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} | 0 | null | ---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-mask-prompt-d-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8807936507936508
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5828877005347594
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5786350148367952
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7787659811006115
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.958
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6140350877192983
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6226851851851852
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9213500075335241
- name: F1 (macro)
type: f1_macro
value: 0.9170167858091296
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8814553990610329
- name: F1 (macro)
type: f1_macro
value: 0.7355097106184322
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.7036836403033586
- name: F1 (macro)
type: f1_macro
value: 0.6966787116526776
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9632051192877513
- name: F1 (macro)
type: f1_macro
value: 0.895336152433551
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9003447195236602
- name: F1 (macro)
type: f1_macro
value: 0.8993684208521904
---
# relbert/roberta-large-conceptnet-mask-prompt-d-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5828877005347594
- Accuracy on SAT: 0.5786350148367952
- Accuracy on BATS: 0.7787659811006115
- Accuracy on U2: 0.6140350877192983
- Accuracy on U4: 0.6226851851851852
- Accuracy on Google: 0.958
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9213500075335241
- Micro F1 score on CogALexV: 0.8814553990610329
- Micro F1 score on EVALution: 0.7036836403033586
- Micro F1 score on K&H+N: 0.9632051192877513
- Micro F1 score on ROOT09: 0.9003447195236602
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8807936507936508
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-mask-prompt-d-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <subj> is the <mask> of <obj>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 88
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-d-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
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} | 0 | null | Access to model maan909/unisumm_2 is restricted and you are not in the authorized list. Visit https://huggingface.co/maan909/unisumm_2 to ask for access. |
Arcktosh/DialoGPT-small-rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 8 | null | ---
tags:
- autotrain
- summarization
language:
- en
widget:
- text: "I love AutoTrain 🤗"
datasets:
- Akbar-Ali/autotrain-data-News_Summariser_Eng
co2_eq_emissions:
emissions: 35.7814981860994
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1224546522
- CO2 Emissions (in grams): 35.7815
## Validation Metrics
- Loss: 0.638
- Rouge1: 44.532
- Rouge2: 33.731
- RougeL: 40.372
- RougeLsum: 40.653
- Gen Len: 57.730
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/Akbar-Ali/autotrain-News_Summariser_Eng-1224546522
``` |
Arina/Erine | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- superb
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-ks
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-base-finetuned-ks
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the superb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0993
- Accuracy: 0.9812
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7367 | 1.0 | 399 | 0.6341 | 0.8819 |
| 0.3087 | 2.0 | 798 | 0.1900 | 0.9771 |
| 0.1979 | 3.0 | 1197 | 0.1232 | 0.9800 |
| 0.171 | 4.0 | 1596 | 0.1057 | 0.9794 |
| 0.1253 | 5.0 | 1995 | 0.0993 | 0.9812 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0
- Datasets 2.4.0
- Tokenizers 0.12.1
|
ArpanZS/search_model | [
"joblib"
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} | 0 | null | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
Abbreviations:
- p31 = Updated version of p3 with new prompts
- xp3 = Multilingual version of P3
- cap = Example Capping (100K / dataset)
- mix = Validation is 5% of train (Else it is the validation set of the datasets used)
- brack = old model with a bug (targets had brackets around them, so it always generates brackets)
- lossseq: Uses https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/326 (The idea is to give every target the same weight regardless of its length)
Code:
- Training Code is MegDS
- xp3 creation; eval scripts; training scripts are all here: https://github.com/bigscience-workshop/bigscience/pull/57
Known issues:
- xP3 has leakage across languages (I.e. in the mixed setup the same sample in the training set may appear in the validation set in a different language)
- The non-mixed xp3 versions have validation sets with a different distribution than xp3 with some langs missing entirely as there are no val sets
XP3 language composition (Training dataset for XP3 has ~the same distribution / language as the below XP3 percentages):
```
Language & Code & ROOTS (perc) & xP3 (perc) & xP3 (MB) & xP3 (M tokens)\\
English & en & 30.038 & 43.139 & 36944.9 & 11083.5\\
Chinese & zh & 16.215 & 4.532 & 3881.2 & 1164.4\\
French & fr & 12.898 & 6.51 & 5575.1 & 1672.5\\
Spanish & es & 10.846 & 7.668 & 6566.6 & 1970.0\\
Programming Languages & code & 10.821 & 0.739 & 633.0 & 189.9\\
Portuguese & pt & 4.91 & 5.976 & 5117.8 & 1535.3\\
Arabic & ar & 4.636 & 6.29 & 5386.9 & 1616.1\\
Vietnamese & vi & 2.707 & 3.325 & 2847.8 & 854.3\\
Hindi & hi & 1.525 & 2.642 & 2262.9 & 678.9\\
Indonesian & id & 1.237 & 4.802 & 4112.8 & 1233.8\\
Bengali & bn & 1.152 & 0.801 & 686.0 & 205.8\\
Catalan & ca & 1.102 & 0.169 & 145.0 & 43.5\\
Tamil & ta & 0.495 & 0.357 & 306.0 & 91.8\\
Malayalam & ml & 0.227 & 0.354 & 303.4 & 91.0\\
Telugu & te & 0.185 & 1.429 & 1223.4 & 367.0\\
Urdu & ur & 0.172 & 0.306 & 261.8 & 78.5\\
Nepali & ne & 0.158 & 0.311 & 266.3 & 79.9\\
Basque & eu & 0.146 & 0.163 & 139.7 & 41.9\\
Kannada & kn & 0.13 & 0.365 & 312.6 & 93.8\\
Marathi & mr & 0.11 & 0.322 & 276.1 & 82.8\\
Punjabi & pa & 0.097 & 0.314 & 268.7 & 80.6\\
Gujarati & gu & 0.074 & 0.306 & 261.8 & 78.5\\
Odia & or & 0.072 & 0.323 & 276.8 & 83.0\\
Assamese & as & 0.018 & 0.31 & 265.3 & 79.6\\
Swahili & sw & 0.015 & 0.759 & 649.7 & 194.9\\
Yoruba & yo & 0.006 & 0.632 & 541.1 & 162.3\\
Kinyarwanda & rw & 0.003 & 0.552 & 473.0 & 141.9\\
Xhosa & xh & 0.001 & 0.55 & 471.4 & 141.4\\
Igbo & ig & 0.001 & 0.571 & 489.2 & 146.8\\
Isi Zulu & zu & 0.001 & 0.574 & 491.8 & 147.5\\
Chi Shona & sn & 0.0 & 0.55 & 471.0 & 141.3\\
Luganda & lg & 0.0 & 0.499 & 427.4 & 128.2\\
Wolof & wo & 0.0 & 0.208 & 178.2 & 53.5\\
Kirundi & rn & 0.0 & 0.165 & 141.5 & 42.4\\
Fon & fon & 0.0 & 0.204 & 174.9 & 52.5\\
Northern Sotho & nso & 0.0 & 0.473 & 405.1 & 121.5\\
Lingala & ln & 0.0 & 0.213 & 182.8 & 54.8\\
Setswana & tn & 0.0 & 0.382 & 326.7 & 98.0\\
Twi & tw & 0.0 & 0.172 & 147.4 & 44.2\\
Chi Chewa & ny & 0.0 & 0.596 & 510.4 & 153.1\\
Sesotho & st & 0.0 & 0.17 & 145.8 & 43.7\\
Xitsonga & ts & 0.0 & 0.557 & 476.9 & 143.1\\
Akan & ak & 0.0 & 0.176 & 151.1 & 45.3\\
Bambara & bm & 0.0 & 0.175 & 149.7 & 44.9\\
Kikuyu & ki & 0.0 & 0.191 & 163.7 & 49.1\\
Chi Tumbuka & tum & 0.0 & 0.175 & 149.8 & 44.9\\
```
|
Augustvember/wokka5 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 11 | null | ---
tags:
- generated_from_trainer
datasets:
