 | null | transformers | 11,139 | Entry not found |
sismetanin/mbart_ru_sum_gazeta-ru-sentiment-krnd | f48dcde73805a046fd10292bc488907efda89b31 | 2021-02-21T13:19:50.000Z | [
"pytorch",
"mbart",
"text-classification",
"transformers"
]
| text-classification | false | sismetanin | null | sismetanin/mbart_ru_sum_gazeta-ru-sentiment-krnd | 11 | null | transformers | 11,140 | Entry not found |
skylord/wav2vec2-large-xlsr-greek-2 | 00ac229e00fa3743119d3f6152ac3f9247984f62 | 2021-03-31T09:42:31.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"el",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | skylord | null | skylord/wav2vec2-large-xlsr-greek-2 | 11 | null | transformers | 11,141 | ---
language: el
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: Greek XLSR Wav2Vec2 Large 53
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice el
type: common_voice
args: el
metrics:
- name: Test WER
type: wer
value: 45.048955
---
# Wav2Vec2-Large-XLSR-53-Greek
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on Greek using the [Common Voice](https://huggingface.co/datasets/common_voice),
The Greek CV data has a majority of male voices. To balance it synthesised female voices were created using the approach discussed here [slack](https://huggingface.slack.com/archives/C01QZ90Q83Z/p1616741140114800)
The text from the common-voice dataset was used to synthesize vocies of female speakers using [Googe's TTS Standard Voice model](https://cloud.google.com/text-to-speech)
Fine-tuned on facebook/wav2vec2-large-xlsr-53 using Greek CommonVoice :: 5 epochs >> 56.25% WER
Resuming from checkpoints trained for another 15 epochs >> 34.00%
Added synthesised female voices trained for 12 epochs >> 34.00% (no change)
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "el", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1")
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Greek test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "el", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("skylord/greek_lsr_1")
model = Wav2Vec2ForCTC.from_pretrained("skylord/greek_lsr_1")
model.to("cuda")
chars_to_ignore_regex = '[\\\\\\\\,\\\\\\\\?\\\\\\\\.\\\\\\\\!\\\\\\\\-\\\\\\\\;\\\\\\\\:\\\\\\\\"\\\\\\\\“]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 45.048955 %
## Training
The Common Voice `train`, `validation`, datasets were used for training as well as
The script used for training can be found [here](...) # TODO: fill in a link to your training script here. If you trained your model in a colab, simply fill in the link here. If you trained the model locally, it would be great if you could upload the training script on github and paste the link here.
|
sshleifer/student_enro_sum_12_1 | d8ee5fac300d1df4c5adc0ebecd8056909dbe7e0 | 2020-07-18T20:16:27.000Z | [
"pytorch",
"bart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | sshleifer | null | sshleifer/student_enro_sum_12_1 | 11 | null | transformers | 11,142 | Entry not found |
sshleifer/student_pegasus_cnn_12_2 | d8ce6f11b4a1174c2d0edf3b979a9a1f5232f4c6 | 2020-10-02T03:49:28.000Z | [
"pytorch",
"pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | sshleifer | null | sshleifer/student_pegasus_cnn_12_2 | 11 | null | transformers | 11,143 | Entry not found |
stanford-crfm/durin-gpt2-medium-x343 | fe7e487b4a5109d1190a57b28e4419f3ab972468 | 2022-06-20T10:58:10.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | stanford-crfm | null | stanford-crfm/durin-gpt2-medium-x343 | 11 | null | transformers | 11,144 | Entry not found |
stanford-crfm/expanse-gpt2-small-x777 | 1a3988b4f158d80cf8f72df885ea258783317434 | 2022-06-20T09:32:56.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | stanford-crfm | null | stanford-crfm/expanse-gpt2-small-x777 | 11 | null | transformers | 11,145 | Entry not found |
stefan-it/wav2vec2-large-xlsr-53-basque | 19cc35f777b8105c807a94b4c0a68ffe3009b18c | 2021-03-29T15:54:40.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"eu",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | stefan-it | null | stefan-it/wav2vec2-large-xlsr-53-basque | 11 | null | transformers | 11,146 | ---
language: eu
datasets:
- common_voice
tags:
- audio
- automatic-speech-recognition
- speech
- xlsr-fine-tuning-week
license: apache-2.0
model-index:
- name: XLSR Wav2Vec2 Basque Stefan Schweter
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice eu
type: common_voice
args: eu
metrics:
- name: Test WER
type: wer
value: 18.272625
---
# Wav2Vec2-Large-XLSR-53-Basque
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) in Basque using the [Common Voice](https://huggingface.co/datasets/common_voice).
When using this model, make sure that your speech input is sampled at 16kHz.
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "eu", split="test[:2%]").
processor = Wav2Vec2Processor.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model = Wav2Vec2ForCTC.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
inputs = processor(test_dataset["speech"][:2], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
print("Reference:", test_dataset["sentence"][:2])
```
## Evaluation
The model can be evaluated as follows on the Basque test data of Common Voice.
```python
import torch
import torchaudio
from datasets import load_dataset, load_metric
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
import re
test_dataset = load_dataset("common_voice", "eu", split="test")
wer = load_metric("wer")
processor = Wav2Vec2Processor.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model = Wav2Vec2ForCTC.from_pretrained("stefan-it/wav2vec2-large-xlsr-53-basque")
model.to("cuda")
chars_to_ignore_regex = '[\\\\,\\\\?\\\\.\\\\!\\\\-\\\\;\\\\:\\\\"\\\\“\\\\%\\\\‘\\\\”\\\\�]'
resampler = torchaudio.transforms.Resample(48_000, 16_000)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def speech_file_to_array_fn(batch):
batch["sentence"] = re.sub(chars_to_ignore_regex, '', batch["sentence"]).lower()
speech_array, sampling_rate = torchaudio.load(batch["path"])
batch["speech"] = resampler(speech_array).squeeze().numpy()
return batch
test_dataset = test_dataset.map(speech_file_to_array_fn)
# Preprocessing the datasets.
# We need to read the aduio files as arrays
def evaluate(batch):
inputs = processor(batch["speech"], sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
logits = model(inputs.input_values.to("cuda"), attention_mask=inputs.attention_mask.to("cuda")).logits
pred_ids = torch.argmax(logits, dim=-1)
batch["pred_strings"] = processor.batch_decode(pred_ids)
return batch
result = test_dataset.map(evaluate, batched=True, batch_size=8)
print("WER: {:2f}".format(100 * wer.compute(predictions=result["pred_strings"], references=result["sentence"])))
```
**Test Result**: 18.272625%
## Training
The Common Voice `train`, `validation` datasets were used for training.
The script used for training can be found here, hopefully very soon!
## Acknowledgements
Many thanks to the [OVH team](https://www.ovhcloud.com) for providing access to a V-100 instance. Without their help,
fine-tuning would not be possible!
I would also thank [Manuel Romero](https://github.com/mrm8488) (mrm8488) for helping with the fine-tuning script! |
sukhendrasingh/finetuning-sentiment-model-3000-samples | eba498245dd43db7e5e6812e0180a6b0bbb08c83 | 2022-02-07T17:20:03.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | sukhendrasingh | null | sukhendrasingh/finetuning-sentiment-model-3000-samples | 11 | null | transformers | 11,147 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.8733333333333333
- name: F1
type: f1
value: 0.879746835443038
---
<!-- 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: 0.3323
- Accuracy: 0.8733
- F1: 0.8797
## 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 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.0
|
surajp/SanBERTa | 43ee7b437c82b84e0405a3d194a676dd6308dee4 | 2021-05-20T22:03:36.000Z | [
"pytorch",
"jax",
"roberta",
"fill-mask",
"sa",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | surajp | null | surajp/SanBERTa | 11 | null | transformers | 11,148 | ---
language: sa
---
# RoBERTa trained on Sanskrit (SanBERTa)
**Mode size** (after training): **340MB**
### Dataset:
[Wikipedia articles](https://www.kaggle.com/disisbig/sanskrit-wikipedia-articles) (used in [iNLTK](https://github.com/goru001/nlp-for-sanskrit)).
It contains evaluation set.
[Sanskrit scraps from CLTK](http://cltk.org/)
### Configuration
| Parameter | Value |
|---|---|
| `num_attention_heads` | 12 |
| `num_hidden_layers` | 6 |
| `hidden_size` | 768 |
| `vocab_size` | 29407 |
### Training :
- On TPU
- For language modelling
- Iteratively increasing `--block_size` from 128 to 256 over epochs
### Evaluation
|Metric| # Value |
|---|---|
|Perplexity (`block_size=256`)|4.04|
## Example of usage:
### For Embeddings
```
tokenizer = AutoTokenizer.from_pretrained("surajp/SanBERTa")
model = RobertaModel.from_pretrained("surajp/SanBERTa")
op = tokenizer.encode("इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।", return_tensors="pt")
ps = model(op)
ps[0].shape
```
```
'''
Output:
--------
torch.Size([1, 47, 768])
```
### For \<mask\> Prediction
```
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model="surajp/SanBERTa",
tokenizer="surajp/SanBERTa"
)
## इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।
fill_mask("इयं भाषा न केवल<mask> भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।")
ps = model(torch.tensor(enc).unsqueeze(1))
print(ps[0].shape)
```
```
'''
Output:
--------
[{'score': 0.7516744136810303,
'sequence': '<s> इयं भाषा न केवलं भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 280,
'token_str': 'à¤Ĥ'},
{'score': 0.06230105459690094,
'sequence': '<s> इयं भाषा न केवली भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 289,
'token_str': 'à¥Ģ'},
{'score': 0.055410224944353104,
'sequence': '<s> इयं भाषा न केवला भारतस्य अपि तु विश्वस्य प्राचीनतमा भाषा इति मन्यते।</s>',
'token': 265,
'token_str': 'ा'},
...]
```
### It works!! 🎉 🎉 🎉
> Created by [Suraj Parmar/@parmarsuraj99](https://twitter.com/parmarsuraj99) | [LinkedIn](https://www.linkedin.com/in/parmarsuraj99/)
> Made with <span style="color: #e25555;">♥</span> in India
|
techiaith/wav2vec2-xlsr-ft-cy | 69df7e44646babb4b3edc4abd7ad8885e8d4d5c0 | 2022-06-15T12:37:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"cy",
"dataset:common_voice",
"transformers",
"audio",
"hf-asr-leaderboard",
"ken-lm",
"robust-speech-event",
"speech",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | techiaith | null | techiaith/wav2vec2-xlsr-ft-cy | 11 | 3 | transformers | 11,149 | ---
language: cy
datasets:
- common_voice
metrics:
- wer
tags:
- audio
- automatic-speech-recognition
- hf-asr-leaderboard
- ken-lm
- robust-speech-event
- speech
license: apache-2.0
model-index:
- name: wav2vec2-xlsr-ft-cy with KenLM language model (by Bangor University)
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice cy
type: common_voice
args: cy
metrics:
- name: Test WER
type: wer
value: 13.74%
---
# wav2vec2-xlsr-ft-cy
Fine-tuned [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the [Welsh Common Voice version 8 dataset](https://huggingface.co/datasets/common_voice).
Source code and scripts for training acoustic and KenLM language models, as well as examples of inference in transcribing or a self-hosted API service, can be found at [https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy](https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy).
## Usage
The wav2vec2-xlsr-ft-cy (acoustic) model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
import librosa
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy")
model = Wav2Vec2ForCTC.from_pretrained("techiaith/wav2vec2-xlsr-ft-cy")
audio, rate = librosa.load(audio_file, sr=16000)
inputs = processor(audio, sampling_rate=16_000, return_tensors="pt", padding=True)
with torch.no_grad():
tlogits = model(inputs.input_values, attention_mask=inputs.attention_mask).logits
# greedy decoding
predicted_ids = torch.argmax(logits, dim=-1)
print("Prediction:", processor.batch_decode(predicted_ids))
```
## Using the Language Model
See https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/releases/tag/21.08 for more details and
examples of a KenLM usage with the Parlance PyTorch CTC decode bindings library: [https://github.com/parlance/ctcdecode](https://github.com/parlance/ctcdecode)
## Evaluation
According to the Welsh Common Voice version 9 test set, the WER of techiaith/wav2vec2-xlsr-ft-cy standalone is **23.09%**
When assisted by the KenLM language model the same test produces a WER of **13.74%*
See: https://github.com/techiaith/docker-wav2vec2-xlsr-ft-cy/blob/main/train/python/evaluate.py
|
timtarusov/distilbert-base-uncased-finetuned-emotion | 959f361640c433397f05357a6c78d046e7b78e36 | 2022-02-13T08:48:03.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | timtarusov | null | timtarusov/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,150 | ---
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: 0.921
- name: F1
type: f1
value: 0.9211076096482195
---
<!-- 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: 0.2274
- Accuracy: 0.921
- F1: 0.9211
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8308 | 1.0 | 250 | 0.3319 | 0.8955 | 0.8897 |
| 0.2516 | 2.0 | 500 | 0.2274 | 0.921 | 0.9211 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
tomascufaro/xls-r-es-test | 4acc7c880e7016dc92e72e46ac93963a25525cd7 | 2022-03-24T11:58:49.000Z | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"es",
"dataset:mozilla-foundation/common_voice_8_0",
"transformers",
"mozilla-foundation/common_voice_8_0",
"generated_from_trainer",
"robust-speech-event",
"hf-asr-leaderboard",
"license:apache-2.0",
"model-index"
]
| automatic-speech-recognition | false | tomascufaro | null | tomascufaro/xls-r-es-test | 11 | null | transformers | 11,151 | ---
language:
- es
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_8_0
- generated_from_trainer
- es
- robust-speech-event
- hf-asr-leaderboard
datasets:
- mozilla-foundation/common_voice_8_0
model-index:
- name: xls-r-es-test
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 8.0
type: mozilla-foundation/common_voice_8_0
args: es
metrics:
- name: Test WER
type: wer
value: 12.62
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: es
metrics:
- name: Test WER
type: wer
value: 36.08
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Test Data
type: speech-recognition-community-v2/eval_data
args: es
metrics:
- name: Test WER
type: wer
value: 39.19
---
<!-- 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. -->
# xls-r-es-test
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - ES dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1304
- WER: 0.1261
- CER: 0.035
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 7.5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- 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: 2000
- num_epochs: 10.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 2.9613 | 0.07 | 500 | 2.9647 | 1.0 |
| 2.604 | 0.14 | 1000 | 1.8300 | 0.9562 |
| 1.177 | 0.21 | 1500 | 0.3652 | 0.3077 |
| 1.0745 | 0.28 | 2000 | 0.2707 | 0.2504 |
| 1.0103 | 0.35 | 2500 | 0.2338 | 0.2157 |
| 0.9858 | 0.42 | 3000 | 0.2321 | 0.2129 |
| 0.974 | 0.49 | 3500 | 0.2164 | 0.2031 |
| 0.9699 | 0.56 | 4000 | 0.2078 | 0.1970 |
| 0.9513 | 0.63 | 4500 | 0.2173 | 0.2139 |
| 0.9657 | 0.7 | 5000 | 0.2050 | 0.1979 |
| 0.9484 | 0.77 | 5500 | 0.2008 | 0.1919 |
| 0.9317 | 0.84 | 6000 | 0.2012 | 0.1911 |
| 0.9366 | 0.91 | 6500 | 0.2024 | 0.1976 |
| 0.9242 | 0.98 | 7000 | 0.2062 | 0.2028 |
| 0.9138 | 1.05 | 7500 | 0.1924 | 0.1863 |
| 0.921 | 1.12 | 8000 | 0.1935 | 0.1836 |
| 0.9117 | 1.19 | 8500 | 0.1887 | 0.1815 |
| 0.9064 | 1.26 | 9000 | 0.1909 | 0.1839 |
| 0.9118 | 1.32 | 9500 | 0.1869 | 0.1830 |
| 0.9121 | 1.39 | 10000 | 0.1863 | 0.1802 |
| 0.9048 | 1.46 | 10500 | 0.1845 | 0.1791 |
| 0.8955 | 1.53 | 11000 | 0.1863 | 0.1774 |
| 0.8947 | 1.6 | 11500 | 0.1907 | 0.1814 |
| 0.9073 | 1.67 | 12000 | 0.1892 | 0.1853 |
| 0.8927 | 1.74 | 12500 | 0.1821 | 0.1750 |
| 0.8732 | 1.81 | 13000 | 0.1815 | 0.1768 |
| 0.8761 | 1.88 | 13500 | 0.1822 | 0.1749 |
| 0.8751 | 1.95 | 14000 | 0.1789 | 0.1715 |
| 0.8889 | 2.02 | 14500 | 0.1819 | 0.1791 |
| 0.8864 | 2.09 | 15000 | 0.1826 | 0.1794 |
| 0.886 | 2.16 | 15500 | 0.1788 | 0.1776 |
| 0.8915 | 2.23 | 16000 | 0.1756 | 0.1719 |
| 0.8689 | 2.3 | 16500 | 0.1769 | 0.1711 |
| 0.879 | 2.37 | 17000 | 0.1777 | 0.1739 |
| 0.8692 | 2.44 | 17500 | 0.1765 | 0.1705 |
| 0.8504 | 2.51 | 18000 | 0.1699 | 0.1652 |
| 0.8728 | 2.58 | 18500 | 0.1705 | 0.1694 |
| 0.8523 | 2.65 | 19000 | 0.1674 | 0.1645 |
| 0.8513 | 2.72 | 19500 | 0.1661 | 0.1611 |
| 0.8498 | 2.79 | 20000 | 0.1660 | 0.1631 |
| 0.8432 | 2.86 | 20500 | 0.1636 | 0.1610 |
| 0.8492 | 2.93 | 21000 | 0.1708 | 0.1688 |
| 0.8561 | 3.0 | 21500 | 0.1663 | 0.1604 |