- squad_modified_for_t5_qg
model-index:
- name: Fine-tuned-T5-for-MCQGenerator
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. -->
# Fine-tuned-T5-for-MCQGenerator
This model is a fine-tuned version of [ramsrigouthamg/t5_squad_v1](https://huggingface.co/ramsrigouthamg/t5_squad_v1) on the squad_modified_for_t5_qg dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
### Training results
### Framework versions
- Transformers 4.21.1
- Pytorch 1.11.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Aviora/news2vec | [] | null | {
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} | 0 | 2022-08-07T03:41:27Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: bert-base-uncased-finetuned
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-uncased-finetuned
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4410
- Accuracy: 0.8550
- F1: 0.8557
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.4141 | 1.0 | 561 | 0.3768 | 0.8540 | 0.8545 |
| 0.1774 | 2.0 | 1122 | 0.4410 | 0.8550 | 0.8557 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Axon/resnet50-v1 | [
"dataset:ImageNet",
"arxiv:1512.03385",
"Axon",
"Elixir",
"license:apache-2.0"
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: rwang5688/distilbert-base-uncased-finetuned-sst2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# rwang5688/distilbert-base-uncased-finetuned-sst2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1022
- Validation Loss: 0.2643
- Train Accuracy: 0.9014
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 12627, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.2128 | 0.3199 | 0.8784 | 0 |
| 0.1022 | 0.2643 | 0.9014 | 1 |
### Framework versions
- Transformers 4.27.4
- TensorFlow 2.12.0
- Datasets 2.11.0
- Tokenizers 0.13.2
|
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} | 0 | null | ---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-mask-prompt-e-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.9325396825396826
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5561497326203209
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5578635014836796
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7593107281823235
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.898
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5657894736842105
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.5902777777777778
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9303902365526593
- name: F1 (macro)
type: f1_macro
value: 0.9253608458682704
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8781690140845071
- name: F1 (macro)
type: f1_macro
value: 0.7319159638510688
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6939328277356447
- name: F1 (macro)
type: f1_macro
value: 0.6992515104207172
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9631355637476525
- name: F1 (macro)
type: f1_macro
value: 0.8833254511680932
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9088060169225948
- name: F1 (macro)
type: f1_macro
value: 0.9064745584707974
---
# relbert/roberta-large-conceptnet-mask-prompt-e-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.5561497326203209
- Accuracy on SAT: 0.5578635014836796
- Accuracy on BATS: 0.7593107281823235
- Accuracy on U2: 0.5657894736842105
- Accuracy on U4: 0.5902777777777778
- Accuracy on Google: 0.898
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9303902365526593
- Micro F1 score on CogALexV: 0.8781690140845071
- Micro F1 score on EVALution: 0.6939328277356447
- Micro F1 score on K&H+N: 0.9631355637476525
- Micro F1 score on ROOT09: 0.9088060169225948
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.9325396825396826
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-mask-prompt-e-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: I wasn’t aware of this relationship, but I just read in the encyclopedia that <obj> is <subj>’s <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 146
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-mask-prompt-e-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Ayham/xlnet_gpt_xsum | [
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
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} | 11 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-summarizer-finetuned
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mt5-small-summarizer-finetuned
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0158
- Rouge1: 17.7167
- Rouge2: 8.7443
- Rougel: 17.4487
- Rougelsum: 17.4013
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.8514 | 1.0 | 1209 | 3.2992 | 14.7682 | 6.17 | 14.2741 | 14.2171 |
| 3.8706 | 2.0 | 2418 | 3.1206 | 16.1753 | 7.7142 | 15.436 | 15.5325 |
| 3.5426 | 3.0 | 3627 | 3.0748 | 17.9388 | 8.786 | 17.3878 | 17.3805 |
| 3.3773 | 4.0 | 4836 | 3.0461 | 16.79 | 7.9251 | 16.4337 | 16.3482 |
| 3.2734 | 5.0 | 6045 | 3.0438 | 17.201 | 8.2405 | 16.9939 | 16.9181 |
| 3.194 | 6.0 | 7254 | 3.0227 | 17.3508 | 8.4746 | 17.0519 | 17.0831 |
| 3.1556 | 7.0 | 8463 | 3.0201 | 17.6119 | 8.686 | 17.3536 | 17.3228 |
| 3.1256 | 8.0 | 9672 | 3.0158 | 17.7167 | 8.7443 | 17.4487 | 17.4013 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
AyushPJ/ai-club-inductions-21-nlp-XLNet | [
"pytorch",
"xlnet",
"question-answering",
"transformers",
"generated_from_trainer",
"autotrain_compatible"
] | question-answering | {
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8648740833380706
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1365
- F1: 0.8649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2553 | 1.0 | 525 | 0.1575 | 0.8279 |
| 0.1284 | 2.0 | 1050 | 0.1386 | 0.8463 |
| 0.0813 | 3.0 | 1575 | 0.1365 | 0.8649 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BSC-LT/roberta-base-bne-capitel-ner-plus | [
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"roberta",
"token-classification",
"es",
"dataset:bne",
"dataset:capitel",
"arxiv:1907.11692",
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"bne",
"capitel",
"ner",
"license:apache-2.0",
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} | 9 | null | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
---
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p>
<img src="https://assets.website-files.com/6139f3cdcbbff3a68486761d/613cd8997b270da063e230c5_Tekengebied%201-p-500.png" alt="BigScience Logo" width="200"/>
Version 1.3 / 11.July.2022 - Available intermediary checkpoints - global steps:
+ `1000`, `10000`, `50000`, `100000`, `150000`, `200000`, `250000`, `300000`
You can check the available checkpoints by clicking on the branches section of the repo
# How to load a specific version
We use `git tags` to load a model in a specific version (eg. `global_step1000`):
```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"bigscience/bloom-2b5-intermediate",
revision="global_step1000",
torch_dtype="auto",
)
```
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
---
# Model Details
BLOOM is a type of language model, which is a probability distribution over sequences of words. Specifically, BLOOM is a Large Language Model (LLM), meaning that it is trained on vast amounts of text data using industrial-scale computational resources. As such, the model is able to capture the statistical tendencies of words, phrases, sentences, and larger spans of text that it is exposed to in the training data.
## Basics
*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*
*It is useful for anyone who wants to reference the model.*
<details>
<summary>Click to expand</summary>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
*All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
## Technical Specifications
*This section includes details about the model objective and architecture, and the compute infrastructure.*
*It is useful for people interested in model development.*
<details>
<summary>Click to expand</summary>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
### Model Architecture and Objective
* Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 176 billion parameters:
* 70 layers, 112 attention heads
* Hidden layers are 14336-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
### Compute infrastructure
Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
#### Hardware
* 384 A100 80GB GPUs (48 nodes)
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
#### Software
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
</details>
---
# Training
*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.*
*It is useful for people who want to learn more about the model inputs and training footprint.*
<details>
<summary>Click to expand</summary>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
### Languages
The pie chart shows the distribution of languages in training data.
The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.
Distribution of Niger Congo and Indic languages.
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
Distribution of programming languages.
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
### Preprocessing
**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
## Speeds, Sizes, Times
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
- Dates:
- Started 11th March, 2022 11:42am PST
- Estimated end: 5th July, 2022
- Checkpoint size:
- Bf16 weights: 329GB
- Full checkpoint with optimizer states: 2.3TB
- Training throughput: About 150 TFLOP per GPU per second
- Number of epochs: 1
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
## Environmental Impact
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming.)*
**Estimated electricity usage:** *(Forthcoming.)*
</details>
---
# Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.*
*It is useful for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary>
## Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
### Direct Use
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
### Downstream Use
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### Out-of-scope Uses
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### Misuse
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
## Intended Users
### Direct Users
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
---
# Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
---
# Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary>
## Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
## Factors
*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
## Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
(More evaluation scores forthcoming.)
</details>
---
# Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models trained or finetuned downstream of BLOOM LM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
---
# Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
---
# More Information
*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.*
<details>
<summary>Click to expand</summary>
## Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
## Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
## Lessons
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
## Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
---
# Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
|
BSC-LT/roberta-large-bne-sqac | [
"pytorch",
"roberta",
"question-answering",
"es",
"dataset:BSC-TeMU/SQAC",
"arxiv:1907.11692",
"arxiv:2107.07253",
"transformers",
"national library of spain",
"spanish",
"bne",
"qa",
"question answering",
"license:apache-2.0",
"autotrain_compatible"
] | question-answering | {
"architectures": [
"RobertaForQuestionAnswering"
],
"model_type": "roberta",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_ro": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 15 | null | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
---
# <span style="color:red"><b>WARNING:</b> The checkpoints on this repo are not fully trained model. Evaluations of intermediary checkpoints and the final model will be added when conducted (see below).</span>
# <p>BLOOM LM<br/> _BigScience Large Open-science Open-access Multilingual Language Model_ <br/>Model Card</p>
<img src="https://assets.website-files.com/6139f3cdcbbff3a68486761d/613cd8997b270da063e230c5_Tekengebied%201-p-500.png" alt="BigScience Logo" width="200"/>
Version 1.3 / 11.July.2022 - Available intermediary checkpoints - global steps:
+ `1000`, `10000`, `100000`, `200000`, `300000`, `400000`, `500000`, `600000`
You can check the available checkpoints by clicking on the branches section of the repo
# How to load a specific version
We use `git tags` to load a model in a specific version (eg. `global_step1000`):
```python
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained(
"bigscience/bloom-350m-intermediate",
revision="global_step1000",
torch_dtype="auto",
)
```
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
---
# Model Details
BLOOM is a type of language model, which is a probability distribution over sequences of words. Specifically, BLOOM is a Large Language Model (LLM), meaning that it is trained on vast amounts of text data using industrial-scale computational resources. As such, the model is able to capture the statistical tendencies of words, phrases, sentences, and larger spans of text that it is exposed to in the training data.
## Basics
*This section provides information about the model type, version, license, funders, release date, developers, and contact information.*
*It is useful for anyone who wants to reference the model.*
<details>
<summary>Click to expand</summary>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
*All collaborators are either volunteers or have an agreement with their employer. (Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** [email protected]
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
## Technical Specifications
*This section includes details about the model objective and architecture, and the compute infrastructure.*
*It is useful for people interested in model development.*
<details>
<summary>Click to expand</summary>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
### Model Architecture and Objective
* Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 176 billion parameters:
* 70 layers, 112 attention heads
* Hidden layers are 14336-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
### Compute infrastructure
Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
#### Hardware
* 384 A100 80GB GPUs (48 nodes)
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
#### Software
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
</details>
---
# Training
*This section provides information about the training data, the speed and size of training elements, and the environmental impact of training.*
*It is useful for people who want to learn more about the model inputs and training footprint.*
<details>
<summary>Click to expand</summary>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
### Languages
The pie chart shows the distribution of languages in training data.
The following tables shows the further distribution of Niger-Congo & Indic languages and programming languages in the training data.
Distribution of Niger Congo and Indic languages.
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
Distribution of programming languages.
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
### Preprocessing
**Tokenization:** The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)), a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
## Speeds, Sizes, Times
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11-176B-ml-logs/)
- Dates:
- Started 11th March, 2022 11:42am PST
- Estimated end: 5th July, 2022
- Checkpoint size:
- Bf16 weights: 329GB
- Full checkpoint with optimizer states: 2.3TB
- Training throughput: About 150 TFLOP per GPU per second
- Number of epochs: 1
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
## Environmental Impact
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming.)*
**Estimated electricity usage:** *(Forthcoming.)*
</details>
---
# Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.*
*It is useful for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary>
## Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
### Direct Use
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
### Downstream Use
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### Out-of-scope Uses
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### Misuse
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
## Intended Users
### Direct Users
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
---
# Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
---
# Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary>
## Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
## Factors
*This section lists some different aspects of what BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
## Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.0
- Validation Loss: 2.2
- Perplexity: 8.9
(More evaluation scores forthcoming.)