| 0.842 | 3.07 | 22000 | 0.1690 | 0.1625 |
| 0.857 | 3.14 | 22500 | 0.1642 | 0.1605 |
| 0.8518 | 3.21 | 23000 | 0.1626 | 0.1585 |
| 0.8506 | 3.28 | 23500 | 0.1651 | 0.1605 |
| 0.8394 | 3.35 | 24000 | 0.1647 | 0.1585 |
| 0.8431 | 3.42 | 24500 | 0.1632 | 0.1573 |
| 0.8566 | 3.49 | 25000 | 0.1614 | 0.1550 |
| 0.8534 | 3.56 | 25500 | 0.1645 | 0.1589 |
| 0.8386 | 3.63 | 26000 | 0.1632 | 0.1582 |
| 0.8357 | 3.7 | 26500 | 0.1631 | 0.1556 |
| 0.8299 | 3.77 | 27000 | 0.1612 | 0.1550 |
| 0.8421 | 3.84 | 27500 | 0.1602 | 0.1552 |
| 0.8375 | 3.91 | 28000 | 0.1592 | 0.1537 |
| 0.8328 | 3.97 | 28500 | 0.1587 | 0.1537 |
| 0.8155 | 4.04 | 29000 | 0.1587 | 0.1520 |
| 0.8335 | 4.11 | 29500 | 0.1624 | 0.1556 |
| 0.8138 | 4.18 | 30000 | 0.1581 | 0.1547 |
| 0.8195 | 4.25 | 30500 | 0.1560 | 0.1507 |
| 0.8092 | 4.32 | 31000 | 0.1561 | 0.1534 |
| 0.8191 | 4.39 | 31500 | 0.1549 | 0.1493 |
| 0.8008 | 4.46 | 32000 | 0.1540 | 0.1493 |
| 0.8138 | 4.53 | 32500 | 0.1544 | 0.1493 |
| 0.8173 | 4.6 | 33000 | 0.1553 | 0.1511 |
| 0.8081 | 4.67 | 33500 | 0.1541 | 0.1484 |
| 0.8192 | 4.74 | 34000 | 0.1560 | 0.1506 |
| 0.8068 | 4.81 | 34500 | 0.1540 | 0.1503 |
| 0.8105 | 4.88 | 35000 | 0.1529 | 0.1483 |
| 0.7976 | 4.95 | 35500 | 0.1507 | 0.1451 |
| 0.8143 | 5.02 | 36000 | 0.1505 | 0.1462 |
| 0.8053 | 5.09 | 36500 | 0.1517 | 0.1476 |
| 0.785 | 5.16 | 37000 | 0.1526 | 0.1478 |
| 0.7936 | 5.23 | 37500 | 0.1489 | 0.1421 |
| 0.807 | 5.3 | 38000 | 0.1483 | 0.1420 |
| 0.8092 | 5.37 | 38500 | 0.1481 | 0.1435 |
| 0.793 | 5.44 | 39000 | 0.1503 | 0.1438 |
| 0.814 | 5.51 | 39500 | 0.1495 | 0.1480 |
| 0.807 | 5.58 | 40000 | 0.1472 | 0.1424 |
| 0.7913 | 5.65 | 40500 | 0.1471 | 0.1422 |
| 0.7844 | 5.72 | 41000 | 0.1473 | 0.1422 |
| 0.7888 | 5.79 | 41500 | 0.1445 | 0.1385 |
| 0.7806 | 5.86 | 42000 | 0.1435 | 0.1394 |
| 0.7773 | 5.93 | 42500 | 0.1461 | 0.1424 |
| 0.786 | 6.0 | 43000 | 0.1450 | 0.1413 |
| 0.7784 | 6.07 | 43500 | 0.1463 | 0.1424 |
| 0.7937 | 6.14 | 44000 | 0.1438 | 0.1386 |
| 0.7738 | 6.21 | 44500 | 0.1437 | 0.1383 |
| 0.7728 | 6.28 | 45000 | 0.1424 | 0.1371 |
| 0.7681 | 6.35 | 45500 | 0.1416 | 0.1376 |
| 0.776 | 6.42 | 46000 | 0.1415 | 0.1380 |
| 0.7773 | 6.49 | 46500 | 0.1416 | 0.1371 |
| 0.7692 | 6.56 | 47000 | 0.1398 | 0.1345 |
| 0.7642 | 6.62 | 47500 | 0.1381 | 0.1341 |
| 0.7692 | 6.69 | 48000 | 0.1392 | 0.1334 |
| 0.7667 | 6.76 | 48500 | 0.1392 | 0.1348 |
| 0.7712 | 6.83 | 49000 | 0.1398 | 0.1333 |
| 0.7628 | 6.9 | 49500 | 0.1392 | 0.1344 |
| 0.7622 | 6.97 | 50000 | 0.1377 | 0.1329 |
| 0.7639 | 7.04 | 50500 | 0.1361 | 0.1316 |
| 0.742 | 7.11 | 51000 | 0.1376 | 0.1327 |
| 0.7526 | 7.18 | 51500 | 0.1387 | 0.1342 |
| 0.7606 | 7.25 | 52000 | 0.1363 | 0.1316 |
| 0.7626 | 7.32 | 52500 | 0.1365 | 0.1313 |
| 0.752 | 7.39 | 53000 | 0.1354 | 0.1309 |
| 0.7562 | 7.46 | 53500 | 0.1362 | 0.1312 |
| 0.7557 | 7.53 | 54000 | 0.1358 | 0.1325 |
| 0.7588 | 7.6 | 54500 | 0.1343 | 0.1311 |
| 0.7485 | 7.67 | 55000 | 0.1346 | 0.1301 |
| 0.7466 | 7.74 | 55500 | 0.1354 | 0.1314 |
| 0.7558 | 7.81 | 56000 | 0.1359 | 0.1325 |
| 0.7578 | 7.88 | 56500 | 0.1363 | 0.1334 |
| 0.7411 | 7.95 | 57000 | 0.1346 | 0.1301 |
| 0.7478 | 8.02 | 57500 | 0.1355 | 0.1305 |
| 0.7451 | 8.09 | 58000 | 0.1349 | 0.1302 |
| 0.7383 | 8.16 | 58500 | 0.1349 | 0.1294 |
| 0.7482 | 8.23 | 59000 | 0.1341 | 0.1293 |
| 0.742 | 8.3 | 59500 | 0.1338 | 0.1296 |
| 0.7343 | 8.37 | 60000 | 0.1348 | 0.1307 |
| 0.7385 | 8.44 | 60500 | 0.1324 | 0.1282 |
| 0.7567 | 8.51 | 61000 | 0.1334 | 0.1281 |
| 0.7342 | 8.58 | 61500 | 0.1338 | 0.1289 |
| 0.7401 | 8.65 | 62000 | 0.1331 | 0.1285 |
| 0.7362 | 8.72 | 62500 | 0.1329 | 0.1283 |
| 0.7241 | 8.79 | 63000 | 0.1323 | 0.1277 |
| 0.7244 | 8.86 | 63500 | 0.1317 | 0.1269 |
| 0.7274 | 8.93 | 64000 | 0.1308 | 0.1260 |
| 0.7411 | 9.0 | 64500 | 0.1309 | 0.1256 |
| 0.7255 | 9.07 | 65000 | 0.1316 | 0.1265 |
| 0.7406 | 9.14 | 65500 | 0.1315 | 0.1270 |
| 0.7418 | 9.21 | 66000 | 0.1315 | 0.1269 |
| 0.7301 | 9.27 | 66500 | 0.1315 | 0.1273 |
| 0.7248 | 9.34 | 67000 | 0.1323 | 0.1274 |
| 0.7423 | 9.41 | 67500 | 0.1309 | 0.1267 |
| 0.7152 | 9.48 | 68000 | 0.1312 | 0.1271 |
| 0.7295 | 9.55 | 68500 | 0.1306 | 0.1262 |
| 0.7231 | 9.62 | 69000 | 0.1308 | 0.1263 |
| 0.7344 | 9.69 | 69500 | 0.1313 | 0.1267 |
| 0.7264 | 9.76 | 70000 | 0.1305 | 0.1263 |
| 0.7309 | 9.83 | 70500 | 0.1303 | 0.1262 |
| 0.73 | 9.9 | 71000 | 0.1303 | 0.1261 |
| 0.7353 | 9.97 | 71500 | 0.1304 | 0.1260 |
### Framework versions
- Transformers 4.17.0.dev0
- Pytorch 1.10.2+cu102
- Datasets 1.18.3
- Tokenizers 0.11.0
|
tr3cks/bert-ner-es | 0c770676060b3cfb81b5a5a3a90c236c1ebc3b4b | 2021-05-20T08:04:44.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | tr3cks | null | tr3cks/bert-ner-es | 11 | null | transformers | 11,152 | Entry not found |
uclanlp/plbart-javascript-en_XX | cd6ccba4322ae1156f46cd66a4cd347396e5f0c6 | 2021-11-09T17:09:03.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | uclanlp | null | uclanlp/plbart-javascript-en_XX | 11 | null | transformers | 11,153 | Entry not found |
uclanlp/plbart-single_task-interpreted-summarization | 8b8b4c7eceedacbce73ce35db33581297f3cf6d0 | 2022-03-02T07:18:17.000Z | [
"pytorch",
"plbart",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | uclanlp | null | uclanlp/plbart-single_task-interpreted-summarization | 11 | null | transformers | 11,154 | Entry not found |
unicamp-dl/ptt5-small-t5-vocab | f9b94c40e21ae3437745254f09cd05c22d9f383c | 2021-06-23T14:35:18.000Z | [
"pytorch",
"tf",
"jax",
"t5",
"text2text-generation",
"pt",
"dataset:brWaC",
"transformers",
"tensorflow",
"pt-br",
"license:mit",
"autotrain_compatible"
]
| text2text-generation | false | unicamp-dl | null | unicamp-dl/ptt5-small-t5-vocab | 11 | null | transformers | 11,155 | ---
language: pt
license: mit
tags:
- t5
- pytorch
- tensorflow
- pt
- pt-br
datasets:
- brWaC
widget:
- text: "Texto de exemplo em português"
inference: false
---
# Portuguese T5 (aka "PTT5")
## Introduction
PTT5 is a T5 model pretrained in the BrWac corpus, a large collection of web pages in Portuguese, improving T5's performance on Portuguese sentence similarity and entailment tasks. It's available in three sizes (small, base and large) and two vocabularies (Google's T5 original and ours, trained on Portuguese Wikipedia).
For further information or requests, please go to [PTT5 repository](https://github.com/unicamp-dl/PTT5).
## Available models
| Model | Size | #Params | Vocabulary |
| :-: | :-: | :-: | :-: |
| [unicamp-dl/ptt5-small-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-small-t5-vocab) | small | 60M | Google's T5 |
| [unicamp-dl/ptt5-base-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-base-t5-vocab) | base | 220M | Google's T5 |
| [unicamp-dl/ptt5-large-t5-vocab](https://huggingface.co/unicamp-dl/ptt5-large-t5-vocab) | large | 740M | Google's T5 |
| [unicamp-dl/ptt5-small-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-small-portuguese-vocab) | small | 60M | Portuguese |
| **[unicamp-dl/ptt5-base-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-base-portuguese-vocab)** **(Recommended)** | **base** | **220M** | **Portuguese** |
| [unicamp-dl/ptt5-large-portuguese-vocab](https://huggingface.co/unicamp-dl/ptt5-large-portuguese-vocab) | large | 740M | Portuguese |
## Usage
```python
# Tokenizer
from transformers import T5Tokenizer
# PyTorch (bare model, baremodel + language modeling head)
from transformers import T5Model, T5ForConditionalGeneration
# Tensorflow (bare model, baremodel + language modeling head)
from transformers import TFT5Model, TFT5ForConditionalGeneration
model_name = 'unicamp-dl/ptt5-base-portuguese-vocab'
tokenizer = T5Tokenizer.from_pretrained(model_name)
# PyTorch
model_pt = T5ForConditionalGeneration.from_pretrained(model_name)
# TensorFlow
model_tf = TFT5ForConditionalGeneration.from_pretrained(model_name)
```
# Citation
If you use PTT5, please cite:
@article{ptt5_2020,
title={PTT5: Pretraining and validating the T5 model on Brazilian Portuguese data},
author={Carmo, Diedre and Piau, Marcos and Campiotti, Israel and Nogueira, Rodrigo and Lotufo, Roberto},
journal={arXiv preprint arXiv:2008.09144},
year={2020}
}
|
usami/distilbert-base-uncased-finetuned-squad | 7789301904b54425cc675ba9d993641aff5057b9 | 2021-11-22T05:00:14.000Z | [
"pytorch",
"distilbert",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | usami | null | usami/distilbert-base-uncased-finetuned-squad | 11 | null | transformers | 11,156 | Entry not found |
vasudevgupta/bigbird-pegasus-large-arxiv | 6c6fe12337ca6163a7c037a98c5b9014aa0ffb8b | 2021-05-04T11:12:15.000Z | [
"pytorch",
"bigbird_pegasus",
"text2text-generation",
"transformers",
"autotrain_compatible"
]
| text2text-generation | false | vasudevgupta | null | vasudevgupta/bigbird-pegasus-large-arxiv | 11 | null | transformers | 11,157 | Moved here: https://huggingface.co/google/bigbird-pegasus-large-arxiv |
vera-pro/bert-mention-en | 7f98a5ac4cabea8afd6e7b3bc99279d8f15a0ffe | 2021-05-20T08:54:13.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | vera-pro | null | vera-pro/bert-mention-en | 11 | null | transformers | 11,158 | Entry not found |
vera-pro/bert-mention-fr | 44540bb9306318fb86dbcda06a88a8d6f5bd3b1a | 2021-05-20T08:55:28.000Z | [
"pytorch",
"jax",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | vera-pro | null | vera-pro/bert-mention-fr | 11 | null | transformers | 11,159 | Entry not found |
vesteinn/XLMR-ENIS-finetuned-ner | 2bc9cc721e44344f1453be39fada67e095160755 | 2021-09-28T20:43:19.000Z | [
"pytorch",
"tensorboard",
"xlm-roberta",
"token-classification",
"en",
"is",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:agpl-3.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | vesteinn | null | vesteinn/XLMR-ENIS-finetuned-ner | 11 | 1 | transformers | 11,160 | ---
license: agpl-3.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
language:
- en
- is
model-index:
- name: XLMR-ENIS-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9398313331170938
- name: Recall
type: recall
value: 0.9517943664285128
- name: F1
type: f1
value: 0.9457750214207026
- name: Accuracy
type: accuracy
value: 0.9853686150987764
---
<!-- 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. -->
# XLMR-ENIS-finetuned-ner
This model is a fine-tuned version of [vesteinn/XLMR-ENIS](https://huggingface.co/vesteinn/XLMR-ENIS) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0671
- Precision: 0.9398
- Recall: 0.9518
- F1: 0.9458
- Accuracy: 0.9854
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2825 | 1.0 | 878 | 0.0712 | 0.9220 | 0.9379 | 0.9299 | 0.9815 |
| 0.0688 | 2.0 | 1756 | 0.0689 | 0.9354 | 0.9477 | 0.9415 | 0.9839 |
| 0.039 | 3.0 | 2634 | 0.0671 | 0.9398 | 0.9518 | 0.9458 | 0.9854 |
### Framework versions
- Transformers 4.10.3
- Pytorch 1.9.0+cu102
- Datasets 1.12.1
- Tokenizers 0.10.3
|
vinhood/chefberto-italian-cased | 13845093331d54458b4508c66a35ead0ff7e466f | 2022-01-02T20:24:22.000Z | [
"pytorch",
"bert",
"fill-mask",
"it",
"transformers",
"license:mit",
"autotrain_compatible"
]
| fill-mask | false | vinhood | null | vinhood/chefberto-italian-cased | 11 | null | transformers | 11,161 | ---
language: it
license: mit
widget:
- text: "La pasta più semplice è aglio, [MASK] e peperoncino."
- text: "Per fare la carbonara servono le [MASK]."
- text: "A tavola non può mancare del buon [MASK]."
---
# ChefBERTo 👨🍳
**chefberto-italian-cased** is a BERT model obtained by MLM adaptive-tuning [**bert-base-italian-xxl-cased**](https://huggingface.co/dbmdz/bert-base-italian-xxl-cased) on Italian cooking recipes, approximately 50k sentences (2.6M words).
**Author:** Cristiano De Nobili ([@denocris](https://twitter.com/denocris) on Twitter, [LinkedIn](https://www.linkedin.com/in/cristiano-de-nobili/)) for [VINHOOD](https://www.vinhood.com/en/).
<p>
<img src="https://drive.google.com/uc?export=view&id=1u5aY2wKu-X5DAzbOq7rsgGFW5_lGUAQn" width="400"> </br>
</p>
# Perplexity
Test set: 9k sentences about food.
| Model | Perplexity |
| ------ | ------ |
| chefberto-italian-cased | **1.84** |
| bert-base-italian-xxl-cased | 2.85 |
# Usage
```python
from transformers import AutoModel, AutoTokenizer
model_name = "vinhood/chefberto-italian-cased"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModel.from_pretrained(model_name)
``` |
voidful/tts_hubert_cluster_bart_base | 8975f9dcf1a9b02fa92cb6e455a9086432add72f | 2021-08-11T07:19:13.000Z | [
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:librispeech",
"transformers",
"audio",
"automatic-speech-recognition",
"speech",
"asr",
"hubert",
"license:apache-2.0",
"autotrain_compatible"
]
| text2text-generation | false | voidful | null | voidful/tts_hubert_cluster_bart_base | 11 | null | transformers | 11,162 | ---
language: en
datasets:
- librispeech
tags:
- audio
- automatic-speech-recognition
- speech
- asr
- hubert
license: apache-2.0
metrics:
- wer
- cer
---
# voidful/tts_hubert_cluster_bart_base
## Usage
````python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("voidful/tts_hubert_cluster_bart_base")
model = AutoModelForSeq2SeqLM.from_pretrained("voidful/tts_hubert_cluster_bart_base")
````
generate output
```python
gen_output = model.generate(input_ids=tokenizer("going along slushy country roads and speaking to damp audience in drifty school rooms day after day for a fortnight he'll have to put in an appearance at some place of worship on sunday morning and he can come to ask immediately afterwards",return_tensors='pt').input_ids, max_length=1024)
print(tokenizer.decode(gen_output[0], skip_special_tokens=True))
```
## Result
`:vtok402::vtok329::vtok329::vtok75::vtok75::vtok75::vtok44::vtok150::vtok150::vtok222::vtok280::vtok280::vtok138::vtok409::vtok409::vtok409::vtok46::vtok441:` |
yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli | e77239d25f8338eafcda2b47ad57243d8bb44061 | 2021-06-03T05:12:16.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:wnli",
"transformers",
"wnli",
"glue",
"kd",
"torchdistill",
"license:apache-2.0"
]
| text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-base-uncased-wnli_from_bert-large-uncased-wnli | 11 | null | transformers | 11,163 | ---
language: en
tags:
- bert
- wnli
- glue
- kd
- torchdistill
license: apache-2.0
datasets:
- wnli
metrics:
- accuracy
---
`bert-base-uncased` fine-tuned on WNLI dataset, using fine-tuned `bert-large-uncased` as a teacher model, [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_kd_and_submission.ipynb) for knowledge distillation.
The training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/wnli/kd/bert_base_uncased_from_bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **78.9**.
|
yoshitomo-matsubara/bert-large-uncased-stsb | 59d22c2cb70a5fae4928263c42e2f74762418a67 | 2021-05-29T21:34:30.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:stsb",
"transformers",
"stsb",
"glue",
"torchdistill",
"license:apache-2.0"
]
| text-classification | false | yoshitomo-matsubara | null | yoshitomo-matsubara/bert-large-uncased-stsb | 11 | null | transformers | 11,164 | ---
language: en
tags:
- bert
- stsb
- glue
- torchdistill
license: apache-2.0
datasets:
- stsb
metrics:
- pearson correlation
- spearman correlation
---
`bert-large-uncased` fine-tuned on STS-B dataset, using [***torchdistill***](https://github.com/yoshitomo-matsubara/torchdistill) and [Google Colab](https://colab.research.google.com/github/yoshitomo-matsubara/torchdistill/blob/master/demo/glue_finetuning_and_submission.ipynb).
The hyperparameters are the same as those in Hugging Face's example and/or the paper of BERT, and the training configuration (including hyperparameters) is available [here](https://github.com/yoshitomo-matsubara/torchdistill/blob/main/configs/sample/glue/stsb/mse/bert_large_uncased.yaml).
I submitted prediction files to [the GLUE leaderboard](https://gluebenchmark.com/leaderboard), and the overall GLUE score was **80.2**.
|
z6228574/codegpt | 38be69baf21c2ed42e7df0c35c70369f6f1bcbaf | 2021-07-07T10:45:39.000Z | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
]
| text-generation | false | z6228574 | null | z6228574/codegpt | 11 | null | transformers | 11,165 | Entry not found |
zanelim/singbert-large-sg | 03a9bc840176ce94408fa77378f002e8373927a1 | 2021-05-20T09:36:17.000Z | [
"pytorch",
"tf",
"jax",
"bert",
"pretraining",
"en",
"dataset:reddit singapore, malaysia",
"dataset:hardwarezone",
"transformers",
"singapore",
"sg",
"singlish",
"malaysia",
"ms",
"manglish",
"bert-large-uncased",
"license:mit"
]
| null | false | zanelim | null | zanelim/singbert-large-sg | 11 | 1 | transformers | 11,166 | ---
language: en
tags:
- singapore
- sg
- singlish
- malaysia
- ms
- manglish
- bert-large-uncased
license: mit
datasets:
- reddit singapore, malaysia
- hardwarezone
widget:
- text: "kopi c siew [MASK]"
- text: "die [MASK] must try"
---
# Model name
SingBert Large - Bert for Singlish (SG) and Manglish (MY).
## Model description
Similar to [SingBert](https://huggingface.co/zanelim/singbert) but the large version, which was initialized from [BERT large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models), with pre-training finetuned on
[singlish](https://en.wikipedia.org/wiki/Singlish) and [manglish](https://en.wikipedia.org/wiki/Manglish) data.
## Intended uses & limitations
#### How to use
```python
>>> from transformers import pipeline
>>> nlp = pipeline('fill-mask', model='zanelim/singbert-large-sg')
>>> nlp("kopi c siew [MASK]")
[{'sequence': '[CLS] kopi c siew dai [SEP]',
'score': 0.9003700017929077,
'token': 18765,
'token_str': 'dai'},
{'sequence': '[CLS] kopi c siew mai [SEP]',
'score': 0.0779474675655365,
'token': 14736,
'token_str': 'mai'},
{'sequence': '[CLS] kopi c siew. [SEP]',
'score': 0.0032227332703769207,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] kopi c siew bao [SEP]',
'score': 0.0017727474914863706,
'token': 25945,
'token_str': 'bao'},
{'sequence': '[CLS] kopi c siew peng [SEP]',
'score': 0.0012526646023616195,
'token': 26473,
'token_str': 'peng'}]
>>> nlp("one teh c siew dai, and one kopi [MASK]")
[{'sequence': '[CLS] one teh c siew dai, and one kopi. [SEP]',
'score': 0.5249741077423096,
'token': 1012,
'token_str': '.'},
{'sequence': '[CLS] one teh c siew dai, and one kopi o [SEP]',
'score': 0.27349168062210083,
'token': 1051,
'token_str': 'o'},
{'sequence': '[CLS] one teh c siew dai, and one kopi peng [SEP]',
'score': 0.057190295308828354,
'token': 26473,
'token_str': 'peng'},
{'sequence': '[CLS] one teh c siew dai, and one kopi c [SEP]',
'score': 0.04022320732474327,
'token': 1039,
'token_str': 'c'},
{'sequence': '[CLS] one teh c siew dai, and one kopi? [SEP]',
'score': 0.01191170234233141,
'token': 1029,
'token_str': '?'}]
>>> nlp("die [MASK] must try")
[{'sequence': '[CLS] die die must try [SEP]',
'score': 0.9921030402183533,
'token': 3280,
'token_str': 'die'},
{'sequence': '[CLS] die also must try [SEP]',
'score': 0.004993876442313194,
'token': 2036,
'token_str': 'also'},
{'sequence': '[CLS] die liao must try [SEP]',
'score': 0.000317625846946612,
'token': 727,
'token_str': 'liao'},
{'sequence': '[CLS] die still must try [SEP]',
'score': 0.0002260878391098231,
'token': 2145,
'token_str': 'still'},
{'sequence': '[CLS] die i must try [SEP]',
'score': 0.00016935862367972732,
'token': 1045,
'token_str': 'i'}]
>>> nlp("dont play [MASK] leh")
[{'sequence': '[CLS] dont play play leh [SEP]',
'score': 0.9079819321632385,
'token': 2377,
'token_str': 'play'},
{'sequence': '[CLS] dont play punk leh [SEP]',
'score': 0.006846973206847906,
'token': 7196,
'token_str': 'punk'},
{'sequence': '[CLS] dont play games leh [SEP]',
'score': 0.004041737411171198,
'token': 2399,
'token_str': 'games'},
{'sequence': '[CLS] dont play politics leh [SEP]',
'score': 0.003728888463228941,
'token': 4331,
'token_str': 'politics'},
{'sequence': '[CLS] dont play cheat leh [SEP]',
'score': 0.0032805048394948244,
'token': 21910,
'token_str': 'cheat'}]
>>> nlp("confirm plus [MASK]")
{'sequence': '[CLS] confirm plus chop [SEP]',
'score': 0.9749826192855835,
'token': 24494,
'token_str': 'chop'},
{'sequence': '[CLS] confirm plus chopped [SEP]',
'score': 0.017554156482219696,
'token': 24881,
'token_str': 'chopped'},
{'sequence': '[CLS] confirm plus minus [SEP]',
'score': 0.002725469646975398,
'token': 15718,
'token_str': 'minus'},
{'sequence': '[CLS] confirm plus guarantee [SEP]',
'score': 0.000900257145985961,
'token': 11302,
'token_str': 'guarantee'},
{'sequence': '[CLS] confirm plus one [SEP]',
'score': 0.0004384620988275856,
'token': 2028,
'token_str': 'one'}]
>>> nlp("catch no [MASK]")
[{'sequence': '[CLS] catch no ball [SEP]',
'score': 0.9381157159805298,
'token': 3608,
'token_str': 'ball'},
{'sequence': '[CLS] catch no balls [SEP]',
'score': 0.060842301696538925,
'token': 7395,
'token_str': 'balls'},
{'sequence': '[CLS] catch no fish [SEP]',
'score': 0.00030917322146706283,
'token': 3869,
'token_str': 'fish'},
{'sequence': '[CLS] catch no breath [SEP]',
'score': 7.552534952992573e-05,
'token': 3052,
'token_str': 'breath'},
{'sequence': '[CLS] catch no tail [SEP]',
'score': 4.208395694149658e-05,
'token': 5725,
'token_str': 'tail'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import BertTokenizer, BertModel
tokenizer = BertTokenizer.from_pretrained('zanelim/singbert-large-sg')
model = BertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='pt')
output = model(**encoded_input)
```
and in TensorFlow:
```python
from transformers import BertTokenizer, TFBertModel
tokenizer = BertTokenizer.from_pretrained("zanelim/singbert-large-sg")
model = TFBertModel.from_pretrained("zanelim/singbert-large-sg")
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
#### Limitations and bias
This model was finetuned on colloquial Singlish and Manglish corpus, hence it is best applied on downstream tasks involving the main
constituent languages- english, mandarin, malay. Also, as the training data is mainly from forums, beware of existing inherent bias.
## Training data
Colloquial singlish and manglish (both are a mixture of English, Mandarin, Tamil, Malay, and other local dialects like Hokkien, Cantonese or Teochew)
corpus. The corpus is collected from subreddits- `r/singapore` and `r/malaysia`, and forums such as `hardwarezone`.
## Training procedure
Initialized with [bert large uncased (whole word masking)](https://github.com/google-research/bert#pre-trained-models) vocab and checkpoints (pre-trained weights).
Top 1000 custom vocab tokens (non-overlapped with original bert vocab) were further extracted from training data and filled into unused tokens in original bert vocab.
Pre-training was further finetuned on training data with the following hyperparameters
* train_batch_size: 512
* max_seq_length: 128
* num_train_steps: 300000
* num_warmup_steps: 5000
* learning_rate: 2e-5
* hardware: TPU v3-8
|
wietsedv/xlm-roberta-base-ft-udpos28-zh | 8191f0f4a70f7c127e222490c1b1e8e61ff4ff4d | 2022-02-25T09:59:40.000Z | [
"pytorch",
"xlm-roberta",
"token-classification",
"zh",
"dataset:universal_dependencies",
"transformers",
"part-of-speech",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | wietsedv | null | wietsedv/xlm-roberta-base-ft-udpos28-zh | 11 | null | transformers | 11,167 |
---
language:
- zh
license: apache-2.0
library_name: transformers
tags:
- part-of-speech
- token-classification
datasets:
- universal_dependencies
metrics:
- accuracy
model-index:
- name: xlm-roberta-base-ft-udpos28-zh
results:
- task:
type: token-classification
name: Part-of-Speech Tagging
dataset:
type: universal_dependencies
name: Universal Dependencies v2.8
metrics:
- type: accuracy
name: English Test accuracy
value: 60.2
- type: accuracy
name: Dutch Test accuracy
value: 56.9
- type: accuracy
name: German Test accuracy
value: 57.5
- type: accuracy
name: Italian Test accuracy
value: 57.3
- type: accuracy
name: French Test accuracy
value: 54.1
- type: accuracy
name: Spanish Test accuracy
value: 54.4
- type: accuracy
name: Russian Test accuracy
value: 69.6
- type: accuracy
name: Swedish Test accuracy
value: 61.8
- type: accuracy
name: Norwegian Test accuracy
value: 60.3
- type: accuracy
name: Danish Test accuracy
value: 62.6
- type: accuracy
name: Low Saxon Test accuracy
value: 29.6
- type: accuracy
name: Akkadian Test accuracy
value: 16.3
- type: accuracy
name: Armenian Test accuracy
value: 70.7
- type: accuracy
name: Welsh Test accuracy
value: 52.3
- type: accuracy
name: Old East Slavic Test accuracy
value: 50.1
- type: accuracy
name: Albanian Test accuracy
value: 59.0
- type: accuracy
name: Slovenian Test accuracy
value: 52.9
- type: accuracy
name: Guajajara Test accuracy
value: 20.3
- type: accuracy
name: Kurmanji Test accuracy
value: 66.5
- type: accuracy
name: Turkish Test accuracy
value: 69.6
- type: accuracy
name: Finnish Test accuracy
value: 70.3
- type: accuracy
name: Indonesian Test accuracy
value: 65.8
- type: accuracy
name: Ukrainian Test accuracy
value: 69.4
- type: accuracy
name: Polish Test accuracy
value: 65.3
- type: accuracy
name: Portuguese Test accuracy
value: 60.6
- type: accuracy
name: Kazakh Test accuracy
value: 76.2
- type: accuracy
name: Latin Test accuracy
value: 60.5
- type: accuracy
name: Old French Test accuracy
value: 19.5
- type: accuracy
name: Buryat Test accuracy
value: 56.2
- type: accuracy
name: Kaapor Test accuracy
value: 10.4
- type: accuracy
name: Korean Test accuracy
value: 63.2
- type: accuracy
name: Estonian Test accuracy
value: 70.4
- type: accuracy
name: Croatian Test accuracy
value: 61.2
- type: accuracy
name: Gothic Test accuracy
value: 5.4
- type: accuracy
name: Swiss German Test accuracy
value: 36.2
- type: accuracy
name: Assyrian Test accuracy
value: 17.0
- type: accuracy
name: North Sami Test accuracy
value: 22.9
- type: accuracy
name: Naija Test accuracy
value: 21.5
- type: accuracy
name: Latvian Test accuracy
value: 74.1
- type: accuracy
name: Chinese Test accuracy
value: 93.4
- type: accuracy
name: Tagalog Test accuracy
value: 59.1
- type: accuracy
name: Bambara Test accuracy
value: 21.0
- type: accuracy
name: Lithuanian Test accuracy
value: 73.8
- type: accuracy
name: Galician Test accuracy
value: 56.7
- type: accuracy
name: Vietnamese Test accuracy
value: 59.6
- type: accuracy
name: Greek Test accuracy
value: 58.4
- type: accuracy
name: Catalan Test accuracy
value: 52.2
- type: accuracy
name: Czech Test accuracy
value: 64.6
- type: accuracy
name: Erzya Test accuracy
value: 39.4
- type: accuracy
name: Bhojpuri Test accuracy
value: 42.7
- type: accuracy
name: Thai Test accuracy
value: 65.6
- type: accuracy
name: Marathi Test accuracy
value: 74.2
- type: accuracy
name: Basque Test accuracy
value: 66.0
- type: accuracy
name: Slovak Test accuracy
value: 66.0
- type: accuracy
name: Kiche Test accuracy
value: 23.1
- type: accuracy
name: Yoruba Test accuracy
value: 16.4
- type: accuracy
name: Warlpiri Test accuracy
value: 29.6
- type: accuracy
name: Tamil Test accuracy
value: 82.6
- type: accuracy
name: Maltese Test accuracy
value: 13.7
- type: accuracy
name: Ancient Greek Test accuracy
value: 65.2
- type: accuracy
name: Icelandic Test accuracy
value: 63.4
- type: accuracy
name: Mbya Guarani Test accuracy
value: 23.2
- type: accuracy
name: Urdu Test accuracy
value: 53.8
- type: accuracy
name: Romanian Test accuracy
value: 61.2
- type: accuracy
name: Persian Test accuracy
value: 59.6
- type: accuracy
name: Apurina Test accuracy
value: 24.7
- type: accuracy
name: Japanese Test accuracy
value: 56.4
- type: accuracy
name: Hungarian Test accuracy
value: 59.9
- type: accuracy
name: Hindi Test accuracy
value: 59.4
- type: accuracy
name: Classical Chinese Test accuracy
value: 58.2
- type: accuracy
name: Komi Permyak Test accuracy
value: 34.7
- type: accuracy
name: Faroese Test accuracy
value: 55.9
- type: accuracy
name: Sanskrit Test accuracy
value: 19.0
- type: accuracy
name: Livvi Test accuracy
value: 52.8
- type: accuracy
name: Arabic Test accuracy
value: 64.2
- type: accuracy
name: Wolof Test accuracy
value: 17.6
- type: accuracy
name: Bulgarian Test accuracy
value: 64.2
- type: accuracy
name: Akuntsu Test accuracy
value: 16.5
- type: accuracy
name: Makurap Test accuracy
value: 6.8
- type: accuracy
name: Kangri Test accuracy
value: 38.9
- type: accuracy
name: Breton Test accuracy
value: 49.9
- type: accuracy
name: Telugu Test accuracy
value: 82.8
- type: accuracy
name: Cantonese Test accuracy
value: 80.6
- type: accuracy
name: Old Church Slavonic Test accuracy
value: 41.0
- type: accuracy
name: Karelian Test accuracy
value: 60.5
- type: accuracy
name: Upper Sorbian Test accuracy
value: 47.0
- type: accuracy
name: South Levantine Arabic Test accuracy
value: 59.7
- type: accuracy
name: Komi Zyrian Test accuracy
value: 29.4
- type: accuracy
name: Irish Test accuracy
value: 49.7
- type: accuracy
name: Nayini Test accuracy
value: 50.0
- type: accuracy
name: Munduruku Test accuracy
value: 10.6
- type: accuracy
name: Manx Test accuracy
value: 22.3
- type: accuracy
name: Skolt Sami Test accuracy
value: 24.9
- type: accuracy
name: Afrikaans Test accuracy
value: 58.6
- type: accuracy
name: Old Turkish Test accuracy
value: 45.7
- type: accuracy
name: Tupinamba Test accuracy
value: 20.7
- type: accuracy
name: Belarusian Test accuracy
value: 69.7
- type: accuracy
name: Serbian Test accuracy
value: 61.9
- type: accuracy
name: Moksha Test accuracy
value: 35.1
- type: accuracy
name: Western Armenian Test accuracy
value: 67.2
- type: accuracy
name: Scottish Gaelic Test accuracy
value: 44.6
- type: accuracy
name: Khunsari Test accuracy
value: 44.6
- type: accuracy
name: Hebrew Test accuracy
value: 82.3
- type: accuracy
name: Uyghur Test accuracy
value: 71.6
- type: accuracy
name: Chukchi Test accuracy
value: 32.1
---
# XLM-RoBERTa base Universal Dependencies v2.8 POS tagging: Chinese
This model is part of our paper called:
- Make the Best of Cross-lingual Transfer: Evidence from POS Tagging with over 100 Languages
Check the [Space](https://huggingface.co/spaces/wietsedv/xpos) for more details.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-zh")
model = AutoModelForTokenClassification.from_pretrained("wietsedv/xlm-roberta-base-ft-udpos28-zh")
```
|
SuperAI2-Machima/mt5-small-thai-qg-v2 | ba0dfc3b8a8699fef51c7722ad84f29f9c9e80fb | 2022-03-01T14:53:52.000Z | [
"pytorch",
"mt5",
"text2text-generation",
"thai",
"th",
"dataset:NSC2018",
"dataset:wiki-documents-nsc",
"dataset:ThaiQACorpus-DevelopmentDataset",
"transformers",
"question-generation",
"license:mit",
"autotrain_compatible"
]
| text2text-generation | false | SuperAI2-Machima | null | SuperAI2-Machima/mt5-small-thai-qg-v2 | 11 | 2 | transformers | 11,168 | ---
tags:
- question-generation
language:
- thai
- th
datasets:
- NSC2018
- wiki-documents-nsc
- ThaiQACorpus-DevelopmentDataset
widget:
- text: "โรงเรียนบ้านขุนด่าน ตั้งอยู่ที่ขุนด่าน จ.นครนายก </s>"
example_title: "Example 01"
- text: "พลเอก ประยุทธ์ จันทร์โอชา (เกิด 21 มีนาคม พ.ศ. 2497) ชื่อเล่น ตู่ เป็นนักการเมืองและอดีตนายทหารบกชาวไทย </s>"
example_title: "Example 02"
- text: "วันที่ 1 กันยายน 2550 12:00 น. ตำรวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่า 80 แผ่น </s>"
example_title: "Example 03"
- text: "กรุงเทพมหานคร เป็นศูนย์กลางการปกครอง การศึกษา การคมนาคมขนส่ง การเงินการธนาคาร การพาณิชย์ การสื่อสาร และความเจริญของประเทศ ตั้งอยู่บนสามเหลี่ยมปากแม่น้ำเจ้าพระยา มีแม่น้ำเจ้าพระยาไหลผ่านและแบ่งเมืองออกเป็น 2 ฝั่ง คือ ฝั่งพระนครและฝั่งธนบุรี กรุงเทพมหานครมีพื้นที่ทั้งหมด 1,568.737 ตร.กม. </s>"
example_title: "Example 04"
license: mit
---
[SuperAI Engineer Season 2](https://superai.aiat.or.th/) , [Machima](https://machchima.superai.me/)
[Google's mT5](https://github.com/google-research/multilingual-t5) , [Pollawat](https://huggingface.co/Pollawat/mt5-small-thai-qg)
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config
model = T5ForConditionalGeneration.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg-v2')
tokenizer = T5Tokenizer.from_pretrained('SuperAI2-Machima/mt5-small-thai-qg-v2')
source_text = 'บุกยึดไม้เถื่อน อดีต ส.ส.บุรีรัมย์ เตรียมสร้างคฤหาสน์ทรงไทย 1 กันยายน 2550 12:00 น. ตำรวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่า 80 แผ่น'
print('Predicted Summary Text : ')
tokenized_text = tokenizer.encode(source_text, return_tensors="pt").to(device)
summary_ids = model.generate(tokenized_text,
num_beams=4,
no_repeat_ngram_size=2,
max_length=50,
early_stopping=True)
output = tokenizer.decode(summary_ids[0], skip_special_tokens=True)
print(output)
#Predicted Summary Text :
#answer: 80 แผ่น question: ตํารวจภูธรจ.บุรีรัมย์บุกตรวจยึดไม้แปรรูปหวงห้ามกว่ากี่แผ่น
``` |
mrm8488/biomedtra-small-es | e48b9360e1b3a886851a0c3fff5f07d67963a1e6 | 2022-03-30T21:07:50.000Z | [
"pytorch",
"tensorboard",
"electra",
"pretraining",
"es",
"dataset:cowese",
"transformers",
"Spanish",
"Electra",
"Bio",
"Medical"
]
| null | false | mrm8488 | null | mrm8488/biomedtra-small-es | 11 | 2 | transformers | 11,169 | ---
language: es
tags:
- Spanish
- Electra
- Bio
- Medical
datasets:
- cowese
---
## 🦠 BIOMEDtra 🏥
**BIOMEDtra** (small) is an Electra like model (discriminator in this case) trained on [Spanish Biomedical Crawled Corpus](https://zenodo.org/record/5510033#.Yhdk1ZHMLJx).