</details>
---
# Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models trained or finetuned downstream of BLOOM LM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
---
# Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
---
# More Information
*This section provides links to writing on dataset creation, technical specifications, lessons learned, and initial results.*
<details>
<summary>Click to expand</summary>
## Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
## Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
## Lessons
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
## Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
---
# Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff |
BSen/wav2vec2-base-timit-demo-colab | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"transformers",
"generated_from_trainer",
"license:apache-2.0"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
"conversational": {
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},
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},
"text-generation": {
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},
"translation_en_to_de": {
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"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
"early_stopping": null,
"max_length": null,
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"prefix": null
},
"translation_en_to_ro": {
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"prefix": null
}
}
} | 4 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
It has been fine-tuned on https://brain.louis030195.com using code from https://github.com/louis030195/obsidian-search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian')
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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian')
model = AutoModel.from_pretrained('louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian')
# 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, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=louis030195/multi-qa-MiniLM-L6-cos-v1-obsidian)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 218 with parameters:
```
{'batch_size': 8, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.DenoisingAutoEncoderLoss.DenoisingAutoEncoderLoss`
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 3e-05
},
"scheduler": "constantlr",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Babelscape/rebel-large | [
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:Babelscape/rebel-dataset",
"transformers",
"seq2seq",
"relation-extraction",
"license:cc-by-nc-sa-4.0",
"model-index",
"autotrain_compatible",
"has_space"
] | text2text-generation | {
"architectures": [
"BartForConditionalGeneration"
],
"model_type": "bart",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
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"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": null,
"max_length": null,
"num_beams": null,
"prefix": null
},
"translation_en_to_fr": {
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},
"translation_en_to_ro": {
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"max_length": null,
"num_beams": null,
"prefix": null
}
}
} | 9,458 | null | See https://wandb.ai/yepster/long-t5-local-base?workspace=user-yepster for
logs |
Banshee/dialoGPT-luke-small | [] | null | {
"architectures": null,
"model_type": null,
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},
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}
}
} | 0 | null | ---
tags:
- conversational
---
# Harry Potter DialoGPT Model |
Barkavi/totto-t5-base-bert-score-121K | [
"pytorch",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 51 | null | ```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
model = AutoModelForCausalLM.from_pretrained("BigSalmon/InformalToFormalLincoln63Paraphrase")
```
```
Demo:
https://huggingface.co/spaces/BigSalmon/FormalInformalConciseWordy
```
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
input_ids = tokenizer.encode(prompt, return_tensors='pt')
outputs = model.generate(input_ids=input_ids,
max_length=10 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
do_sample=True,
num_return_sequences=5,
early_stopping=True)
for i in range(5):
print(tokenizer.decode(outputs[i]))
```
Most likely outputs:
```
prompt = """informal english: corn fields are all across illinois, visible once you leave chicago.\nTranslated into the Style of Abraham Lincoln:"""
text = tokenizer.encode(prompt)
myinput, past_key_values = torch.tensor([text]), None
myinput = myinput
myinput= myinput.to(device)
logits, past_key_values = model(myinput, past_key_values = past_key_values, return_dict=False)
logits = logits[0,-1]
probabilities = torch.nn.functional.softmax(logits)
best_logits, best_indices = logits.topk(250)
best_words = [tokenizer.decode([idx.item()]) for idx in best_indices]
text.append(best_indices[0].item())
best_probabilities = probabilities[best_indices].tolist()
words = []
print(best_words)
```
```
How To Make Prompt:
informal english: i am very ready to do that just that.
Translated into the Style of Abraham Lincoln: you can assure yourself of my readiness to work toward this end.
Translated into the Style of Abraham Lincoln: please be assured that i am most ready to undertake this laborious task.
***
informal english: space is huge and needs to be explored.
Translated into the Style of Abraham Lincoln: space awaits traversal, a new world whose boundaries are endless.
Translated into the Style of Abraham Lincoln: space is a ( limitless / boundless ) expanse, a vast virgin domain awaiting exploration.
***
informal english: corn fields are all across illinois, visible once you leave chicago.
Translated into the Style of Abraham Lincoln: corn fields ( permeate illinois / span the state of illinois / ( occupy / persist in ) all corners of illinois / line the horizon of illinois / envelop the landscape of illinois ), manifesting themselves visibly as one ventures beyond chicago.
informal english:
```
```
infill: chrome extensions [MASK] accomplish everyday tasks.
Translated into the Style of Abraham Lincoln: chrome extensions ( expedite the ability to / unlock the means to more readily ) accomplish everyday tasks.
infill: at a time when nintendo has become inflexible, [MASK] consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
Translated into the Style of Abraham Lincoln: at a time when nintendo has become inflexible, ( stubbornly [MASK] on / firmly set on / unyielding in its insistence on ) consoles that are tethered to a fixed iteration, sega diligently curates its legacy of classic video games on handheld devices.
infill:
```
```
Essay Intro (Warriors vs. Rockets in Game 7):
text: eagerly anticipated by fans, game 7's are the highlight of the post-season.
text: ever-building in suspense, game 7's have the crowd captivated.
***
Essay Intro (South Korean TV Is Becoming Popular):
text: maturing into a bona fide paragon of programming, south korean television ( has much to offer / entertains without fail / never disappoints ).
text: increasingly held in critical esteem, south korean television continues to impress.
text: at the forefront of quality content, south korea is quickly achieving celebrity status.
***
Essay Intro (
```
```
Search: What is the definition of Checks and Balances?
https://en.wikipedia.org/wiki/Checks_and_balances
Checks and Balances is the idea of having a system where each and every action in government should be subject to one or more checks that would not allow one branch or the other to overly dominate.
https://www.harvard.edu/glossary/Checks_and_Balances
Checks and Balances is a system that allows each branch of government to limit the powers of the other branches in order to prevent abuse of power
https://www.law.cornell.edu/library/constitution/Checks_and_Balances
Checks and Balances is a system of separation through which branches of government can control the other, thus preventing excess power.
***
Search: What is the definition of Separation of Powers?
https://en.wikipedia.org/wiki/Separation_of_powers
The separation of powers is a principle in government, whereby governmental powers are separated into different branches, each with their own set of powers, that are prevent one branch from aggregating too much power.
https://www.yale.edu/tcf/Separation_of_Powers.html
Separation of Powers is the division of governmental functions between the executive, legislative and judicial branches, clearly demarcating each branch's authority, in the interest of ensuring that individual liberty or security is not undermined.
***
Search: What is the definition of Connection of Powers?
https://en.wikipedia.org/wiki/Connection_of_powers
Connection of Powers is a feature of some parliamentary forms of government where different branches of government are intermingled, typically the executive and legislative branches.
https://simple.wikipedia.org/wiki/Connection_of_powers
The term Connection of Powers describes a system of government in which there is overlap between different parts of the government.
***
Search: What is the definition of
```
```
Search: What are phrase synonyms for "second-guess"?
https://www.powerthesaurus.org/second-guess/synonyms
Shortest to Longest:
- feel dubious about
- raise an eyebrow at
- wrinkle their noses at
- cast a jaundiced eye at
- teeter on the fence about
***
Search: What are phrase synonyms for "mean to newbies"?
https://www.powerthesaurus.org/mean_to_newbies/synonyms
Shortest to Longest:
- readiness to balk at rookies
- absence of tolerance for novices
- hostile attitude toward newcomers
***
Search: What are phrase synonyms for "make use of"?
https://www.powerthesaurus.org/make_use_of/synonyms
Shortest to Longest:
- call upon
- glean value from
- reap benefits from
- derive utility from
- seize on the merits of
- draw on the strength of
- tap into the potential of
***
Search: What are phrase synonyms for "hurting itself"?
https://www.powerthesaurus.org/hurting_itself/synonyms
Shortest to Longest:
- erring
- slighting itself
- forfeiting its integrity
- doing itself a disservice
- evincing a lack of backbone
***
Search: What are phrase synonyms for "
```
```
- nebraska
- unicamerical legislature
- different from federal house and senate
text: featuring a unicameral legislature, nebraska's political system stands in stark contrast to the federal model, comprised of a house and senate.
***
- penny has practically no value
- should be taken out of circulation
- just as other coins have been in us history
- lost use
- value not enough
- to make environmental consequences worthy
text: all but valueless, the penny should be retired. as with other coins in american history, it has become defunct. too minute to warrant the environmental consequences of its production, it has outlived its usefulness.
***
-
```
```
original: sports teams are profitable for owners. [MASK], their valuations experience a dramatic uptick.
infill: sports teams are profitable for owners. ( accumulating vast sums / stockpiling treasure / realizing benefits / cashing in / registering robust financials / scoring on balance sheets ), their valuations experience a dramatic uptick.
***
original:
```
```
wordy: classical music is becoming less popular more and more.
Translate into Concise Text: interest in classic music is fading.