As mentioned in the original [paper](https://openreview.net/pdf?id=r1xMH1BtvB):
**ELECTRA** is a new method for self-supervised language representation learning. It can be used to pre-train transformer networks using relatively little compute. ELECTRA models are trained to distinguish "real" input tokens vs "fake" input tokens generated by another neural network, similar to the discriminator of a [GAN](https://arxiv.org/pdf/1406.2661.pdf). At small scale, ELECTRA achieves strong results even when trained on a single GPU. At large scale, ELECTRA achieves state-of-the-art results on the [SQuAD 2.0](https://rajpurkar.github.io/SQuAD-explorer/) dataset.
For a detailed description and experimental results, please refer the paper [ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators](https://openreview.net/pdf?id=r1xMH1BtvB).
## Training details
The model was trained using the Electra base code for 3 days on 1 GPU (Tesla V100 16GB).
## Dataset details
The largest Spanish biomedical and heath corpus to date gathered from a massive Spanish health domain crawler over more than 3,000 URLs were downloaded and preprocessed. The collected data have been preprocessed to produce the **CoWeSe** (Corpus Web Salud Español) resource, a large-scale and high-quality corpus intended for biomedical and health NLP in Spanish.
## Model details ⚙
|Param| # Value|
|-----|--------|
|Layers| 12 |
|Hidden | 256 |
|Params| 14M |
## Evaluation metrics (for discriminator) 🧾
|Metric | # Score |
|-------|---------|
|Accuracy| 0.9561|
|Precision| 0.808|
|Recall | 0.531 |
|AUC | 0.949|
## Benchmarks 🔨
WIP 🚧
## How to use the discriminator in `transformers`
```py
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("mrm8488/biomedtra-small-es")
tokenizer = ElectraTokenizerFast.from_pretrained("mrm8488/biomedtra-small-es")
sentence = "Los españoles tienden a sufir déficit de vitamina c"
fake_sentence = "Los españoles tienden a déficit sufrir de vitamina c"
fake_tokens = tokenizer.tokenize(fake_sentence)
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
discriminator_outputs = discriminator(fake_inputs)
predictions = torch.round((torch.sign(discriminator_outputs[0]) + 1) / 2)
[print("%7s" % token, end="") for token in fake_tokens]
[print("%7s" % prediction, end="") for prediction in predictions.tolist()]
```
## Acknowledgments
TBA
## Citation
If you want to cite this model you can use this:
```bibtex
@misc{mromero2022biomedtra,
title={Spanish BioMedical Electra (small)},
author={Romero, Manuel},
publisher={Hugging Face},
journal={Hugging Face Hub},
howpublished={\url{https://huggingface.co/mrm8488/biomedtra-small-es},
year={2022}
}
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain |
inovex/multi2convai-corona-en-bert | 5e862ff6f37fe603efa642cefb4e7538ddce0898 | 2022-03-01T09:20:04.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"transformers",
"license:mit"
]
| text-classification | false | inovex | null | inovex/multi2convai-corona-en-bert | 11 | null | transformers | 11,170 | ---
tags:
- text-classification
- pytorch
- transformers
widget:
- text: "Do I need to wear a mask?"
license: mit
language: en
---
# Multi2ConvAI-Corona: finetuned Bert for English
This model was developed in the [Multi2ConvAI](https://multi2conv.ai) project:
- domain: Corona (more details about our use cases: ([en](https://multi2convai/en/blog/use-cases), [de](https://multi2convai/en/blog/use-cases)))
- language: English (en)
- model type: finetuned Bert
## How to run
Requires:
- Huggingface transformers
### Run with Huggingface Transformers
````python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("inovex/multi2convai-logistics-en-bert")
model = AutoModelForSequenceClassification.from_pretrained("inovex/multi2convai-logistics-en-bert")
````
## Further information on Multi2ConvAI:
- https://multi2conv.ai
- https://github.com/inovex/multi2convai
- mailto: [email protected] |
ghadeermobasher/BC5CDR-Disease-Modified_BiomedNLP-PubMedBERT-base-uncased-abstract | fa7196fb87e9f0a57b50ef596b09dcdf9f62d9b7 | 2022-02-25T18:29:23.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | ghadeermobasher | null | ghadeermobasher/BC5CDR-Disease-Modified_BiomedNLP-PubMedBERT-base-uncased-abstract | 11 | null | transformers | 11,171 | Entry not found |
Jackett/subject_classifier | e84d02b8f8251e1ce439d589b8fe2a15ce6cfefc | 2022-02-27T04:57:39.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers"
]
| text-classification | false | Jackett | null | Jackett/subject_classifier | 11 | null | transformers | 11,172 | Label association
{'Biology': 0, 'Physics': 1, 'Chemistry': 2, 'Maths': 3}
|
facebook/wav2vec2-base-en-voxpopuli-v2 | f464d59d152a11106e62cecb3b3bcdfd8f9d1b3f | 2022-02-27T13:13:03.000Z | [
"pytorch",
"wav2vec2",
"pretraining",
"en",
"dataset:voxpopuli",
"arxiv:2101.00390",
"transformers",
"audio",
"automatic-speech-recognition",
"voxpopuli-v2",
"license:cc-by-nc-4.0"
]
| automatic-speech-recognition | false | facebook | null | facebook/wav2vec2-base-en-voxpopuli-v2 | 11 | null | transformers | 11,173 | ---
language: en
tags:
- audio
- automatic-speech-recognition
- voxpopuli-v2
datasets:
- voxpopuli
license: cc-by-nc-4.0
inference: false
---
# Wav2Vec2-base-VoxPopuli-V2
[Facebook's Wav2Vec2](https://ai.facebook.com/blog/wav2vec-20-learning-the-structure-of-speech-from-raw-audio/) base model pretrained only in **en** on **24.1k** unlabeled datat of the [VoxPopuli corpus](https://arxiv.org/abs/2101.00390).
The model is pretrained on 16kHz sampled speech audio. When using the model make sure that your speech input is also sampled at 16Khz.
**Note**: This model does not have a tokenizer as it was pretrained on audio alone. In order to use this model for **speech recognition**, a tokenizer should be created and the model should be fine-tuned on labeled text data in **en**. Check out [this blog](https://huggingface.co/blog/fine-tune-xlsr-wav2vec2) for a more in-detail explanation of how to fine-tune the model.
**Paper**: *[VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation
Learning, Semi-Supervised Learning and Interpretation](https://arxiv.org/abs/2101.00390)*
**Authors**: *Changhan Wang, Morgane Riviere, Ann Lee, Anne Wu, Chaitanya Talnikar, Daniel Haziza, Mary Williamson, Juan Pino, Emmanuel Dupoux* from *Facebook AI*.
See the official website for more information, [here](https://github.com/facebookresearch/voxpopuli/).
|
azizbarank/mbert-finetuned-azerbaijani-ner | fbba54b7deeb4a5bcce5ca5d33f7c5f776fa084e | 2022-03-01T00:58:02.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:wikiann",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| token-classification | false | azizbarank | null | azizbarank/mbert-finetuned-azerbaijani-ner | 11 | null | transformers | 11,174 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wikiann
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: mbert-finetuned-azerbaijani-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: wikiann
type: wikiann
args: az
metrics:
- name: Precision
type: precision
value: 0.8898541731306236
- name: Recall
type: recall
value: 0.915416533673795
- name: F1
type: f1
value: 0.9024543738200126
- name: Accuracy
type: accuracy
value: 0.966948310139165
---
<!-- 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. -->
# mbert-finetuned-azerbaijani-ner
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the wikiann dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1385
- Precision: 0.8899
- Recall: 0.9154
- F1: 0.9025
- Accuracy: 0.9669
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2928 | 1.0 | 625 | 0.1415 | 0.8584 | 0.8918 | 0.8748 | 0.9595 |
| 0.1254 | 2.0 | 1250 | 0.1335 | 0.8875 | 0.9119 | 0.8996 | 0.9637 |
| 0.077 | 3.0 | 1875 | 0.1385 | 0.8899 | 0.9154 | 0.9025 | 0.9669 |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
coastalcph/fairlex-scotus-minilm | 2df435689d16fdcbaee9d901a122f6100d17cd1d | 2022-03-01T13:24:01.000Z | [
"pytorch",
"roberta",
"fill-mask",
"en",
"transformers",
"legal",
"fairlex",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible"
]
| fill-mask | false | coastalcph | null | coastalcph/fairlex-scotus-minilm | 11 | null | transformers | 11,175 | ---
language: en
pipeline_tag: fill-mask
license: cc-by-nc-sa-4.0
tags:
- legal
- fairlex
widget:
- text: "Because the Court granted <mask> before judgment, the Court effectively stands in the shoes of the Court of Appeals and reviews the defendants’ appeals."
---
# FairLex: A multilingual benchmark for evaluating fairness in legal text processing
We present a benchmark suite of four datasets for evaluating the fairness of pre-trained legal language models and the techniques used to fine-tune them for downstream tasks. Our benchmarks cover four jurisdictions (European Council, USA, Swiss, and Chinese), five languages (English, German, French, Italian and Chinese) and fairness across five attributes (gender, age, nationality/region, language, and legal area). In our experiments, we evaluate pre-trained language models using several group-robust fine-tuning techniques and show that performance group disparities are vibrant in many cases, while none of these techniques guarantee fairness, nor consistently mitigate group disparities. Furthermore, we provide a quantitative and qualitative analysis of our results, highlighting open challenges in the development of robustness methods in legal NLP.
---
Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. FairLex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland.
---
## Pre-training details
For the purpose of this work, we release four domain-specific BERT models with continued pre-training on the corpora of the examined datasets (ECtHR, SCOTUS, FSCS, SPC).
We train mini-sized BERT models with 6 Transformer blocks, 384 hidden units, and 12 attention heads.
We warm-start all models from the public MiniLMv2 (Wang et al., 2021) using the distilled version of RoBERTa (Liu et al., 2019).
For the English datasets (ECtHR, SCOTUS) and the one distilled from XLM-R (Conneau et al., 2021) for the rest (trilingual FSCS, and Chinese SPC).
## Models list
| Model name | Training corpora | Language |
|-----------------------------------|------------------|--------------------|
| `coastalcph/fairlex-ecthr-minlm` | ECtHR | `en` |
| `coastalcph/fairlex-scotus-minlm` | SCOTUS | `en` |
| `coastalcph/fairlex-fscs-minlm` | FSCS | [`de`, `fr`, `it`] |
| `coastalcph/fairlex-cail-minlm` | CAIL | `zh` |
## Load Pretrained Model
```python
from transformers import AutoTokenizer, AutoModel
tokenizer = AutoTokenizer.from_pretrained("coastalcph/fairlex-scotus-minlm")
model = AutoModel.from_pretrained("coastalcph/fairlex-scotus-minlm")
```
## Evaluation on downstream tasks
Consider the experiments in the article:
_Ilias Chalkidis, Tommaso Passini, Sheng Zhang, Letizia Tomada, Sebastian Felix Schwemer, and Anders Søgaard. 2022. Fairlex: A multilingual bench-mark for evaluating fairness in legal text processing. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics, Dublin, Ireland._
## Author - Publication
```
@inproceedings{chalkidis-2022-fairlex,
author={Chalkidis, Ilias and Passini, Tommaso and Zhang, Sheng and
Tomada, Letizia and Schwemer, Sebastian Felix and Søgaard, Anders},
title={FairLex: A Multilingual Benchmark for Evaluating Fairness in Legal Text Processing},
booktitle={Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics},
year={2022},
address={Dublin, Ireland}
}
```
Ilias Chalkidis on behalf of [CoAStaL NLP Group](https://coastalcph.github.io)
| Github: [@ilias.chalkidis](https://github.com/iliaschalkidis) | Twitter: [@KiddoThe2B](https://twitter.com/KiddoThe2B) | |
adit94/relevancy_classifier | e8720d8fd074c4d649e276743b6675832b5b280c | 2022-03-02T06:45:18.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | adit94 | null | adit94/relevancy_classifier | 11 | null | transformers | 11,176 | {'junk': 0, 'relevant': 1}
|
segments-tobias/segformer-b3-finetuned-segments-sidewalk | c9757006e487c23728d764e475683631d7b296a8 | 2022-03-08T17:32:09.000Z | [
"pytorch",
"segformer",
"dataset:segments/sidewalk-semantic",
"transformers",
"generated_from_trainer",
"vision",
"image-segmentation",
"license:apache-2.0",
"model-index"
]
| image-segmentation | false | segments-tobias | null | segments-tobias/segformer-b3-finetuned-segments-sidewalk | 11 | 1 | transformers | 11,177 | ---
license: apache-2.0
tags:
- generated_from_trainer
- vision
- image-segmentation
datasets:
- segments/sidewalk-semantic
widget:
- src: https://segmentsai-prod.s3.eu-west-2.amazonaws.com/assets/admin-tobias/439f6843-80c5-47ce-9b17-0b2a1d54dbeb.jpg
example_title: Brugge
model-index:
- name: segformer-b3-finetuned-segments-sidewalk
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. -->
# segformer-b3-finetuned-segments-sidewalk
This model is a fine-tuned version of [nvidia/mit-b3](https://huggingface.co/nvidia/mit-b3) on the [`sidewalk-semantic`](https://huggingface.co/datasets/segments/sidewalk-semantic) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8527
- Miou: 0.4345
- Macc: 0.5079
- Overall Accuracy: 0.8871
- Per Category Iou: [nan, 0.8382620833593052, 0.8876413942052827, 0.6261839847460975, 0.6590417473673477, 0.48228357004057837, 0.0, 0.6202905105623743, 0.748344409080285, 0.39096811362981676, 0.8848513296576286, 0.2415092028297553, 0.0, 0.07068982339740462, 0.41356382978723405, 0.6474134903246308, 0.0, 0.3062052505966587, 0.7704161510118073, 0.16108765491481541, 0.49752934863906867, 0.4734664813860761, 0.09820294554789893, nan, 0.17153699720635862, 0.514555863370054, 0.4660696051735875, 0.08826901031715705, 0.8991007829081079, 0.829742650939299, 0.9612781430019607, 0.01112666737555973, 0.1861992251927429, 0.391388886866003, 0.0]
- Per Category Accuracy: [nan, 0.9255583122183136, 0.9555184973850358, 0.8927561553139153, 0.7130378697969978, 0.6275811980710011, 0.0, 0.7474676455043131, 0.8545937449541465, 0.43523520560447965, 0.9672661630501664, 0.28627436744473084, 0.0, 0.0707036205718747, 0.47675012774655084, 0.7689381524189783, 0.0, 0.31600985221674877, 0.9278457312029238, 0.2055231456928555, 0.6363063556709445, 0.5255962863991213, 0.10240946878962942, nan, 0.30514996921453075, 0.6575213496395762, 0.6054551483999336, 0.08830275229357798, 0.9550074747938649, 0.8984159398975186, 0.9823971352874257, 0.013025497748978224, 0.3256981066248004, 0.49491941043060034, 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: 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss | Miou | Macc | Overall Accuracy | Per Category Iou | Per Category Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:----------------:|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------:|
| 0.4111 | 5.0 | 250 | 0.5342 | 0.3203 | 0.3895 | 0.8534 | [nan, 0.7411544992329885, 0.8587185188919024, 0.5322704558305212, 0.6145803724062279, 0.4207354824823325, 0.0, 0.4207652960849892, 0.6330214639515686, 0.0, 0.8090628889518269, 0.0, 0.0, 0.0, 0.0, 0.5525831345173927, 0.0, 0.0, 0.7449180731329554, 0.0, 0.39030048997684846, 0.5341813036240857, 0.0, nan, 0.0, 0.33603046089798805, 0.0, 0.0, 0.8611153164212525, 0.7580460497843906, 0.9307216449484303, 0.0, 0.12255543837545918, 0.30973651706611804, 0.0] | [nan, 0.865566426722594, 0.9394823497202754, 0.7339862219054845, 0.6960293899277608, 0.579233048631689, 0.0, 0.5226808772686938, 0.8148925583341846, 0.0, 0.954498711658196, 0.0, 0.0, 0.0, 0.0, 0.7453393323599813, 0.0, 0.0, 0.8609332075296946, 0.0, 0.5752897519263941, 0.6257335170644275, 0.0, nan, 0.0, 0.48320796165623753, 0.0, 0.0, 0.9563707209678979, 0.8591391181347248, 0.9690236728180618, 0.0, 0.23234437690377469, 0.43908949309871237, 0.0] |
| 0.2527 | 10.0 | 500 | 0.5899 | 0.3521 | 0.4258 | 0.8567 | [nan, 0.7536144931874272, 0.8666514611419747, 0.5791278186302583, 0.5507597043116981, 0.38697553330878387, 0.0, 0.49981379939131665, 0.6547462641660816, 0.006951340615690168, 0.8411064971463371, 0.21915505349651998, 0.0, 0.0, 0.0, 0.5704538365564567, 0.0, 0.0, 0.7601855085224487, 0.12506138175041864, 0.39942757047955846, 0.4668252406895441, 0.0, nan, 0.1030902538148915, 0.3805134719351324, 0.3639179515418502, 0.0, 0.8767798800448732, 0.7800121144818535, 0.9401348565379605, 0.00018008110081004338, 0.16755112790045706, 0.3264804931974313, 0.0] | [nan, 0.90406121258153, 0.933431363952898, 0.7264726392177598, 0.5740020955021516, 0.6563755737609668, 0.0, 0.6039363626224962, 0.8605186066359769, 0.0072522755864722855, 0.9522222704681134, 0.25318546484190657, 0.0, 0.0, 0.0, 0.7265874080033372, 0.0, 0.0, 0.9034801649669348, 0.15050382604742785, 0.6282930136175867, 0.4977144779061467, 0.0, nan, 0.1478142316826458, 0.4757332103391217, 0.43831868678494446, 0.0, 0.9461766367056283, 0.8685344399584078, 0.9696726615409282, 0.00019363628190676414, 0.39697811413925904, 0.4314488757452496, 0.0] |