***
wordy:
```
```
sweet: savvy voters ousted him.
longer: voters who were informed delivered his defeat.
***
sweet:
```
```
1: commercial space company spacex plans to launch a whopping 52 flights in 2022.
2: spacex, a commercial space company, intends to undertake a total of 52 flights in 2022.
3: in 2022, commercial space company spacex has its sights set on undertaking 52 flights.
4: 52 flights are in the pipeline for 2022, according to spacex, a commercial space company.
5: a commercial space company, spacex aims to conduct 52 flights in 2022.
***
1:
```
Keywords to sentences or sentence.
```
ngos are characterized by:
□ voluntary citizens' group that is organized on a local, national or international level
□ encourage political participation
□ often serve humanitarian functions
□ work for social, economic, or environmental change
***
what are the drawbacks of living near an airbnb?
□ noise
□ parking
□ traffic
□ security
□ strangers
***
```
```
original: musicals generally use spoken dialogue as well as songs to convey the story. operas are usually fully sung.
adapted: musicals generally use spoken dialogue as well as songs to convey the story. ( in a stark departure / on the other hand / in contrast / by comparison / at odds with this practice / far from being alike / in defiance of this standard / running counter to this convention ), operas are usually fully sung.
***
original: akoya and tahitian are types of pearls. akoya pearls are mostly white, and tahitian pearls are naturally dark.
adapted: akoya and tahitian are types of pearls. ( a far cry from being indistinguishable / easily distinguished / on closer inspection / setting them apart / not to be mistaken for one another / hardly an instance of mere synonymy / differentiating the two ), akoya pearls are mostly white, and tahitian pearls are naturally dark.
***
original:
```
```
original: had trouble deciding.
translated into journalism speak: wrestled with the question, agonized over the matter, furrowed their brows in contemplation.
***
original:
```
```
input: not loyal
1800s english: ( two-faced / inimical / perfidious / duplicitous / mendacious / double-dealing / shifty ).
***
input:
```
```
first: ( was complicit in / was involved in ).
antonym: ( was blameless / was not an accomplice to / had no hand in / was uninvolved in ).
***
first: ( have no qualms about / see no issue with ).
antonym: ( are deeply troubled by / harbor grave reservations about / have a visceral aversion to / take ( umbrage at / exception to ) / are wary of ).
***
first: ( do not see eye to eye / disagree often ).
antonym: ( are in sync / are united / have excellent rapport / are like-minded / are in step / are of one mind / are in lockstep / operate in perfect harmony / march in lockstep ).
***
first:
```
```
stiff with competition, law school {A} is the launching pad for countless careers, {B} is a crowded field, {C} ranks among the most sought-after professional degrees, {D} is a professional proving ground.
***
languishing in viewership, saturday night live {A} is due for a creative renaissance, {B} is no longer a ratings juggernaut, {C} has been eclipsed by its imitators, {C} can still find its mojo.
***
dubbed the "manhattan of the south," atlanta {A} is a bustling metropolis, {B} is known for its vibrant downtown, {C} is a city of rich history, {D} is the pride of georgia.
***
embattled by scandal, harvard {A} is feeling the heat, {B} cannot escape the media glare, {C} is facing its most intense scrutiny yet, {D} is in the spotlight for all the wrong reasons.
```
Infill / Infilling / Masking / Phrase Masking
```
his contention [blank] by the evidence [sep] was refuted [answer]
***
few sights are as [blank] new york city as the colorful, flashing signage of its bodegas [sep] synonymous with [answer]
***
when rick won the lottery, all of his distant relatives [blank] his winnings [sep] clamored for [answer]
***
the library’s quiet atmosphere encourages visitors to [blank] in their work [sep] immerse themselves [answer]
***
``` |
Barleysack/AERoberta2 | [
"pytorch",
"roberta",
"question-answering",
"transformers",
"autotrain_compatible"
] | question-answering | {
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"RobertaForQuestionAnswering"
],
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}
}
} | 2 | null | ---
license: mit
tags:
- image-to-text
- image-captioning
---
A model that inputs chemistry journal article table of contents (ToC) images and generates appropriate titles. Trained on all JACS ToCs and titles. |
Barytes/hellohf | [
"tf",
"bert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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"max_length": null
},
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},
"translation_en_to_fr": {
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},
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}
}
} | 2 | null | ---
tags:
- generated_from_trainer
datasets:
- conll2003
model-index:
- name: scibert-lm-v2-finetuned-20
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. -->
# scibert-lm-v2-finetuned-20
This model is a fine-tuned version of [allenai/scibert_scivocab_cased](https://huggingface.co/allenai/scibert_scivocab_cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 15.7952
## 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: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0211 | 1.0 | 878 | 15.1971 |
| 0.0001 | 2.0 | 1756 | 16.8774 |
| 0.0001 | 3.0 | 2634 | 15.7952 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BatuhanYilmaz/dummy | [] | null | {
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}
} | 0 | null | ---
tags:
- generated_from_trainer
model-index:
- name: multi_news_article_title_25000_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multi_news_article_title_25000_2
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1740
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3431 | 0.32 | 500 | 0.2731 |
| 0.2136 | 0.64 | 1000 | 0.2028 |
| 0.215 | 0.96 | 1500 | 0.1880 |
| 0.1972 | 1.28 | 2000 | 0.1809 |
| 0.1903 | 1.6 | 2500 | 0.1760 |
| 0.1886 | 1.92 | 3000 | 0.1740 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Baybars/wav2vec2-xls-r-1b-turkish | [
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"tr",
"dataset:common_voice",
"transformers",
"common_voice",
"generated_from_trainer"
] | automatic-speech-recognition | {
"architectures": [
"Wav2Vec2ForCTC"
],
"model_type": "wav2vec2",
"task_specific_params": {
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},
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}
}
} | 13 | null | ---
license: apache-2.0
tags:
- summarization
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mt5-small-finetuned-amazon-en-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. -->
# mt5-small-finetuned-amazon-en-es
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0294
- Rouge1: 16.497
- Rouge2: 8.0618
- Rougel: 16.2979
- Rougelsum: 16.1465
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5.6e-05
- train_batch_size: 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|
| 6.5928 | 1.0 | 1209 | 3.3005 | 14.7843 | 6.5518 | 14.2805 | 14.2951 |
| 3.9024 | 2.0 | 2418 | 3.1399 | 16.8202 | 8.6739 | 16.1194 | 16.0844 |