| 0.1643 | 15.0 | 750 | 0.5756 | 0.3745 | 0.4534 | 0.8670 | [nan, 0.7726733036696652, 0.8671375594955328, 0.6103086102682944, 0.6314757371793478, 0.4273275344315441, 0.0, 0.5317600409405491, 0.6720224116289428, 0.16158774132109774, 0.8523694222801956, 0.24038155802861685, 0.0, 0.0, 0.04680851063829787, 0.5899459811865512, 0.0, 0.0, 0.7737178234025645, 0.14913933159903917, 0.4521741438458425, 0.5380504294958312, 0.0, nan, 0.14005003894540563, 0.40247802412573747, 0.41014102702120786, 0.0, 0.8822285387940414, 0.7982006290565458, 0.9485248204807992, 0.0010217644126931384, 0.12182141082818915, 0.3359618308006764, 0.0] | [nan, 0.8685068344016257, 0.9339337963085826, 0.7830275791780654, 0.71311646057369, 0.6411881935971181, 0.0, 0.7043771304992945, 0.8750572549898341, 0.18416833172993907, 0.9605602195211583, 0.301884052709914, 0.0, 0.0, 0.047777210015329585, 0.7549536664580913, 0.0, 0.0, 0.9068618510561295, 0.22672929767406622, 0.5668210000093578, 0.6053490157566916, 0.0, nan, 0.26095083120767, 0.5263161648629628, 0.5264190570939037, 0.0, 0.9540034951620896, 0.8931918202171408, 0.9742561443961733, 0.0012759247861356422, 0.2862606175274747, 0.465761930571415, 0.0] |
| 0.1302 | 20.0 | 1000 | 0.6021 | 0.3949 | 0.4802 | 0.8703 | [nan, 0.7801307689107726, 0.8786287731596124, 0.5996414476192669, 0.5791044393247451, 0.40685088294894184, 0.0, 0.5532316603013168, 0.7004119209771223, 0.3567117426846971, 0.8682022390275189, 0.4354632088736135, 0.0, 0.08566271525440265, 0.0, 0.592928092042186, 0.0, 0.07216748768472907, 0.7775334326155094, 0.16241710128130835, 0.46182139479806994, 0.562496456296332, 0.0, nan, 0.17592232145836345, 0.4180433534862313, 0.4040778498609824, 0.0, 0.8871669760617459, 0.8059650048666752, 0.9507085299921569, 0.0116151761673367, 0.16524860560484375, 0.34088472074456944, 0.0] | [nan, 0.901160937374861, 0.9425971578567806, 0.7984110745840901, 0.6312022008440196, 0.6127889140665853, 0.0, 0.6839893129548904, 0.8679197408614445, 0.4606921729692395, 0.9554783385950772, 0.5059353105601336, 0.0, 0.08568361471650239, 0.0, 0.7677860214733371, 0.0, 0.07216748768472907, 0.9086920613558305, 0.26755814834457153, 0.6342091828512193, 0.6617058325161462, 0.0, nan, 0.347809833758466, 0.541995549384712, 0.5421986403581496, 0.0, 0.9485582664128994, 0.9007181197365832, 0.9752496697792675, 0.013976390204770367, 0.39040296284368586, 0.42825081431510703, 0.0] |
| 0.1124 | 25.0 | 1250 | 0.5783 | 0.4085 | 0.4818 | 0.8809 | [nan, 0.8123818380472958, 0.8869254012115516, 0.5989965500806077, 0.6513288286982387, 0.45923979621249245, 0.0, 0.551056327882726, 0.7019146834355392, 0.2950008215576734, 0.8706733575298916, 0.3601874581566615, 0.0, 0.10517468206402572, 0.08712413261372398, 0.6136850006388144, 0.0, 0.2600985221674877, 0.7849825834204975, 0.17919511788917702, 0.45289730566932423, 0.5903637402399543, 0.0, nan, 0.18690435558822757, 0.42362687815353783, 0.43259719089833193, 0.0, 0.8841707465292419, 0.8032936112469397, 0.952030831872504, 0.008140849441390317, 0.16554455213884192, 0.3617462711649899, 0.0] | [nan, 0.9178324592492587, 0.9561686622912909, 0.7680310658482571, 0.7215460770544782, 0.5924548254023589, 0.0, 0.6491584679315913, 0.8452550030151549, 0.35430079676361037, 0.9581720479074639, 0.410135404944277, 0.0, 0.10532350931980092, 0.11548288196218702, 0.763316547977315, 0.0, 0.2600985221674877, 0.9177799037685564, 0.22825214031366012, 0.572804752898559, 0.6994932257437348, 0.0, nan, 0.31308822235904654, 0.5407402476367994, 0.5353727961089925, 0.0, 0.9583768797437656, 0.8894811289823983, 0.976516152184038, 0.010158989218608448, 0.379761952685748, 0.458744875997832, 0.0] |
| 0.1 | 30.0 | 1500 | 0.6125 | 0.4071 | 0.4817 | 0.8777 | [nan, 0.7976347312880722, 0.8842065126488408, 0.6220522211975981, 0.5992989007197456, 0.4754131699628208, 0.0, 0.5620206554196702, 0.7103054176260091, 0.3001608040201005, 0.8696414262339918, 0.2710134279595442, 0.0, 0.10693402202514375, 0.19945219123505975, 0.6229581109493774, 0.0, 0.21330049261083744, 0.7784639440974739, 0.1842071699891868, 0.4662422580117327, 0.5517361225824782, 0.001549819657348963, nan, 0.17199259716224552, 0.43358794468966694, 0.4268464617063853, 0.0, 0.8891718707035294, 0.8054920070330026, 0.9535609872146814, 0.01007383935063937, 0.16253665133576994, 0.3658318614584579, 0.0] | [nan, 0.8697241860632949, 0.9524319715036934, 0.8257718568242948, 0.7468530628299254, 0.5881267793852769, 0.0, 0.7164141181490659, 0.8437754352203041, 0.3683613310639138, 0.9597225061081064, 0.31468036446800013, 0.0, 0.10708012101102762, 0.20464997445068983, 0.7651242017023728, 0.0, 0.21330049261083744, 0.9302847679052355, 0.2516781574361694, 0.5984553495582629, 0.5925001383659759, 0.0015499506833873467, nan, 0.27588178379804734, 0.5562888715598076, 0.518736527938982, 0.0, 0.9534904946715259, 0.896063924459724, 0.9793106212730868, 0.011784150870325931, 0.3741529460703407, 0.47874361308587277, 0.0] |
| 0.0886 | 35.0 | 1750 | 0.6327 | 0.4115 | 0.4892 | 0.8822 | [nan, 0.8188467619727383, 0.8891141466002311, 0.6466411212625193, 0.6192450697021801, 0.4878651026475247, 0.0, 0.5804609572704323, 0.6873373994573425, 0.24242875689020368, 0.8707606811583432, 0.23605331403413546, 0.0, 0.13050222997866978, 0.2175902389425521, 0.6145514015738078, 0.0, 0.21711822660098523, 0.7803908730722577, 0.17679611946673174, 0.4549480658658346, 0.5467616324171395, 0.03352848701685911, nan, 0.19210202055245182, 0.44554925412112634, 0.43457869634340224, 0.06254767353165523, 0.8901464405497997, 0.8074494955970959, 0.9551576666105007, 0.009091384084852917, 0.16846681832699967, 0.3645371672657186, 0.0] | [nan, 0.9119479474792671, 0.9590241346815159, 0.845415986574404, 0.6953594791245139, 0.6061356109464877, 0.0, 0.7276180593606199, 0.892360619111798, 0.28098867756974766, 0.9616378091517278, 0.2841688750136131, 0.0, 0.1313555186883966, 0.21870209504343383, 0.7725733241957431, 0.0, 0.21711822660098523, 0.9161171536509721, 0.21953178271081142, 0.5994171169644333, 0.6121438495259369, 0.034859799915457235, nan, 0.3531093323951095, 0.6003076440268559, 0.6043221135245676, 0.06269113149847094, 0.9560401237295135, 0.884834427780536, 0.9792357012514029, 0.010829800623785451, 0.34680568415120167, 0.46030641401411304, 0.0] |
| 0.0795 | 40.0 | 2000 | 0.6240 | 0.4282 | 0.5017 | 0.8835 | [nan, 0.8168639361241289, 0.8876591799999074, 0.6570827724213207, 0.6202745367509233, 0.48734716072991435, 0.0, 0.5833200793037147, 0.7249773695346732, 0.31780959887896304, 0.8734250949568915, 0.2279957413675295, 0.0, 0.19478847928505513, 0.2588001983143282, 0.6266940289672047, 0.0, 0.3150246305418719, 0.7870743183835168, 0.18024107181885737, 0.48180217328687497, 0.5880553963585522, 0.042404523149135905, nan, 0.17844859516376527, 0.45068592007174485, 0.44004742517113327, 0.19134396355353075, 0.892022331516544, 0.8143712718909341, 0.9551036492731949, 0.016888403579096854, 0.15958069694966476, 0.36017381107545093, 0.0] | [nan, 0.8991722677575189, 0.9610711923215693, 0.8649585814233277, 0.7118098889111815, 0.594659810586253, 0.0, 0.7184642464033051, 0.8603538440753031, 0.3580502725770246, 0.9623214298952487, 0.26042763277307873, 0.0, 0.1956914218795745, 0.26673479816044965, 0.788603835801476, 0.0, 0.3150246305418719, 0.9230146256606502, 0.2499204485188272, 0.6278490409879275, 0.6625473814771242, 0.04480766521065239, nan, 0.29663998592664265, 0.6117266104950834, 0.5436080252031172, 0.1926605504587156, 0.9509771523653007, 0.887874399303051, 0.9819309132416605, 0.02064301076756039, 0.34012318344672116, 0.46386756263254, 0.0] |
| 0.0754 | 45.0 | 2250 | 0.6471 | 0.4302 | 0.5116 | 0.8840 | [nan, 0.8281984303346407, 0.8897375767546668, 0.6335678497580041, 0.6461049225195123, 0.4896718508137295, 0.0, 0.5769963172973805, 0.7160045601555046, 0.31492773499314275, 0.8789298786291031, 0.41197707824430413, 0.0, 0.19778300628229073, 0.19288119288119288, 0.6158351667955045, 0.0, 0.26785714285714285, 0.7860686941589031, 0.17655380387956127, 0.40860437517167547, 0.5549189258475934, 0.060120717954148355, nan, 0.1768935762224353, 0.45137771772158236, 0.44662611174687306, 0.24400299850074963, 0.8917308479385957, 0.8178316117483762, 0.9546822647246874, 0.0181622066651208, 0.17782411648425822, 0.3692233084050129, 0.0] | [nan, 0.9127907293988842, 0.9579732772469148, 0.8546614098408393, 0.7189306666878257, 0.617758410318982, 0.0, 0.7117038660531152, 0.8630197023070054, 0.3681886578356644, 0.9609314187010253, 0.48673176752459435, 0.0, 0.19893627403142383, 0.2049054675523761, 0.8228995957609527, 0.0, 0.26785714285714285, 0.9313026975574736, 0.22976740662171377, 0.482567055983181, 0.7372479787923986, 0.06399887276313936, nan, 0.30833846424487643, 0.6003932327823953, 0.6147681423755044, 0.24885321100917432, 0.947219534571164, 0.890392783205778, 0.9840024279813396, 0.0241042593066438, 0.3959314574024127, 0.47575603698227187, 0.0] |
| 0.0746 | 50.0 | 2500 | 0.6936 | 0.4117 | 0.4867 | 0.8749 | [nan, 0.7957936899551392, 0.8814366206724774, 0.5436114176098814, 0.6151632247714599, 0.4361122655202057, 0.0, 0.5671206613898421, 0.7141211613500584, 0.3419340943355589, 0.870823541579283, 0.1755482015278508, 0.0, 0.14690036810414178, 0.3004324599338591, 0.6098619199234538, 0.0, 0.16824615384615385, 0.7756330550603614, 0.17781881780267358, 0.4502871856554716, 0.45687245610992666, 0.06802365130029826, nan, 0.19639260088210125, 0.4534812252031405, 0.42577189666036547, 0.27414561664190196, 0.8856918914231561, 0.8034178358523514, 0.9553431034562543, 0.003146721773436032, 0.12501083138368427, 0.36698838817524204, 0.0] | [nan, 0.9180616367888037, 0.9472426408781908, 0.8013172716614175, 0.7302314913997886, 0.524968334204869, 0.0, 0.7028351702702309, 0.8539511709675187, 0.39162288166950343, 0.9630569443900208, 0.20604784550041746, 0.0, 0.14701863960183467, 0.30173735309146654, 0.7343444275597668, 0.0, 0.16834975369458127, 0.9160513108532854, 0.22030456852791877, 0.6251207408000449, 0.5084687072928094, 0.0732703959419473, nan, 0.3280191749494239, 0.6051695608345855, 0.5601890233792074, 0.28211009174311924, 0.9506023739291599, 0.8749006566683216, 0.9851772884487643, 0.003475079702076749, 0.25166727050709176, 0.48520419707741125, 0.0] |
| 0.067 | 55.0 | 2750 | 0.6778 | 0.4277 | 0.5121 | 0.8816 | [nan, 0.8194645919335458, 0.8855287302003849, 0.6053350056000855, 0.654773528870809, 0.4697667824136534, 0.0, 0.5710052174119353, 0.7267313389676074, 0.3551371282700238, 0.8755668722529796, 0.19964417520695182, 0.0, 0.13208006623484148, 0.3486218302094818, 0.6180969846096706, 0.0, 0.20360432519022828, 0.7807972584967618, 0.18003748362164762, 0.4432680689367132, 0.45560830868332836, 0.08040790777737207, nan, 0.1822721323375752, 0.45364137665335047, 0.45602216206006424, 0.36234396671289876, 0.8940119674114063, 0.8166972645181432, 0.9573128637395036, 0.03034622884202592, 0.18678678678678678, 0.3686953575810984, 0.0] | [nan, 0.9128102675762187, 0.9552788883754972, 0.8469619991264167, 0.7317413411289339, 0.5563598861193423, 0.0, 0.746876075856685, 0.8778384470140081, 0.40151459088778707, 0.9621413903500922, 0.24645151922169384, 0.0, 0.13233141407241145, 0.4039345937659683, 0.7933911385238819, 0.0, 0.31305418719211825, 0.9172193620842494, 0.246723236608834, 0.6190652452610861, 0.49203224849677785, 0.08688178103423982, nan, 0.36441199753716247, 0.5990761169332598, 0.6004808489471066, 0.39946483180428133, 0.9529255570362644, 0.9017875242386488, 0.9826782046681377, 0.03842297079549934, 0.3380298699730285, 0.48104842741150405, 0.0] |
| 0.0687 | 60.0 | 3000 | 0.6879 | 0.4291 | 0.5100 | 0.8823 | [nan, 0.8196948326057852, 0.8831657666830767, 0.6467890499563872, 0.6516417841503617, 0.4681981224281317, 0.0, 0.5880231738461575, 0.724187852815783, 0.2984189272432753, 0.8789400109991544, 0.2520251481078467, 0.013058335367341958, 0.10452562571588721, 0.3387726959319697, 0.616015263989506, 0.0, 0.29198813056379824, 0.784720416853429, 0.1792090810910177, 0.44576935641947074, 0.48202529113784476, 0.09516336506303061, nan, 0.18832282614869086, 0.45650264775637484, 0.4556005895357406, 0.2745886654478976, 0.8952007693743541, 0.8138636450290234, 0.9572388978933325, 0.010588595444700982, 0.1924104882672224, 0.35264721130282095, 0.0] | [nan, 0.9097946995213146, 0.9621478252989295, 0.8651175889100899, 0.7142492257108215, 0.566678868165708, 0.0, 0.7143145307931398, 0.867036613536233, 0.32265226078590986, 0.9605385088701248, 0.3026826877699931, 0.020373191165270373, 0.10464038255099053, 0.3765968318855391, 0.820913163096054, 0.0, 0.30295566502463056, 0.9221738131341896, 0.23635123873020683, 0.6270817065600855, 0.5335377838453821, 0.1042412286881781, nan, 0.364565924883455, 0.6191561899689979, 0.615072127342066, 0.2870795107033639, 0.9511700815454721, 0.8878140719993255, 0.9816183488199858, 0.012797283559588108, 0.43857601009084446, 0.4393736482895436, 0.0] |
| 0.0629 | 65.0 | 3250 | 0.6960 | 0.4222 | 0.4985 | 0.8831 | [nan, 0.8385937711298211, 0.8847888472425782, 0.6149328797554199, 0.6525561252288588, 0.48169461209819614, 0.0, 0.5971548536892575, 0.7135824408049566, 0.27369317672375143, 0.8823153606699299, 0.36233237512950345, 0.0, 0.14186935456382538, 0.24867603039373704, 0.6160654277501113, 0.0, 0.08275862068965517, 0.7805731162375585, 0.17752144045477705, 0.44269702931252913, 0.48059292296084216, 0.08923905090414969, nan, 0.17907819011708673, 0.4873286783174559, 0.4527498618417013, 0.22163588390501318, 0.8943575787945166, 0.8201429759960558, 0.9581956395009911, 0.019349515805194163, 0.1776665799886147, 0.3600628431614535, 0.0] | [nan, 0.9267115927398949, 0.9487909172436692, 0.8034188525406715, 0.7109193193887602, 0.6603282784265877, 0.0, 0.725064612012743, 0.8812655082760235, 0.3011914452749204, 0.9638660632870772, 0.45070243583693326, 0.0, 0.14216356006636088, 0.27593254982115484, 0.7556489178908058, 0.0, 0.08275862068965517, 0.9325111323710189, 0.2190847791499356, 0.625637495828009, 0.527886775476724, 0.09623784697759617, nan, 0.31748614653883367, 0.6121141373604427, 0.6112861327585254, 0.22477064220183487, 0.9484829229283243, 0.8975837228691066, 0.9804554182923197, 0.025625687235911233, 0.3392241321471224, 0.46249546141014647, 0.0] |
| 0.0629 | 70.0 | 3500 | 0.7101 | 0.4217 | 0.4989 | 0.8789 | [nan, 0.786640982710835, 0.8880498247990368, 0.6213814597589751, 0.6071277471550605, 0.4592909171926279, 0.0, 0.5867507688789444, 0.7333167906428527, 0.3791430524621254, 0.8814043667546686, 0.28100956352915796, 0.0, 0.0984556925025576, 0.3509064388414253, 0.6349712777519019, 0.0, 0.0, 0.7875471953847744, 0.1780650489932298, 0.4450250049891278, 0.4999114269705531, 0.08133596346637123, nan, 0.18414185986147352, 0.4677542129328365, 0.45241313162139773, 0.28850405305821664, 0.8909480603280158, 0.8200828649597152, 0.9579545152813692, 0.007145844060159359, 0.17539286131557424, 0.37038789587688453, 0.0] | [nan, 0.8598412127047438, 0.9543510233299178, 0.8082120661777665, 0.7612604902628672, 0.5904985183894021, 0.0, 0.7265619620716575, 0.8569696210790629, 0.424110116184415, 0.9631369031291932, 0.35466656986241696, 0.0, 0.09861422855469894, 0.43025038323965253, 0.8124211634536118, 0.0, 0.0, 0.9232874343190659, 0.2156072429729525, 0.6237066827758375, 0.5406547619892345, 0.08482457376356208, nan, 0.2963981000967543, 0.6442741122544078, 0.57353672691096, 0.2993119266055046, 0.9601885858498842, 0.8969635881631085, 0.9813282126850572, 0.008685970359817705, 0.3223031815681065, 0.45102269558033437, 0.0] |
| 0.056 | 75.0 | 3750 | 0.6888 | 0.4319 | 0.5074 | 0.8864 | [nan, 0.846983759179929, 0.8871265021170364, 0.6327919532904038, 0.6690289787883766, 0.4809385638926465, 0.0, 0.5929931910773564, 0.7319858245513943, 0.3873577190849818, 0.8821459096044979, 0.31863963925997724, 0.0, 0.23505840639191783, 0.3168200047180939, 0.6339963432877168, 0.0, 0.0, 0.7891815340906951, 0.16853589090364154, 0.44962094152977145, 0.5116482092488317, 0.10324211857041271, nan, 0.19139417066912298, 0.46438574150773454, 0.4679743443307121, 0.26584176977877766, 0.893033114012553, 0.8167232339927487, 0.958758389465055, 0.00683255888015518, 0.17629150606516764, 0.37230474365117394, 0.0] | [nan, 0.9383299434889024, 0.9547491546521122, 0.8273944994904098, 0.7246575916990003, 0.6112474580210331, 0.0, 0.7317551677487866, 0.879380624581915, 0.4323244283282765, 0.9640948194150409, 0.41171452426761534, 0.0, 0.23614228554698935, 0.34312723556463975, 0.7955762144552705, 0.0, 0.0, 0.9198500013278298, 0.21301613758618076, 0.6485306793405083, 0.5580917132262262, 0.11046921234324363, nan, 0.3198390359750198, 0.6043089183483272, 0.5887636102360029, 0.27102446483180426, 0.9532878705576775, 0.8931910708096411, 0.9816932688416696, 0.008160386166070774, 0.350442145377937, 0.47428658629635284, 0.0] |
| 0.0545 | 80.0 | 4000 | 0.7242 | 0.4313 | 0.5097 | 0.8839 | [nan, 0.8315018755718794, 0.8905184158955881, 0.5801625429382188, 0.6532970384376523, 0.4694179481073208, 0.0, 0.5983799840636467, 0.7235855215136249, 0.3640520350334879, 0.8784869607735561, 0.3143670199951819, 0.0, 0.2781527188584651, 0.3326551373346897, 0.6281559683282705, 0.0, 0.08645320197044334, 0.7821189057727206, 0.19111444811384393, 0.4452253857934852, 0.4994405348435919, 0.10157298545122671, nan, 0.17629709076283684, 0.46700401281623927, 0.4615519817207136, 0.2734785875281743, 0.8899163053914229, 0.8095455355998507, 0.9581430685733312, 0.005790762673569464, 0.17969789570113207, 0.36411010043900494, 0.0] | [nan, 0.9123876444791007, 0.9612296601404773, 0.8930262764661256, 0.7126551176008956, 0.5714955551682064, 0.0, 0.7523059093928652, 0.865652608026573, 0.41833789684007994, 0.9665973690927172, 0.37897048680437073, 0.0, 0.2815458182882795, 0.41773122125702605, 0.7517058490509818, 0.0, 0.08645320197044334, 0.9215057282136607, 0.24684445791347828, 0.6202401611194349, 0.5561602661167979, 0.10663660701704945, nan, 0.2891635148210045, 0.6095369648325313, 0.5805560161388382, 0.2782874617737003, 0.9568961863731891, 0.870963644368671, 0.9845664755331252, 0.007178373593543613, 0.36061350187190533, 0.46088130206223, 0.0] |
| 0.05 | 85.0 | 4250 | 0.7236 | 0.4310 | 0.5096 | 0.8865 | [nan, 0.8344804679717858, 0.891480804753714, 0.6039392215856049, 0.6561901191296589, 0.5040396418009069, 0.0, 0.5972644983662688, 0.7352912849624103, 0.4166594809002328, 0.882374306124748, 0.291759692976696, 0.0, 0.11696789594193015, 0.4100259636508888, 0.6420473687097001, 0.0, 0.0, 0.7822126517859589, 0.18499892874997023, 0.45949977357159744, 0.5246592278602004, 0.10855595092676192, nan, 0.18756695799266987, 0.4678528011435098, 0.4557543571262987, 0.2325056433408578, 0.8913224348625648, 0.8136362687377343, 0.9598605495290813, 0.008994566889922168, 0.1923180020267399, 0.3698758474475382, 0.0] | [nan, 0.9238238149259353, 0.9605341564359651, 0.8564066606895178, 0.714878329764632, 0.6240479925628958, 0.0, 0.7253836717079392, 0.8553615384868866, 0.47677545080046374, 0.96226053416674, 0.36290703161868804, 0.0, 0.11715624085098078, 0.5245273377618804, 0.8139308522789349, 0.0, 0.0, 0.9272880427065164, 0.23551026592923707, 0.5960753651336961, 0.5733261619548913, 0.11520360715795407, nan, 0.29259389568123845, 0.634985354812941, 0.5976344442602112, 0.23623853211009174, 0.9580478059949592, 0.8761671553428071, 0.9800020805814939, 0.011116797255897263, 0.39472377655220536, 0.470034782700211, 0.0] |
| 0.0483 | 90.0 | 4500 | 0.7448 | 0.4348 | 0.5119 | 0.8858 | [nan, 0.8389020217362697, 0.8904583684155554, 0.6053893552299984, 0.6609445788027536, 0.48826307798392343, 0.0, 0.5990805851530085, 0.741553407283815, 0.3904125924159313, 0.8810578364409596, 0.24072208997131173, 0.007595345830639948, 0.11408382066276804, 0.3854978354978355, 0.6358003169572107, 0.0, 0.3205665024630542, 0.7799325512458637, 0.18157179971658008, 0.44179222083868513, 0.4810432700260739, 0.10200241902970031, nan, 0.17958766620104505, 0.47953821940837715, 0.46267085062022195, 0.20652173913043478, 0.8936165310088457, 0.8196186094828226, 0.9601551959806593, 0.007783159441927215, 0.17946660884648744, 0.3712830781592127, 0.0] | [nan, 0.9268645537858738, 0.9579552943101062, 0.8624259561522487, 0.7130170885820071, 0.6134222299692057, 0.0, 0.7456444472460493, 0.8743388902252963, 0.44418954586940973, 0.9629775151789223, 0.28632881983519076, 0.00894897182025895, 0.11422855469893628, 0.45503321410321923, 0.7969369208307261, 0.0, 0.3205665024630542, 0.9184567677287768, 0.2329797711947875, 0.6319321335328264, 0.5199750799329599, 0.10694659715372692, nan, 0.30532588618172224, 0.6374674287235863, 0.6071132482175426, 0.2106269113149847, 0.9560636685684433, 0.8940191660968048, 0.9818139998320264, 0.009301457113021348, 0.3331991465721992, 0.46443061088103893, 0.0] |
| 0.0488 | 95.0 | 4750 | 0.7572 | 0.4392 | 0.5164 | 0.8870 | [nan, 0.8412265993316759, 0.8902791647105773, 0.6166091899398941, 0.6573127590169391, 0.49795139519110443, 0.0, 0.6045930992650757, 0.740872213808363, 0.3893914038172305, 0.8838233368096821, 0.33872329970362863, 0.004128819157720892, 0.1232210193407128, 0.36835222319093286, 0.6420211202135859, 0.0, 0.2602216748768473, 0.7833929304386752, 0.17934607063412256, 0.4671484042901698, 0.5449281805918343, 0.09757754723390911, nan, 0.1862480907024973, 0.4739074459454693, 0.46393408427200666, 0.20655861289106672, 0.8908646555131348, 0.8077701092850268, 0.959734031170495, 0.015509419333207602, 0.19220623899538222, 0.36528917777672343, 0.0] | [nan, 0.9329796512523355, 0.9594185059351048, 0.832704966397695, 0.7156041609282175, 0.6057294753355412, 0.0, 0.740442513492152, 0.8672541001163223, 0.4534398973827672, 0.964824509100999, 0.4003702762551276, 0.00476009139375476, 0.1235727530008783, 0.4317833418497701, 0.8025088644557671, 0.0, 0.2602216748768473, 0.9244890653768502, 0.22295628456701266, 0.6153075940114643, 0.6122502848919965, 0.10522756094124278, nan, 0.32980033424223765, 0.6388606234665348, 0.6146299673907036, 0.20948012232415902, 0.9577606974590687, 0.8682935054472558, 0.9823331908103197, 0.02047357902089197, 0.388175462608859, 0.4557849260933397, 0.0] |
| 0.0466 | 100.0 | 5000 | 0.7516 | 0.4340 | 0.5089 | 0.8868 | [nan, 0.8369914869418346, 0.8917253025027853, 0.63431934846412, 0.6595590976640465, 0.490185886416082, 0.0, 0.6019878455204862, 0.7389529158865543, 0.34824032232931906, 0.8841782288939659, 0.3149823779040495, 0.0, 0.1793690267212795, 0.3540386803185438, 0.6423088361774469, 0.0, 0.145935960591133, 0.7781632167836338, 0.18123317726357693, 0.45431638450718936, 0.5090139572607015, 0.10249373268241192, nan, 0.1875506294119916, 0.501633275054173, 0.45008636966215404, 0.17736422331940752, 0.8917030821290204, 0.8118398661365593, 0.9594706627009374, 0.014780075321537696, 0.20062550586608202, 0.37857391883524044, 0.0] | [nan, 0.9373271597386813, 0.9596797489625617, 0.8314003387051043, 0.7185675621858967, 0.5884759746673639, 0.0, 0.7444904015400207, 0.8778911710334237, 0.3858999975332396, 0.9637834569075349, 0.3974298471702908, 0.0, 0.17949155850492826, 0.397547266223812, 0.7936692390969677, 0.0, 0.145935960591133, 0.9165776142827953, 0.24282142586559588, 0.6377640831341348, 0.5628898195281933, 0.10945469916866281, nan, 0.3207406104318761, 0.6268758202255739, 0.6192450118830487, 0.17851681957186544, 0.9569449380396788, 0.8769881312587235, 0.9830475556030632, 0.01869973236699608, 0.34259221985158944, 0.47854628309223995, 0.0] |
| 0.0681 | 105.0 | 5250 | 0.7608 | 0.4243 | 0.4961 | 0.8801 | [nan, 0.8053305712022708, 0.8888831373349202, 0.6063781727951514, 0.6458484552441548, 0.4450952774354321, 0.0, 0.5835976764940738, 0.7449298281412959, 0.38801677910396126, 0.8805089961159074, 0.14255831144524309, 0.0, 0.1778948138395143, 0.3797164667393675, 0.6438507708603036, 0.0, 0.2848522167487685, 0.7757003332539172, 0.14560873446405273, 0.46351390150988186, 0.47026329896747027, 0.08670882625524723, nan, 0.16717484516436398, 0.49040240585388206, 0.4269185360094451, 0.09782193351165457, 0.8929769955183823, 0.8046204535691968, 0.9590862138793831, 0.04553666153467317, 0.1919049851539303, 0.36759942734721646, 0.0] | [nan, 0.8461725854729251, 0.9657024524747764, 0.8717211889928504, 0.7386199232908679, 0.5728516646330835, 0.0, 0.7229524174348182, 0.8661468957085944, 0.44266015441920126, 0.9636971438314745, 0.16451882237630233, 0.0, 0.17800331804430566, 0.44481349003576903, 0.8150531867346027, 0.0, 0.2848522167487685, 0.9260951906884237, 0.2249185544359421, 0.6512735360080518, 0.5153941017777545, 0.0896435113428209, nan, 0.23148473920309615, 0.6005358807082946, 0.49964074503951805, 0.09785932721712538, 0.9555801683760682, 0.8920875682663394, 0.9854006169210447, 0.0684193055373061, 0.28012828254364425, 0.47628225029862603, 0.0] |
| 0.0668 | 110.0 | 5500 | 0.7138 | 0.4340 | 0.5140 | 0.8742 | [nan, 0.7871483106350147, 0.8799748398030087, 0.6039422540580079, 0.58793837643889, 0.4164255041075429, 0.0, 0.6184209066896527, 0.7402801021253262, 0.3308593247243554, 0.8857427628712552, 0.35066959646049234, 0.0, 0.16199673226522301, 0.42935960591133004, 0.6284724323670036, 0.0, 0.3552955665024631, 0.7640465559057021, 0.1673140841039061, 0.4603793394796352, 0.4502083383450174, 0.08286035553651745, nan, 0.19144741314841254, 0.494703324736749, 0.49196363166286033, 0.21928518242740133, 0.8942953842754613, 0.8018772529737324, 0.9608524553067362, 0.025030461104976583, 0.16785196891874093, 0.3735661360500572, 0.0] | [nan, 0.8648334810431274, 0.9433503159465763, 0.7861368460577638, 0.8401580732564278, 0.456157108825751, 0.0, 0.7569977355489718, 0.8541785433012485, 0.38047312464540317, 0.9656267441330937, 0.428703670091117, 0.0, 0.1620718259002635, 0.5567194685743485, 0.8251045360189903, 0.0, 0.3552955665024631, 0.9128087725432023, 0.21700886430790212, 0.6164003697345833, 0.5046228427325222, 0.08721995209243343, nan, 0.3096138622570147, 0.6316283736234475, 0.6310175205880727, 0.22515290519877676, 0.9574614010065557, 0.8952916600312878, 0.9807011750513465, 0.036369043090988304, 0.3078378487178455, 0.47336308192615123, 0.0] |
| 0.0456 | 115.0 | 5750 | 0.7481 | 0.4396 | 0.5149 | 0.8874 | [nan, 0.8535949387776991, 0.889196790918221, 0.6590754161484988, 0.6643237184774637, 0.46255227979529023, 0.0, 0.6160656034941906, 0.7414819627132849, 0.33609977221984166, 0.881638905287202, 0.26364535016348567, 0.0, 0.11007294284111147, 0.47720425788310905, 0.6368556033975671, 0.0, 0.32869458128078816, 0.7703600738384895, 0.17442321190028753, 0.46530941552214283, 0.48260002610416075, 0.09418922868453915, nan, 0.20518864654252, 0.4743353551385976, 0.4722508031833358, 0.20610399397136397, 0.8954748076190832, 0.8187194150221221, 0.9605552926063987, 0.012601025462761798, 0.17920223292081403, 0.3762309075548745, 0.0] | [nan, 0.9413675139957597, 0.9627770101122414, 0.853864456654176, 0.7242582145309057, 0.5528162221834872, 0.0, 0.7381053284908671, 0.8687863919305888, 0.3676213029428452, 0.9679646105797591, 0.3146622136711802, 0.0, 0.11008099931687323, 0.6070516096065406, 0.8065015941122136, 0.0, 0.32869458128078816, 0.912257229374579, 0.23346465641336464, 0.6230433232027166, 0.5299729086514923, 0.09990136677469354, nan, 0.3254024100624505, 0.6366091637027598, 0.621511081633781, 0.2090978593272171, 0.9563050724169996, 0.8984035746737735, 0.9820063104609347, 0.01504138975525757, 0.32565785059646013, 0.47864626362234725, 0.0] |
| 0.0432 | 120.0 | 6000 | 0.7519 | 0.4416 | 0.5185 | 0.8876 | [nan, 0.8517831570119985, 0.8901004311397058, 0.6339355013970817, 0.6606286462755991, 0.4746063751504886, 0.0, 0.6132450026307165, 0.7426311341925447, 0.3602046617396248, 0.8859214231639748, 0.3273784162152292, 0.0, 0.15872087354977088, 0.4255713403335392, 0.6326264779996124, 0.0, 0.35557744397931546, 0.7741301715457662, 0.17043647800201933, 0.46161159879531216, 0.5113488607281433, 0.11327498751609766, nan, 0.19760381654559253, 0.47813157752711966, 0.46921250159026334, 0.1416030534351145, 0.8955479192568264, 0.8197854779969181, 0.9604275470620833, 0.010892456172159384, 0.18561124493594658, 0.3689976212003217, 0.0] | [nan, 0.9296893165774394, 0.9616835385667785, 0.87624044997203, 0.7260692029572803, 0.5797304049735634, 0.0, 0.7494101274784102, 0.8745695578102397, 0.39073484792422114, 0.9642129041755406, 0.3904962427850583, 0.0, 0.15887576851761492, 0.528104241185488, 0.8103950021354152, 0.0, 0.3556650246305419, 0.9162409381106233, 0.22253201000075765, 0.6204044413898943, 0.5625662560153721, 0.12145977173453572, nan, 0.3206086727064825, 0.6318803849592027, 0.6115348477311667, 0.14181957186544342, 0.9576213674122256, 0.8924536538299407, 0.9825164346850114, 0.013502672872248463, 0.3639547522241456, 0.4569004983240106, 0.0] |
| 0.0446 | 125.0 | 6250 | 0.7468 | 0.4334 | 0.5064 | 0.8877 | [nan, 0.8499567507325978, 0.8871076417101389, 0.6330569753090723, 0.6639770881242221, 0.4871746836767682, 0.0, 0.5980424732505424, 0.7360705192073508, 0.30519138810716817, 0.8812845049064242, 0.23256457139345144, 0.0, 0.13761825807080855, 0.4344916900496439, 0.6344221105527639, 0.0, 0.31022167487684726, 0.7799696347321634, 0.17147761834567948, 0.4735415094048958, 0.5082152629506022, 0.10032137118371719, nan, 0.19083052625766195, 0.477693792160024, 0.4774453072902102, 0.10550458715596331, 0.8982375671163275, 0.8273146135730871, 0.9607895023001171, 0.016035198543508544, 0.15227804315598747, 0.37272481048329426, 0.0] | [nan, 0.9294944628629415, 0.9603275161439091, 0.8696425971478271, 0.7134799429158917, 0.6058991342745919, 0.0, 0.7261197395153978, 0.8763951269825055, 0.32904117023113544, 0.9650643853185165, 0.2747304606672233, 0.0, 0.13769883868449304, 0.5143076136944302, 0.7674085992670063, 0.0, 0.31022167487684726, 0.9269199814674473, 0.20887946056519432, 0.6072557812618596, 0.5566839281178112, 0.10556573199943638, nan, 0.3039625296859882, 0.6508858436198338, 0.6133587575305367, 0.10550458715596331, 0.9551001306600062, 0.9014341786025424, 0.9824066792392325, 0.020100137620071783, 0.3101324423332394, 0.48336771260333516, 0.0] |
| 0.0401 | 130.0 | 6500 | 0.7766 | 0.4379 | 0.5140 | 0.8867 | [nan, 0.8468760227965516, 0.8886795707269431, 0.622437352951649, 0.6682970140214559, 0.4786959592750148, 0.0, 0.6085294389146897, 0.7427519649223919, 0.3908760790623845, 0.8822040839218181, 0.20753357844976364, 0.0, 0.17475089531512655, 0.47288964490750585, 0.6415406446381512, 0.0, 0.2750554050726422, 0.778568992850166, 0.17143968092188597, 0.46392364840506783, 0.4823894964669603, 0.09554546178978404, nan, 0.20017632982073136, 0.47654683547891147, 0.4713058003824428, 0.1655881233346022, 0.8956585893822123, 0.8232044008477167, 0.9608808597268595, 0.012288627559172788, 0.18044196123782585, 0.37141827889613904, 0.0] | [nan, 0.9354963797165556, 0.9559979333791044, 0.8707192502509636, 0.7183888437369763, 0.6083772006275057, 0.0, 0.7415955894118731, 0.866331429776549, 0.4434248501443055, 0.9662129317110005, 0.2510073692235089, 0.0, 0.17500243973846002, 0.5682166581502299, 0.7858128979072931, 0.0, 0.2751231527093596, 0.9165205505248, 0.2204712478218047, 0.6489507377535817, 0.5256828538301831, 0.10136677469353247, nan, 0.3145395373383763, 0.6396451870589802, 0.5925772398165036, 0.16628440366972477, 0.9558582744735443, 0.8949720377326676, 0.9824620341597123, 0.014785513239880775, 0.3475571300135529, 0.47879491888421727, 0.0] |
| 0.0532 | 135.0 | 6750 | 0.8100 | 0.4370 | 0.5099 | 0.8867 | [nan, 0.8418463475820702, 0.8855647993577028, 0.6407052153749961, 0.6672622261373646, 0.48550215050970236, 0.0, 0.6013553074721314, 0.7358587165510544, 0.41406543029797876, 0.8806464817122883, 0.20844846800909883, 0.0, 0.10624649381692236, 0.46624287593160896, 0.6367459896871661, 0.0, 0.2729064039408867, 0.7800250020493483, 0.16987653185041204, 0.47226725829848964, 0.5354231045094412, 0.10532085561497326, nan, 0.19529110166632935, 0.4793455617996517, 0.4643273310907372, 0.1317799847211612, 0.8929265734089717, 0.8098728542013477, 0.9610867606622594, 0.009269971902267766, 0.1905821312686735, 0.3815049812671639, 0.0] | [nan, 0.9263081557808802, 0.9609817135875093, 0.8755450316865522, 0.7097842872099934, 0.608116901981291, 0.0, 0.7151553355218178, 0.871465431167145, 0.49016995979180544, 0.9649383576369068, 0.24783097978001234, 0.0, 0.10627500731921538, 0.5434338272866632, 0.7518349671742002, 0.0, 0.2729064039408867, 0.918908888272893, 0.2238048336995227, 0.6329937167995292, 0.5943152161418457, 0.11100464985205016, nan, 0.31827777289119535, 0.6406199478859578, 0.5836235008014149, 0.13188073394495411, 0.9580930951851359, 0.8802967653698794, 0.9799166622128225, 0.011248193304333996, 0.3184117654952162, 0.4786317927561475, 0.0] |
| 0.039 | 140.0 | 7000 | 0.7955 | 0.4374 | 0.5145 | 0.8873 | [nan, 0.8453406127060666, 0.8894584400292076, 0.618765500137779, 0.6661462422914772, 0.48188110711842147, 0.0, 0.608878748711235, 0.7435697628283624, 0.3796956629902977, 0.8857966705291055, 0.3616908539636749, 0.0, 0.12437204311564161, 0.5013698630136987, 0.6370300461309403, 0.0, 0.18285784554845055, 0.7737808450225561, 0.16547070030804295, 0.47332405936901073, 0.47251187823235086, 0.09493722374379694, nan, 0.19320955193290454, 0.47309349183647703, 0.4585451464536432, 0.13724742661075104, 0.8963119205284326, 0.8287376073022066, 0.9613351708673005, 0.00971653416847346, 0.18365372022293688, 0.38471762753712496, 0.0] | [nan, 0.9325931121764209, 0.9570000189093305, 0.8775718982045564, 0.7170735817481989, 0.6105484864330951, 0.0, 0.7451100949905688, 0.8584129411105655, 0.45178716791238066, 0.9654472341160111, 0.4460376810541983, 0.0, 0.12442666146189128, 0.5610628513030148, 0.7776685239812083, 0.0, 0.183128078817734, 0.9154620178139884, 0.2112205470111372, 0.6293701931124976, 0.5103334549061737, 0.09866140622798365, nan, 0.31508927786084967, 0.6503865758791867, 0.6281711159011772, 0.13761467889908258, 0.9610215191517875, 0.9003190602429954, 0.981970520641659, 0.01171499505535923, 0.3387008037786992, 0.48129837873677234, 0.0] |