| 3.5806 | 3.0 | 3627 | 3.0869 | 18.1223 | 9.3051 | 17.7533 | 17.7254 |
| 3.4201 | 4.0 | 4836 | 3.0590 | 17.654 | 9.0154 | 17.1853 | 17.1769 |
| 3.3202 | 5.0 | 6045 | 3.0598 | 17.612 | 8.6707 | 17.4662 | 17.2963 |
| 3.2436 | 6.0 | 7254 | 3.0409 | 16.7938 | 8.3054 | 16.6141 | 16.4853 |
| 3.2079 | 7.0 | 8463 | 3.0332 | 16.7246 | 8.2362 | 16.5065 | 16.3611 |
| 3.1801 | 8.0 | 9672 | 3.0294 | 16.497 | 8.0618 | 16.2979 | 16.1465 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BeIR/query-gen-msmarco-t5-base-v1 | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 1,816 | null | ---
language:
- zh
tags:
- zh
- zh-tw
- generated_from_trainer
model-index:
- name: sentcore
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. -->
# sentcore
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Tokenizers 0.12.1
|
Beatriz/model_name | [] | null | {
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}
} | 0 | null | ---
tags:
- autotrain
- summarization
language:
- unk
datasets:
- vishw2703/autotrain-data-unisumm_3
co2_eq_emissions:
emissions: 1368.894142563709
---
# Model Trained Using AutoTrain
- Problem type: Summarization
- Model ID: 1228646724
- CO2 Emissions (in grams): 1368.8941
## Validation Metrics
- Loss: 2.319
- Rouge1: 43.703
- Rouge2: 16.106
- RougeL: 23.715
- RougeLsum: 38.984
- Gen Len: 141.091
## Usage
You can use cURL to access this model:
```
$ curl -X POST -H "Authorization: Bearer YOUR_HUGGINGFACE_API_KEY" -H "Content-Type: application/json" -d '{"inputs": "I love AutoTrain"}' https://api-inference.huggingface.co/vishw2703/autotrain-unisumm_3-1228646724
``` |
Bee-Garbs/DialoGPT-real-cartman-small | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
}
} | 10 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 193.11 +/- 17.14
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Beelow/wav2vec2-ukrainian-model-large | [] | null | {
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}
} | 0 | null | ---
tags:
- image-classification
- timm
library_tag: timm
datasets:
- beans
widget:
- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/healthy.jpeg
example_title: Healthy
- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/angular_leaf_spot.jpeg
example_title: Angular Leaf Spot
- src: https://huggingface.co/nateraw/vit-base-beans/resolve/main/bean_rust.jpeg
example_title: Bean Rust
---
# Model card for timm-mobilevitv2_050-beans
This model is a fine-tuned version of `mobilevitv2_050` (from timm) on the `beans` dataset. It achieves the following results on the validation set:
- Loss: 0.08228
- Accuracy: 0.9850
- F1Score: 0.9846
## Image normalization
Imagenet
```python
mean = [0.485, 0.456, 0.406]
std = [0.229, 0.224, 0.225]
``` |
Begimay/Task | [] | null | {
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}
} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: eliwill/distilgpt2-discursive-krishna
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# eliwill/distilgpt2-discursive-krishna
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.2503
- Validation Loss: 3.1371
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.2503 | 3.1371 | 0 |
### Framework versions
- Transformers 4.21.1
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Bella4322/Sarah | [] | null | {
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} | 0 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-0.5
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 1.2575
---
<!-- 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. -->
# distilled-mt5-small-0.5
This model is a distilled version of [Lvxue/finetuned-mt5-base](https://huggingface.co/Lvxue/finetuned-mt5-base) on [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7455
- Bleu: 1.2575
- Gen Len: 94.3597
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
tags:
- exbert
- question-answering
language:
- multilingual
- cs
- en
---
# XLM RoBERTa for Czech+English Extractive Question Answering
This is the [XLM-RoBERTa-large](https://huggingface.co/xlm-roberta-large) model with a head for extractive question answering trained on a combination of [English SQuAD 1.1](https://huggingface.co/datasets/squad) and [Czech SQAD 3.0](https://lindat.cz/repository/xmlui/handle/11234/1-3069) Question Answering datasets. For the Czech SQAD 3.0, original contexts (=whole Wikipedia websites) were limited to fit the RoBERTa's context window, excluding ~3% of the samples.
## Intended uses & limitations
This model is purposed to extract a segment of a given context that contains an answer to a given question (Extractive Question Answering) in English and Czech.
Given the fine-tuning on two languages and a good reported zero-shot cross-lingual applicability of other fine-tuned XLM-RoBERTas, the model will likely work on other languages as well, with a decay in quality.
Note that despite its size, English SQuAD has a variety of reported biases (see, e.g. [L. Mikula (2022)](https://is.muni.cz/th/adh58/?lang=en), Chap. 4.1).
## Usage
Here is how to use this model to answer the question on a given context using 🤗 Transformers in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
tokenizer = AutoTokenizer.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs")
model = AutoModelForQuestionAnswering.from_pretrained("gaussalgo/xlm-roberta-large_extractive-QA_en-cs")
context = """
Podle slovenského lidového podání byl Juro Jánošík obdařen magickými předměty (kouzelná valaška, čarovný opasek),
které mu dodávaly nadpřirozené schopnosti. Okrádal především šlechtice,
trestal panské dráby a ze svého lupu vyděloval část pro chudé, tedy bohatým bral a chudým dával.
"""
question = "Jaké schopnosti daly magické předměty Juro Jánošíkovi?"
inputs = tokenizer(question, context, return_tensors="pt")
outputs = model(**inputs)
start_position = outputs.start_logits[0].argmax()
end_position = outputs.end_logits[0].argmax()
answer_ids = inputs["input_ids"][0][start_position:end_position]
print("Answer:")
print(tokenizer.decode(answer_ids))
```
## Training
The model has been trained using [Adaptor library](https://github.com/gaussalgo/adaptor) v0.1.5, in parallel on both Czech and English data, with the following parameters:
```python
training_arguments = AdaptationArguments(output_dir="train_dir",
learning_rate=1e-5,
stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
do_train=True,
do_eval=True,
warmup_steps=1000,
max_steps=100000,
gradient_accumulation_steps=30,
eval_steps=100,
logging_steps=10,
save_steps=1000,
num_train_epochs=30,
evaluation_strategy="steps")
```
You can find the full training script in [train_roberta_extractive_qa.py](train_roberta_extractive_qa.py), reproducible after a specific data preprocessing for Czech SQAD in [parse_czech_squad.py](parse_czech_squad.py) |
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} | 0 | null |
---
language:
- ja
license:
- cc-by-4.0
tags:
- NER
- medical documents
datasets:
- MedTxt-CR-JA-training-v2.xml
metrics:
- NTCIR-16 Real-MedNLP subtask 1
---
This is a model for named entity recognition of Japanese medical documents.
### How to use
Download the following five files and put into the same folder.
- id_to_tags.pkl
- key_attr.pkl
- text.txt
- NER_medNLP.py
- predict.py
You can use this model by running predict.py.
```
python3 predict.py
```
### Input Example
```
肥大型心筋症、心房細動に対してＷＦ投与が開始となった。
治療経過中に非持続性心室頻拍が認められたためアミオダロンが併用となった。
```
### Output Example
```
<d certainty="positive">肥大型心筋症、心房細動</d>に対して<m-key state="executed">ＷＦ</m-key>投与が開始となった。
<timex3 type="med">治療経過中</timex3>に<d certainty="positive">非持続性心室頻拍</d>が認められたため<m-key state="executed">アミオダロン</m-key>が併用となった。
``` |
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} | 0 | null | ---
license: apache-2.0
---
使用seq-seq模型
encoder_id = "wbbbbb/wav2vec2-large-chinese-zh-cn"
decoder_id = "IDEA-CCNL/Randeng-BART-139M
wer=68.3 |
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} | 0 | 2022-08-08T09:22:10Z | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [🤗 Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
📈 [TensorBoard logs](https://huggingface.co/mohammadhadiarabi/ddpm-butterflies-128/tensorboard?#scalars)
|
Bharathdamu/wav2vec2-large-xls-r-300m-hindi3-colab | [] | null | {
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} | 0 | null | ---
language: en
tags:
- summarization
- biomedical papers
widget:
- text: "Biomedical paper of choice \U0001F917"
datasets:
- Blaise-g/SumPubmed
--- |
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-optimised-finetuned-financial-sentiment
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-optimised-finetuned-financial-sentiment
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3112
- Accuracy: 0.8582
- F1: 0.8581
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.5877 | 1.0 | 561 | 0.3070 | 0.8455 | 0.8459 |
| 0.2304 | 2.0 | 1122 | 0.3112 | 0.8582 | 0.8581 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/BertaMyWorda | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 8 | null | ---
license: mit
language: en
---
# T5(v1.1)-SLED (SLiding-Encoder and Decoder, base-sized model)
SLED models use pretrained, short-range encoder-decoder models, and apply them over
long-text inputs by splitting the input into multiple overlapping chunks, encoding each independently and perform fusion-in-decoder
## Model description
This SLED model is based on the T5(V1.1) model, which is described in its [model card](https://huggingface.co/google/t5-v1_1-base).
The developers write in a [blog post](https://ai.googleblog.com/2020/02/exploring-transfer-learning-with-t5.html) that the T5 model:
> Our text-to-text framework allows us to use the same model, loss function, and hyperparameters on any NLP task, including machine translation, document summarization, question answering, and classification tasks (e.g., sentiment analysis). We can even apply T5 to regression tasks by training it to predict the string representation of a number instead of the number itself.
T5 v1.1 includes several improvments on top of the original checkpoint. see its card for details
## Intended uses & limitations
You can use the raw model for text infilling. However, the model is mostly meant to be fine-tuned on a supervised dataset.
### How to use
To use the model, you first need to install `py-sled` in your environment (or clone the code from the [official repository](https://github.com/Mivg/SLED/blob/main/README.md))
```
pip install py-sled
```
For more installation instructions, see [here](https://github.com/Mivg/SLED#Installation).
Once installed, SLED is fully compatible with HuggingFace's AutoClasses (AutoTokenizer, AutoConfig, AutoModel
and AutoModelForCausalLM) and can be loaded using the from_pretrained methods
```python
import sled # *** required so that SledModels will be registered for the AutoClasses ***
model = AutoModel.from_pretrained('tau/t5-v1_1-base-sled')
```
Here is how to use this model in PyTorch:
```python
from sled import SledTokenizer, SledModel
tokenizer = SledTokenizer.from_pretrained('tau/t5-v1_1-base-sled')
model = SledModel.from_pretrained('tau/t5-v1_1-base-sled')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
You can also replace SledModel by SledModelForConditionalGeneration for Seq2Seq generation
```python
model = SledModelForConditionalGeneration.from_pretrained('tau/t5-v1_1-base-sled')
```
In case you wish to apply SLED on a task containing a prefix (e.g. question) which should be given as a context to
every chunk, you can pass the `prefix_length` tensor input as well (A LongTensor in the length of the batch size).
```python
import torch
import sled # *** required so that SledModels will be registered for the AutoClasses ***
tokenizer = AutoTokenizer.from_pretrained('tau/t5-v1_1-base-sled')
model = AutoModel.from_pretrained('tau/t5-v1_1-base-sled')
document_input_ids = tokenizer("Dogs are great for you.", return_tensors="pt").input_ids
prefix_input_ids = tokenizer("Are dogs good for you?", return_tensors="pt").input_ids
input_ids = torch.cat((prefix_input_ids, document_input_ids), dim=-1)
attention_mask = torch.ones_like(input_ids)
prefix_length = torch.LongTensor([[prefix_input_ids.size(1)]])
outputs = model(input_ids=input_ids, attention_mask=attention_mask, prefix_length=prefix_length)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
Please cite both the SLED [paper](https://arxiv.org/abs/2208.00748.pdf) and the T5 [paper](https://arxiv.org/pdf/1910.10683.pdf) by Raffel et al
```bibtex
@inproceedings{Ivgi2022EfficientLU,
title={Efficient Long-Text Understanding with Short-Text Models},
author={Maor Ivgi and Uri Shaham and Jonathan Berant},
year={2022}
}
```
```bibtex
@article{2020t5,
author = {Colin Raffel and Noam Shazeer and Adam Roberts and Katherine Lee and Sharan Narang and Michael Matena and Yanqi Zhou and Wei Li and Peter J. Liu},
title = {Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer},
journal = {Journal of Machine Learning Research},
year = {2020},
volume = {21},
number = {140},
pages = {1-67},
url = {http://jmlr.org/papers/v21/20-074.html}
}
``` |
BigSalmon/GPT2HardArticleEasyArticle | [
"pytorch",
"jax",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
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} | 7 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Worm
library_name: ml-agents
---
# **ppo** Agent playing **Worm**
This is a trained model of a **ppo** agent playing **Worm** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Worm
2. Step 1: Write your model_id: mrm8488/Worm
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/GPT2HardandEasy | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
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} | 9 | null | ---
tags:
- generated_from_trainer
model-index:
- name: multi_news_article_title_12000_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# multi_news_article_title_12000_2
This model is a fine-tuned version of [google/pegasus-multi_news](https://huggingface.co/google/pegasus-multi_news) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1917
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3887 | 0.65 | 500 | 0.2781 |
| 0.2484 | 1.31 | 1000 | 0.2000 |
| 0.2314 | 1.96 | 1500 | 0.1917 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/GPTIntro | [] | null | {
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} | 0 | null | ---
tags:
- generation
language:
- multilingual
- cs
- en
---
# Mt5-base for Czech+English Generative Question Answering
This is the [mt5-base](https://huggingface.co/google/mt5-base) model with an LM head for a generation of extractive answers. In contrary to our [mt5-base-priming](https://huggingface.co/gaussalgo/mt5-base-priming-QA_en-cs/edit/main/README.md), this is a traditional sequence2sequence model without priming, though can also be used on other Text extraction tasks, such as Named Entity Recognition in zero-shot settings (with a significant decay in quality, compared to priming).
## Intended uses & limitations
This model is purposed to *generate* a segment of a given context that contains an answer to a given question (Extractive Question Answering) in English and Czech.
Given the fine-tuning on two languages and a good reported zero-shot cross-lingual applicability of other fine-tuned multilingual large language models, the model will likely also work on other languages as well, with a specific decay in quality.
Note that despite its size, English SQuAD has a variety of reported biases,
conditioned by the relative position or type of the answer in the context that can affect the model's performance on new data
(see, e.g. [L. Mikula (2022)](https://is.muni.cz/th/adh58/?lang=en), Chap. 4.1).