| 0.0406 | 145.0 | 7250 | 0.8306 | 0.4360 | 0.5141 | 0.8867 | [nan, 0.8435997939171356, 0.886366406157634, 0.6223465646375345, 0.6631770897769883, 0.4788596814657396, 0.0, 0.6085666309373553, 0.7410466976722848, 0.31492224002889196, 0.8837966051190714, 0.22238290725881693, 0.0, 0.13819236298949727, 0.5232347616173808, 0.6307999909800885, 0.0, 0.3076828669612175, 0.7764942343062243, 0.16667183036627153, 0.4750608982109485, 0.4864866269041335, 0.08490179473871118, nan, 0.1946730634021258, 0.47966615140417673, 0.46086619157494946, 0.12857687905379625, 0.8998584935109988, 0.8307591913787293, 0.9614240003370637, 0.006127383872241452, 0.19595372863270513, 0.37590210909466404, 0.0] | [nan, 0.9378495859578592, 0.954765284801492, 0.8295152378981893, 0.7149554091802339, 0.6165097902504213, 0.0, 0.7444147582080288, 0.8516346093644449, 0.3441624115049705, 0.9674811514482063, 0.25957454532253965, 0.0, 0.13835756806870303, 0.6645375574859479, 0.8335170783548365, 0.0, 0.308743842364532, 0.922605741887015, 0.2037881657701341, 0.6393476201715377, 0.5315353798252473, 0.08758630407214316, nan, 0.31051543671387105, 0.6595398177910493, 0.623721881390593, 0.12882262996941896, 0.9541870064066892, 0.8996719468670082, 0.9800173509043849, 0.007351263130960367, 0.3720998886249883, 0.47889753048090633, 0.0] |
| 0.037 | 150.0 | 7500 | 0.8222 | 0.4343 | 0.5077 | 0.8875 | [nan, 0.844207269702504, 0.8878295221933561, 0.6214984234657922, 0.6643742580050236, 0.48557575036716316, 0.0, 0.6097768299571183, 0.7465852256395515, 0.3695119182746879, 0.884482746916304, 0.229786147654232, 0.0, 0.10648001365753726, 0.45458553791887124, 0.6442341311464989, 0.0, 0.258520979451212, 0.7755187494699113, 0.17377147325464898, 0.4744249539706051, 0.5001041209924736, 0.08993947946915624, nan, 0.19405005327880656, 0.4817597684924271, 0.45507234290956095, 0.1162079510703364, 0.898116706658797, 0.8266099378191127, 0.9613809600564381, 0.008963162954562462, 0.1934702763543734, 0.37436200278398785, 0.0] | [nan, 0.9305498764782347, 0.9581999167444519, 0.848117198096508, 0.7216662302611518, 0.6072343268839695, 0.0, 0.7464716749664212, 0.8558986644346832, 0.40151459088778707, 0.9658946853385326, 0.27772534214252004, 0.0, 0.10651898116521909, 0.5268267756770567, 0.8052302772066784, 0.0, 0.258743842364532, 0.9231421412121703, 0.21550875066292902, 0.6330883339173254, 0.5487097904926255, 0.09339157390446667, nan, 0.29633213123405755, 0.648311048557354, 0.6032167136461615, 0.1162079510703364, 0.9569935512071162, 0.8966612022369814, 0.9819461835645514, 0.01076756039031542, 0.35058975081518456, 0.467014318264338, 0.0] |
| 0.0359 | 155.0 | 7750 | 0.8264 | 0.4336 | 0.5100 | 0.8876 | [nan, 0.8425150819450634, 0.8887259579748503, 0.6062849127025877, 0.6661436167605636, 0.477463082002611, 0.0, 0.608982398838398, 0.74429892821273, 0.3660286553193368, 0.8814051326012079, 0.18797448685125717, 0.0, 0.206084945843982, 0.4612220916568743, 0.6472122569202, 0.0, 0.1635491016490278, 0.7777400139827546, 0.16735784151426214, 0.4777184910568181, 0.5271252583451728, 0.1026913327220754, nan, 0.20569207071077533, 0.49218430887769665, 0.4574078290930921, 0.0779816513761468, 0.8958569293152772, 0.8268185544245148, 0.961547775435119, 0.016675747796079745, 0.1920671902330555, 0.3826628162758937, 0.0007393715341959334] | [nan, 0.9373452897590907, 0.9575174915394369, 0.8346226350031035, 0.7189249990837373, 0.592023705769566, 0.0, 0.743514796514588, 0.8620311269429625, 0.40898887491057995, 0.9669574481830303, 0.230551421207391, 0.0, 0.2065726554113399, 0.6016862544711293, 0.8009495148138216, 0.0, 0.16366995073891627, 0.9155667078623104, 0.2259489355254186, 0.6286330113925248, 0.5697087786470788, 0.10731294913343667, nan, 0.3083164746239775, 0.6503009871236473, 0.6106228928314817, 0.0779816513761468, 0.9592060735712507, 0.8998802821519236, 0.9815085933742069, 0.02063609518606372, 0.36394133354803215, 0.48287175655267134, 0.0011527377521613833] |
| 0.0335 | 160.0 | 8000 | 0.8518 | 0.4340 | 0.5059 | 0.8886 | [nan, 0.8436530764368111, 0.8895900440620743, 0.6082310506714941, 0.6647265197368698, 0.48458344251575175, 0.0, 0.6090840245108227, 0.7404627804506331, 0.38335284631867284, 0.8815549567555062, 0.18294506042107886, 0.0, 0.07282879016921051, 0.4207551435677142, 0.6530114804312678, 0.0, 0.3558657849620377, 0.7775443898061408, 0.17116698280457718, 0.4806890482304907, 0.4933879226304321, 0.09181473293485085, nan, 0.17767671317351422, 0.4911045514027132, 0.4719998327724242, 0.08830275229357798, 0.9007817953005852, 0.8305455831626325, 0.9611232513095775, 0.006788911045474309, 0.20454109523352834, 0.3848491020278139, 0.0] | [nan, 0.9335459063558043, 0.9603316031750019, 0.8547916810348131, 0.7148900428130813, 0.5963366451687874, 0.0, 0.7503401525473862, 0.8677483877983438, 0.42808160043414983, 0.9664967587586591, 0.2132355610411297, 0.0, 0.0728749878013077, 0.47547266223811957, 0.7982678307162083, 0.0, 0.3578817733990148, 0.9317873005484486, 0.20920524282142586, 0.6235621577277751, 0.5465768257567909, 0.09480061998027335, nan, 0.25343038086023395, 0.6593662628145387, 0.6239982313601945, 0.08830275229357798, 0.9526813832066575, 0.902749388764508, 0.9824663289380254, 0.007828438254230607, 0.3434241777706212, 0.48473192062598336, 0.0] |
| 0.0346 | 165.0 | 8250 | 0.8438 | 0.4379 | 0.5103 | 0.8883 | [nan, 0.8459468636033894, 0.8888331369606564, 0.6143356921364396, 0.6654980544147341, 0.48167853831328056, 0.0, 0.6135617243950853, 0.7453493425593741, 0.36501505490612823, 0.8871093023776453, 0.28924392439243923, 0.0, 0.11610167426217922, 0.44053678852383155, 0.6419692508995748, 0.0, 0.31108930323846906, 0.7764850703242182, 0.17769648792669843, 0.48261405652354455, 0.5041534749331448, 0.09703109762704519, nan, 0.1935639159166168, 0.4981157384329542, 0.45534552215680196, 0.08371559633027523, 0.8969250693293208, 0.8249491172270096, 0.9618063555393217, 0.009535384030237478, 0.19902344047093898, 0.3833148309593847, 0.0] | [nan, 0.9345559069102661, 0.95845124190979, 0.8289156072553392, 0.7178118816407789, 0.6027575387833363, 0.0, 0.7548031091349021, 0.8646673279137435, 0.4066947877352673, 0.9652041807300498, 0.34996551348604205, 0.0, 0.11622914023616668, 0.4864588656106285, 0.7796748209727561, 0.0, 0.31231527093596056, 0.925766196175982, 0.22965376164860973, 0.6295864608103177, 0.5586281474711666, 0.10094406087079047, nan, 0.2917802797079778, 0.6533227456872777, 0.6091029679986735, 0.08371559633027523, 0.9575749702296287, 0.8960586786072262, 0.9801156536079956, 0.011096050511407251, 0.3511399165358346, 0.48239553350137077, 0.0] |
| 0.0359 | 170.0 | 8500 | 0.8588 | 0.4298 | 0.5008 | 0.8882 | [nan, 0.843094419260262, 0.8900013429866321, 0.6133301326394077, 0.6661149601220273, 0.4853624310010443, 0.0, 0.6120054866295084, 0.7375298943289792, 0.3408351470819216, 0.8829721413070726, 0.22209681464760472, 0.0, 0.03861163959217523, 0.4175319971021492, 0.6376489814784245, 0.0, 0.28027511667894867, 0.7789104093843366, 0.17390202354217138, 0.47461354628029206, 0.516023356843965, 0.08927792321116929, nan, 0.18421222487575034, 0.4871304688103021, 0.45871426798494186, 0.05387848681696599, 0.8994123394635088, 0.8242101331834862, 0.9615335975044262, 0.007916133605582808, 0.22646747269605874, 0.37908474344043297, 0.0] | [nan, 0.9397440850808414, 0.9577213526503497, 0.8272086714637118, 0.7156158739766668, 0.6048939631630934, 0.0, 0.7494343721360998, 0.8668388984634243, 0.3747009053010681, 0.9680392740381917, 0.25716048934548225, 0.0, 0.03862105982238704, 0.441747572815534, 0.7673092776337614, 0.0, 0.2810344827586207, 0.9210972833920102, 0.22352450943253277, 0.6446638544934293, 0.5682428088718845, 0.09226433704382134, nan, 0.31379189022781245, 0.6451870589801624, 0.6128613275852539, 0.05389908256880734, 0.9556559273578009, 0.9013431255913293, 0.9796174593236774, 0.009692187467583211, 0.33278316761268334, 0.4804472286975694, 0.0] |
| 0.0342 | 175.0 | 8750 | 0.8689 | 0.4339 | 0.5051 | 0.8880 | [nan, 0.842207631443645, 0.8893284445771101, 0.6225399576081035, 0.6646476520665043, 0.48347573182283166, 0.0, 0.6145921797450942, 0.7331767170008916, 0.3267635558167394, 0.8840148558277702, 0.2103112515380292, 0.0, 0.10012921471584953, 0.3746216530849825, 0.6392775627666964, 0.0, 0.4631879914224446, 0.7770691785507862, 0.1792685215596115, 0.48551142385802487, 0.48582005237755577, 0.08915524176996963, nan, 0.18459143368114972, 0.48183353534471146, 0.4823333617820261, 0.029434250764525993, 0.897290929740743, 0.8192668128466759, 0.9613327742988569, 0.0055269961977186316, 0.2091533037018423, 0.3819620509014621, 0.0] | [nan, 0.9364984593883142, 0.9624452521749953, 0.8451305393993732, 0.7156570585663757, 0.5934239730404973, 0.0, 0.7383128627606906, 0.8535853980828229, 0.35661955154295866, 0.965347682838101, 0.2512977819726286, 0.0, 0.10020005855372303, 0.41108840061318347, 0.7677959536366616, 0.0, 0.47881773399014776, 0.9253472165067016, 0.23157814985983788, 0.6453594462715136, 0.5296791470411678, 0.09181344229956319, nan, 0.31548509103703054, 0.6294458603571904, 0.6247167412811585, 0.029434250764525993, 0.9557927644216986, 0.8822924375415687, 0.9825359997862155, 0.006282805789724829, 0.3573930196046858, 0.48477927982445523, 0.0] |
| 0.0621 | 180.0 | 9000 | 0.7787 | 0.4015 | 0.4924 | 0.8783 | [0.0, 0.8086755425666048, 0.8830559170088975, 0.5349712025714258, 0.645925544331418, 0.4397485010784333, 0.0, 0.6035436142733216, 0.7401548966695519, 0.27901830172394715, 0.8781545312615516, 0.15653466918823716, 0.0007045974986788797, 0.12723599990265033, 0.20456217807211186, 0.629064116632701, 0.0, 0.28005299927728255, 0.7801685900058292, 0.18456300860811892, 0.45049561474148564, 0.5454936336497989, 0.09604580812445981, nan, 0.13710408411674824, 0.4796006742513984, 0.4462842458656277, 0.08326967150496563, 0.895986048178371, 0.8195021626448673, 0.9584500399303424, 0.012936392680801627, 0.2073265351363334, 0.33898081262786167, 0.001953125] | [nan, 0.9020274819425876, 0.9445349555322843, 0.7582243269960229, 0.7115816733865559, 0.6725024693509964, 0.0, 0.7246456643278654, 0.8622486135230519, 0.3110091516810972, 0.9623436700743563, 0.19680908991904744, 0.0007616146230007616, 0.12754952669073874, 0.21308124680633622, 0.7971156997705671, 0.0, 0.28633004926108374, 0.9141247505929693, 0.2378134707174786, 0.6194613894575736, 0.6931652884469377, 0.10021135691137101, nan, 0.23282610607793122, 0.6372106624569679, 0.5951196595368374, 0.08333333333333333, 0.9429497472807788, 0.9053891766821857, 0.9799066410634253, 0.015400999993084419, 0.4015941387222737, 0.4187395086220052, 0.0023054755043227667] |
| 0.0374 | 185.0 | 9250 | 0.8500 | 0.4261 | 0.5005 | 0.8835 | [nan, 0.8434716396594377, 0.8889128861529657, 0.64763139635125, 0.6591157906879173, 0.47535724026979675, 0.0, 0.6200541090314029, 0.749098883299684, 0.3885603318916056, 0.8826306979452221, 0.1625372623759957, 0.0, 0.08342478113492818, 0.39311682016480853, 0.6380806324629313, 0.0, 0.22758620689655173, 0.7521926906996731, 0.17508827683615819, 0.39885397225233327, 0.46177267841868885, 0.09434473050163783, nan, 0.14603587039096305, 0.4816513597668971, 0.4814476488755492, 0.10313216195569137, 0.9008454163938971, 0.818761674014968, 0.9607465658084764, 0.006843049110009815, 0.22781082971393046, 0.39319498274838577, 0.0] | [nan, 0.9312379371557167, 0.9615129186420878, 0.8851103856793643, 0.708138414982727, 0.5974376852013248, 0.0, 0.7408003646396516, 0.865632836519292, 0.4343471718591973, 0.9632888776864283, 0.1811086506697644, 0.0, 0.08346345271786865, 0.41440981093510476, 0.772771967462233, 0.0, 0.22758620689655173, 0.8636544903580905, 0.22540343965451928, 0.6805965245365061, 0.5255778376023376, 0.09821051148372552, nan, 0.29167033160348316, 0.6413712269623599, 0.6117282927098878, 0.10321100917431193, 0.9538809235006024, 0.8999256213056552, 0.9828299535018667, 0.007811149300488931, 0.3540249319002187, 0.5106163536574456, 0.0] |
| 0.0312 | 190.0 | 9500 | 0.8366 | 0.4271 | 0.5011 | 0.8871 | [nan, 0.8383583648435936, 0.8893585287734083, 0.6242144991822743, 0.6523942357118304, 0.4788692097394316, 0.0, 0.6222419857542325, 0.7495553204266636, 0.3855623463905866, 0.8844989483482312, 0.21960980490245122, 0.0, 0.03046415766238201, 0.39732965009208104, 0.6460657345680039, 0.0, 0.16235120873726838, 0.7700717667212197, 0.16549668505209203, 0.49368437402670146, 0.46331160358515755, 0.09818201434967902, nan, 0.17114682596121936, 0.5135764361691169, 0.4659315099786098, 0.10504201680672269, 0.9002915149364578, 0.8254822330499596, 0.9604699442360148, 0.009150900078881995, 0.18152508685955304, 0.3910305542248974, 0.0] | [nan, 0.9283969805595136, 0.9598165282698033, 0.8767078936680537, 0.7034928688316159, 0.5971018534658068, 0.0, 0.7531496234804661, 0.8637248860666893, 0.4372826167394361, 0.9671062455718215, 0.2709187933350274, 0.0, 0.03047233336586318, 0.4409810935104752, 0.7719575300696244, 0.0, 0.1629310344827586, 0.9197306063880243, 0.22505492840366695, 0.6378119115673065, 0.5240721319571477, 0.10257855431872623, nan, 0.31753012578063156, 0.6519010213591494, 0.620018791797933, 0.10512232415902141, 0.9564885836615992, 0.9020052271173103, 0.9846571430752903, 0.010750271436573745, 0.32109550071789916, 0.49397091092787193, 0.0] |
| 0.0326 | 195.0 | 9750 | 0.8707 | 0.4272 | 0.4984 | 0.8861 | [nan, 0.8261617719245659, 0.8854917604179252, 0.6200336534230758, 0.660580250534605, 0.4498640011519204, 0.0, 0.6209593550575648, 0.7414471855553728, 0.34006487158979826, 0.8877441348891416, 0.23385327442671236, 0.0, 0.0332081728190374, 0.43202489229296315, 0.6361883362956504, 0.0, 0.1902200488997555, 0.7701853795262287, 0.15860467354944288, 0.49904952690861926, 0.46916590678565206, 0.09274864326815566, nan, 0.17989392302744164, 0.5138984658207596, 0.4806735961411222, 0.12204424103737604, 0.9008746454479115, 0.8221407501198316, 0.9611822232918834, 0.00719201457815406, 0.1665572869766945, 0.3941783403071965, 0.0] | [nan, 0.9130443736650012, 0.965349714444587, 0.8908710545070002, 0.7139688682285827, 0.5282453082331068, 0.0, 0.7562413022290538, 0.8614280959708963, 0.37758701497323566, 0.9644183610682487, 0.28246270011253494, 0.0, 0.03322923782570508, 0.46116504854368934, 0.7536724173892315, 0.0, 0.1916256157635468, 0.9292328194741926, 0.19972725206455033, 0.628904385763347, 0.5198814168108274, 0.0958433140763703, nan, 0.325930160964025, 0.6750836867831942, 0.6223677665395457, 0.12232415902140673, 0.9573260874322359, 0.8965555357795243, 0.9795764203309079, 0.007942545348925665, 0.29287602485138814, 0.49881075790503954, 0.0] |
| 0.0323 | 200.0 | 10000 | 0.8527 | 0.4345 | 0.5079 | 0.8871 | [nan, 0.8382620833593052, 0.8876413942052827, 0.6261839847460975, 0.6590417473673477, 0.48228357004057837, 0.0, 0.6202905105623743, 0.748344409080285, 0.39096811362981676, 0.8848513296576286, 0.2415092028297553, 0.0, 0.07068982339740462, 0.41356382978723405, 0.6474134903246308, 0.0, 0.3062052505966587, 0.7704161510118073, 0.16108765491481541, 0.49752934863906867, 0.4734664813860761, 0.09820294554789893, nan, 0.17153699720635862, 0.514555863370054, 0.4660696051735875, 0.08826901031715705, 0.8991007829081079, 0.829742650939299, 0.9612781430019607, 0.01112666737555973, 0.1861992251927429, 0.391388886866003, 0.0] | [nan, 0.9255583122183136, 0.9555184973850358, 0.8927561553139153, 0.7130378697969978, 0.6275811980710011, 0.0, 0.7474676455043131, 0.8545937449541465, 0.43523520560447965, 0.9672661630501664, 0.28627436744473084, 0.0, 0.0707036205718747, 0.47675012774655084, 0.7689381524189783, 0.0, 0.31600985221674877, 0.9278457312029238, 0.2055231456928555, 0.6363063556709445, 0.5255962863991213, 0.10240946878962942, nan, 0.30514996921453075, 0.6575213496395762, 0.6054551483999336, 0.08830275229357798, 0.9550074747938649, 0.8984159398975186, 0.9823971352874257, 0.013025497748978224, 0.3256981066248004, 0.49491941043060034, 0.0] |
### Framework versions
- Transformers 4.16.2
- Pytorch 1.7.1+cu110
- Datasets 1.18.3
- Tokenizers 0.10.3
|
ekohrt/qcat | 52d9238577b4e4486318b29a78f53c5f0800a04c | 2022-03-10T16:10:25.000Z | [
"pytorch",
"distilbert",
"text-classification",
"transformers",
"license:mit"
]
| text-classification | false | ekohrt | null | ekohrt/qcat | 11 | null | transformers | 11,178 | ---
license: mit
---
# **Q-Cat**
A pre-trained Distilbert model for classifying question types. For use in QA systems.
Dataset contains ~800 labeled examples. Classifier uses a taxonomy of 27 question types. |
mcdzwil/bert-base-NER-finetuned-ner-ISU | 5e003bfe8c57864e450195f429f224f4edf91160 | 2022-03-03T20:21:38.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"license:mit",
"model-index",
"autotrain_compatible"
]
| token-classification | false | mcdzwil | null | mcdzwil/bert-base-NER-finetuned-ner-ISU | 11 | null | transformers | 11,179 | ---
license: mit
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-NER-finetuned-ner-ISU
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-NER-finetuned-ner-ISU
This model is a fine-tuned version of [dslim/bert-base-NER](https://huggingface.co/dslim/bert-base-NER) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1090
- Precision: 0.9408
- Recall: 0.8223
- F1: 0.8776
- Accuracy: 0.9644
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 48 | 0.1411 | 0.8970 | 0.7840 | 0.8367 | 0.9473 |
| No log | 2.0 | 96 | 0.1231 | 0.9453 | 0.7964 | 0.8645 | 0.9589 |
| No log | 3.0 | 144 | 0.1090 | 0.9408 | 0.8223 | 0.8776 | 0.9644 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
azaninello/distilbert-base-uncased-finetuned-mushrooms | bca1be2660863efa4061a53835d45f4540f5ef60 | 2022-03-04T17:45:46.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"fill-mask",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
]
| fill-mask | false | azaninello | null | azaninello/distilbert-base-uncased-finetuned-mushrooms | 11 | null | transformers | 11,180 | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-mushrooms
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-mushrooms
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: 2.4432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.734 | 1.0 | 157 | 2.5275 |
| 2.5807 | 2.0 | 314 | 2.4169 |
| 2.5122 | 3.0 | 471 | 2.4352 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.3
- Tokenizers 0.11.6
|
cambridgeltl/simctg_writingprompts | 2d8e8b0e47b4a70b54f19835c605e3ceff3240a2 | 2022-06-25T19:21:26.000Z | [
"pytorch",
"gpt2",
"text-generation",
"arxiv:1805.04833",
"arxiv:2202.06417",
"transformers"
]
| text-generation | false | cambridgeltl | null | cambridgeltl/simctg_writingprompts | 11 | null | transformers | 11,181 | This model provides a GPT-2 language model trained with SimCTG on the WritingPrompts benchmark [(Fan et al., 2018)](https://arxiv.org/abs/1805.04833) based on our paper [_A Contrastive Framework for Neural Text Generation_](https://arxiv.org/abs/2202.06417).
We provide a detailed tutorial on how to apply SimCTG and Contrastive Search in our [project repo](https://github.com/yxuansu/SimCTG#4-huggingface-style-tutorials-back-to-top). In the following, we illustrate a brief tutorial on how to use our approach to perform text generation.
## 1. Installation of SimCTG:
```yaml
pip install simctg --upgrade
```
## 2. Initialize SimCTG Model:
```python
import torch
# load SimCTG language model
from simctg.simctggpt import SimCTGGPT
model_name = r'cambridgeltl/simctg_writingprompts'
model = SimCTGGPT(model_name)
model.eval()
tokenizer = model.tokenizer
```
## 3. Prepare the Text Prefix:
```python
prefix_text = r"[ WP ] A kid doodling in a math class accidentally creates the world 's first functional magic circle in centuries . <|endoftext|>"
print ('Prefix is: {}'.format(prefix_text))
tokens = tokenizer.tokenize(prefix_text)
input_ids = tokenizer.convert_tokens_to_ids(tokens)
input_ids = torch.LongTensor(input_ids).view(1,-1)
```
## 4. Generate Text with Contrastive Search:
```python
beam_width, alpha, decoding_len = 5, 0.6, 200
output = model.fast_contrastive_search(input_ids=input_ids, beam_width=beam_width,
alpha=alpha, decoding_len=decoding_len)
print("Output:\n" + 100 * '-')
print(tokenizer.decode(output))
'''
Prefix is: [ WP ] A kid doodling in a math class accidentally creates the world 's first functional magic circle in centuries . <|endoftext|>
Output:
----------------------------------------------------------------------------------------------------
[ WP ] A kid doodling in a math class accidentally creates the world's first functional magic circle in centuries. <|endoftext|> I looked at
the circle, it wasn't there. I couldn't see it, and my eyes were watering from the rain that had fallen over the school, the wind howling through
the windows and making a wispy noise as it passed through the air. `` What is it? '' I asked, trying to find the source of the noise. `` It's a
circle, '' the teacher said in a voice that sounded like it was from an old TV show or something like that. `` You can't make it out of there. ''
I looked around the room, there was no one there. It was as if I was in a dream, but no one seemed to notice me. Then I saw a flash of light, and
the circle appeared in front of me. I turned around to see what was going on, I had never seen anything like it before in my life. I ran up to the
teacher and asked, `` Are you sure this is real?
'''
```
For more details of our work, please refer to our main [project repo](https://github.com/yxuansu/SimCTG).
## 5. Citation:
If you find our paper and resources useful, please kindly leave a star and cite our paper. Thanks!
```bibtex
@article{su2022contrastive,
title={A Contrastive Framework for Neural Text Generation},
author={Su, Yixuan and Lan, Tian and Wang, Yan and Yogatama, Dani and Kong, Lingpeng and Collier, Nigel},
journal={arXiv preprint arXiv:2202.06417},
year={2022}
}
```
|
MrAnderson/nystrom-512-full-trivia | bda8883ec231b512572768053e0c8b592f6051d9 | 2022-03-07T21:00:48.000Z | [
"pytorch",
"nystromformer",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | MrAnderson | null | MrAnderson/nystrom-512-full-trivia | 11 | null | transformers | 11,182 | Entry not found |
StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es | 41484e61f5f6d7870e2952aa8a52a7672397ab11 | 2022-03-08T10:57:12.000Z | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"transformers",
"generated_from_trainer",
"model-index",
"autotrain_compatible"
]
| token-classification | false | StivenLancheros | null | StivenLancheros/biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es | 11 | null | transformers | 11,183 | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es
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. -->
# biobert-base-cased-v1.2-finetuned-ner-Concat_CRAFT_es
This model is a fine-tuned version of [dmis-lab/biobert-base-cased-v1.2](https://huggingface.co/dmis-lab/biobert-base-cased-v1.2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2079
- Precision: 0.8487
- Recall: 0.8443
- F1: 0.8465
- Accuracy: 0.9693
## 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
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0698 | 1.0 | 2719 | 0.1463 | 0.8132 | 0.8233 | 0.8182 | 0.9643 |
| 0.0321 | 2.0 | 5438 | 0.1612 | 0.8321 | 0.8463 | 0.8392 | 0.9681 |
| 0.0154 | 3.0 | 8157 | 0.1832 | 0.8448 | 0.8404 | 0.8426 | 0.9683 |
| 0.0058 | 4.0 | 10876 | 0.2079 | 0.8487 | 0.8443 | 0.8465 | 0.9693 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
ICFNext/EYY-Categorisation | 6c711628fdd64a53494eb4d4be51fe17f2445412 | 2022-03-12T13:24:28.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"transformers"
]
| text-classification | false | ICFNext | null | ICFNext/EYY-Categorisation | 11 | 0 | transformers | 11,184 | |
antho-data/distilbert-base-uncased-finetuned-emotion | 739aae9e1e6991556d1c3a6feed4a9957e256e5f | 2022-03-09T21:27:17.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | antho-data | null | antho-data/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,185 | ---
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: 0.9235
- name: F1
type: f1
value: 0.9237367861627231
---
<!-- 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: 0.2294
- Accuracy: 0.9235
- F1: 0.9237
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8637 | 1.0 | 250 | 0.3319 | 0.9075 | 0.9050 |
| 0.2634 | 2.0 | 500 | 0.2294 | 0.9235 | 0.9237 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
farrokhguiahi/distilbert-base-uncased-finetuned-emotion | 040434f3bbe596ecfbe811e02b7102da6bb6546f | 2022-03-28T15:19:47.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | farrokhguiahi | null | farrokhguiahi/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,186 | ---
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: 0.9275
- name: F1
type: f1
value: 0.9278961513392271
---
<!-- 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: 0.2168
- Accuracy: 0.9275
- F1: 0.9279
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8418 | 1.0 | 250 | 0.3102 | 0.905 | 0.9012 |
| 0.2454 | 2.0 | 500 | 0.2168 | 0.9275 | 0.9279 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
waboucay/camembert-base-finetuned-xnli_fr | 9a935237b3054740cb4ef28092f3e83999392940 | 2022-03-30T17:47:05.000Z | [
"pytorch",
"camembert",
"text-classification",
"fr",
"transformers",
"nli"
]
| text-classification | false | waboucay | null | waboucay/camembert-base-finetuned-xnli_fr | 11 | null | transformers | 11,187 | ---
language:
- fr
tags:
- nli
metrics:
- f1
---
## Eval results
We obtain the following results on ```validation``` and ```test``` sets:
| Set | F1<sub>micro</sub> | F1<sub>macro</sub> |
|------------|--------------------|--------------------|
| validation | 89.2 | 87.6 |
| test | 88.9 | 87.4 |
|
markt23917/finetuning-sentiment-model-3000-samples | a8a92d619d3ac84f1ddca70304060f5a50e4d85f | 2022-03-12T08:11:37.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:imdb",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | markt23917 | null | markt23917/finetuning-sentiment-model-3000-samples | 11 | null | transformers | 11,188 | ---
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: 0.8766666666666667
- name: F1
type: f1
value: 0.8825396825396825
---
<!-- 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: 0.3351
- Accuracy: 0.8767
- F1: 0.8825
## 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 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 1.18.4
- Tokenizers 0.11.6
|
MrAnderson/yoso-512-full-trivia-copied-embeddings | 36119b9255ccc138a76c4cff5ee749515408782d | 2022-03-13T15:24:35.000Z | [
"pytorch",
"yoso",
"question-answering",
"transformers",
"autotrain_compatible"
]
| question-answering | false | MrAnderson | null | MrAnderson/yoso-512-full-trivia-copied-embeddings | 11 | null | transformers | 11,189 | Entry not found |
DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323 | 5cc52158825e20033c8be795a573910026a53f1a | 2022-03-13T07:31:45.000Z | [
"pytorch",
"bert",
"text-classification",
"en",
"dataset:DrishtiSharma/autonlp-data-Text-Classification-Catalonia-Independence-AutoNLP",
"transformers",
"autonlp",
"co2_eq_emissions"
]
| text-classification | false | DrishtiSharma | null | DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323 | 11 | null | transformers | 11,190 | ---
tags: autonlp
language: en
widget:
- text: "I love AutoNLP 🤗"
datasets:
- DrishtiSharma/autonlp-data-Text-Classification-Catalonia-Independence-AutoNLP
co2_eq_emissions: 3.622203603306694
---
# Model Trained Using AutoNLP
- Problem type: Multi-class Classification
- Model ID: 633018323
- CO2 Emissions (in grams): 3.622203603306694
## Validation Metrics
- Loss: 0.681106686592102
- Accuracy: 0.709136109384711
- Macro F1: 0.6987186860138147
- Micro F1: 0.709136109384711
- Weighted F1: 0.7059639788836748
- Macro Precision: 0.7174345617951404
- Micro Precision: 0.709136109384711
- Weighted Precision: 0.712710833401347
- Macro Recall: 0.6912117894374218
- Micro Recall: 0.709136109384711
- Weighted Recall: 0.709136109384711
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoNLP"}' https://api-inference.huggingface.co/models/DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323
```
Or Python API:
```
from transformers import AutoModelForSequenceClassification, AutoTokenizer
model = AutoModelForSequenceClassification.from_pretrained("DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323", use_auth_token=True)
tokenizer = AutoTokenizer.from_pretrained("DrishtiSharma/autonlp-Text-Classification-Catalonia-Independence-AutoNLP-633018323", use_auth_token=True)
inputs = tokenizer("I love AutoNLP", return_tensors="pt")
outputs = model(**inputs)
``` |
anwesham/mbert_ar_ur | 2daa617f0a9e3acc6112eb553a077a95727c20ae | 2022-03-13T10:10:05.000Z | [
"pytorch",
"bert",
"token-classification",
"transformers",
"autotrain_compatible"
]
| token-classification | false | anwesham | null | anwesham/mbert_ar_ur | 11 | null | transformers | 11,191 | Entry not found |
lanesket/finetuned-r-codebert-mlm | 1c9c4e0cea102c7ecf44cc2de0ecf9182c209f54 | 2022-03-30T11:45:55.000Z | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
]
| fill-mask | false | lanesket | null | lanesket/finetuned-r-codebert-mlm | 11 | null | transformers | 11,192 | Entry not found |
jfealko/wav2vec2-large-xls-r-300m-irish-wav-dataset | 6fae06e9fef0bbb7e1b3c769afc0c102d049d694 | 2022-03-14T02:29:26.000Z | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | jfealko | null | jfealko/wav2vec2-large-xls-r-300m-irish-wav-dataset | 11 | null | transformers | 11,193 | Entry not found |
quincyqiang/distilbert-base-uncased-finetuned-emotion | 4ad4db56c7181bf32997dff379f9279add872f05 | 2022-07-21T08:11:11.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | quincyqiang | null | quincyqiang/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,194 | ---
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: 0.927
- name: F1
type: f1
value: 0.927273630943427
---
<!-- 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: 0.2106
- Accuracy: 0.927
- F1: 0.9273
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8007 | 1.0 | 250 | 0.2955 | 0.914 | 0.9117 |
| 0.2417 | 2.0 | 500 | 0.2106 | 0.927 | 0.9273 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.8.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
anton-l/xtreme_s_xlsr_bart_covost | 4cbfc360b01f25af319f1d36db6a7a52cdc19001 | 2022-03-15T14:31:12.000Z | [
"pytorch",
"tensorboard",
"speech-encoder-decoder",
"automatic-speech-recognition",
"transformers"
]
| automatic-speech-recognition | false | anton-l | null | anton-l/xtreme_s_xlsr_bart_covost | 11 | null | transformers | 11,195 | Entry not found |
moralstories/roberta-large_action-norm | b40b9e108a6ebe0df18e2e236c4df9b629c616cd | 2022-03-15T17:35:54.000Z | [
"pytorch",
"roberta",
"text-classification",
"transformers",
"license:afl-3.0"
]
| text-classification | false | moralstories | null | moralstories/roberta-large_action-norm | 11 | null | transformers | 11,196 | ---
license: afl-3.0
---
|
dodobird/distilbert-base-uncased-finetuned-emotion | fc9b0213ab130bae696508ad981c6fcc02d6bba0 | 2022-03-21T03:04:10.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | dodobird | null | dodobird/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,197 | ---
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: 0.9245
- name: F1
type: f1
value: 0.9248889383977278
---
<!-- 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: 0.2154
- Accuracy: 0.9245
- F1: 0.9249
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8175 | 1.0 | 250 | 0.3139 | 0.9025 | 0.8986 |
| 0.2485 | 2.0 | 500 | 0.2154 | 0.9245 | 0.9249 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
rurupang/bert-base-finetuned-sts | 9d3f592604aec5277771e0d35d6da18baedfc198 | 2022-03-21T19:23:42.000Z | [
"pytorch",
"bert",
"text-classification",
"dataset:klue",
"transformers",
"generated_from_trainer",
"model-index"
]
| text-classification | false | rurupang | null | rurupang/bert-base-finetuned-sts | 11 | null | transformers | 11,198 | ---
tags:
- generated_from_trainer
datasets:
- klue
metrics:
- pearsonr
model-index:
- name: bert-base-finetuned-sts
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: klue
type: klue
args: sts
metrics:
- name: Pearsonr
type: pearsonr
value: 0.8722017849942011
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-finetuned-sts
This model is a fine-tuned version of [klue/bert-base](https://huggingface.co/klue/bert-base) on the klue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4274
- Pearsonr: 0.8722
## 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearsonr |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 365 | 0.5106 | 0.8429 |
| 0.1092 | 2.0 | 730 | 0.5466 | 0.8497 |
| 0.0958 | 3.0 | 1095 | 0.4123 | 0.8680 |
| 0.0958 | 4.0 | 1460 | 0.4336 | 0.8719 |
| 0.0661 | 5.0 | 1825 | 0.4274 | 0.8722 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
loulou/distilbert-base-uncased-finetuned-emotion | 207b288ea8133de25e4b8bf5bf168e3d6c6f5ea4 | 2022-03-30T04:57:58.000Z | [
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"dataset:emotion",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index"
]
| text-classification | false | loulou | null | loulou/distilbert-base-uncased-finetuned-emotion | 11 | null | transformers | 11,199 | ---
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: 0.922
- name: F1
type: f1
value: 0.9221931901873676
---
<!-- 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: 0.2285
- Accuracy: 0.922
- F1: 0.9222
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8366 | 1.0 | 250 | 0.3212 | 0.9025 | 0.8990 |
| 0.2588 | 2.0 | 500 | 0.2285 | 0.922 | 0.9222 |
### Framework versions
- Transformers 4.18.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 2.0.0
- Tokenizers 0.11.6
|
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