## Usage
Here is how to use this model to answer the question on a given context using 🤗 Transformers in PyTorch:
```python
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("gaussalgo/mt5-base-generative-QA_en-cs")
model = AutoModelForSeq2SeqLM.from_pretrained("gaussalgo/mt5-base-generative-QA_en-cs")
context = """
Podle slovenského lidového podání byl Juro Jánošík obdařen magickými předměty (kouzelná valaška, čarovný opasek),
které mu dodávaly nadpřirozené schopnosti. Okrádal především šlechtice,
trestal panské dráby a ze svého lupu vyděloval část pro chudé, tedy bohatým bral a chudým dával.
"""
question = "Jaké schopnosti daly magické předměty Juro Jánošíkovi?"
inputs = tokenizer(question, context, return_tensors="pt")
outputs = model.generate(**inputs)
print("Answer:")
print(tokenizer.decode(outputs))
```
## Training
The model has been trained using [Adaptor library](https://github.com/gaussalgo/adaptor) v0.1.5, in parallel on both Czech and English data, with the following parameters:
```python
training_arguments = AdaptationArguments(output_dir="train_dir",
learning_rate=5e-5,
stopping_strategy=StoppingStrategy.ALL_OBJECTIVES_CONVERGED,
do_train=True,
do_eval=True,
warmup_steps=1000,
max_steps=100000,
gradient_accumulation_steps=4,
eval_steps=100,
logging_steps=10,
save_steps=1000,
num_train_epochs=50,
evaluation_strategy="steps",
remove_unused_columns=False)
```
You can find the full training script in [train_mt5_qa_en+cs.py](https://huggingface.co/gaussalgo/mt5-base-generative-QA_en-cs/blob/main/train_mt5_qa_en%2Bcs.py), reproducible after a specific data preprocessing for Czech SQAD in [parse_czech_squad.py](parse_czech_squad.py) |
BigSalmon/InformalToFormalLincoln16 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
],
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}
} | 8 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-uncased-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-squad
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 3
- training precision: Mixed Precision
### Training results
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.0+cpu
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BigSalmon/InformalToFormalLincoln20 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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}
} | 8 | null | ---
library_name: stable-baselines3
tags:
- AntBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: 911.55 +/- 62.62
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: AntBulletEnv-v0
type: AntBulletEnv-v0
---
# **A2C** Agent playing **AntBulletEnv-v0**
This is a trained model of a **A2C** agent playing **AntBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/MrLincoln10 | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
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"GPT2LMHeadModel"
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}
} | 5 | null | ---
library_name: stable-baselines3
tags:
- HumanoidFlagrunBulletEnv-v0
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- metrics:
- type: mean_reward
value: -61.72 +/- 16.30
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: HumanoidFlagrunBulletEnv-v0
type: HumanoidFlagrunBulletEnv-v0
---
# **A2C** Agent playing **HumanoidFlagrunBulletEnv-v0**
This is a trained model of a **A2C** agent playing **HumanoidFlagrunBulletEnv-v0**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
BigSalmon/MrLincoln12 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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"GPT2LMHeadModel"
],
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}
}
} | 9 | null | ---
tags:
- stanza
- token-classification
library_name: stanza
language: bxr
license: apache-2.0
---
# Stanza model for Buryat (bxr)
Stanza is a collection of accurate and efficient tools for the linguistic analysis of many human languages. Starting from raw text to syntactic analysis and entity recognition, Stanza brings state-of-the-art NLP models to languages of your choosing.
Find more about it in [our website](https://stanfordnlp.github.io/stanza) and our [GitHub repository](https://github.com/stanfordnlp/stanza).
This card and repo were automatically prepared with `hugging_stanza.py` in the `stanfordnlp/huggingface-models` repo
Last updated 2023-05-19 03:30:48.254
|
BigSalmon/ParaphraseParentheses2.0 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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"max_length": 50
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}
} | 13 | null | ---
language:
- en
tags:
- pytorch
- ner
- text generation
- seq2seq
inference: false
license: mit
datasets:
- conll2003
metrics:
- f1
---
# t5-base-qa-ner-conll
Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
t5-base model tuned on conll2003 dataset.
https://github.com/ovbystrova/InstructionNER
## Inference
```shell
git clone https://github.com/ovbystrova/InstructionNER
cd InstructionNER
```
```python
from instruction_ner.model import Model
model = Model(
model_path_or_name="olgaduchovny/t5-base-ner-mit-restaurant",
tokenizer_path_or_name="olgaduchovny/t5-base-mit-restaurant"
)
options = ["LOC", "PER", "ORG", "MISC"]
instruction = "please extract entities and their types from the input sentence, " \
"all entity types are in options"
text = "Once I visited Sovok in Nizny Novgorod. I had asian wok there. It was the best WOK i ever had"\
"It was cheap but lemonades cost 5 dollars."
generation_kwargs = {
"num_beams": 2,
"max_length": 128
}
pred_spans = model.predict(
text=text,
generation_kwargs=generation_kwargs,
instruction=instruction,
options=options
)
>>> ('sovok is a Restaurant_Name, Nizny Novgorod is a Location, asian wok is a Dish, cheap is a Price, lemonades is a Dish, 5 dollars is a Price.',
[(24, 38, 'Location'),
(46, 55, 'Dish'),
(100, 105, 'Price'),
(110, 119, 'Dish'),
(125, 134, 'Price')])
``` |
BigSalmon/PhraseBerta | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"RobertaForMaskedLM"
],
"model_type": "roberta",
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}
} | 10 | null |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
library_name: ml-agents
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-Pyramids
2. Step 1: Write your model_id: andres-hsn/testpyramidsrnd
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
BigSalmon/T5Salmon | [
"pytorch",
"jax",
"t5",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"T5ForConditionalGeneration"
],
"model_type": "t5",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": true,
"length_penalty": 2,
"max_length": 200,
"min_length": 30,
"no_repeat_ngram_size": 3,
"num_beams": 4,
"prefix": "summarize: "
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to German: "
},
"translation_en_to_fr": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to French: "
},
"translation_en_to_ro": {
"early_stopping": true,
"max_length": 300,
"num_beams": 4,
"prefix": "translate English to Romanian: "
}
}
} | 6 | null | ---
license: mit
---
A simple single label classification model, ResNet18, to predict the cat or dog breed from the provided image. The model was created in Fast.ai
and exported to ONNX using PyTorch's ONNX export capabilities.
The source dataset is the OXFORD-IIIT PET. Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman and C. V. Jawahar
We have created a 37 category pet dataset with roughly 200 images for each class.
The images have a large variations in scale, pose and lighting. All images havean
associated ground truth annotation of breed, head ROI, and pixel level trimap segmentation.
The ONNX model can be used in other frameworks like Elixir's Axon. An example of converting the ONNX model into Axon can be found at:
https://github.com/elixir-nx/axon/tree/main/notebooks/onnx_to_axon.livemd.
|
BigTooth/DialoGPT-Megumin | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
}
} | 16 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: 3-way-detection-prop-16
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. -->
# 3-way-detection-prop-16
This model is a fine-tuned version of [ultra-coder54732/3-way-detection-prop-16](https://huggingface.co/ultra-coder54732/3-way-detection-prop-16) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BigTooth/Megumin-v0.2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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}
} | 13 | null | ---
language: en
tags:
- exbert
license: mit
---
# GPT-2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Model description
GPT-2 is a transformers model pretrained on a very large corpus of English data in a self-supervised fashion. This
means it was pretrained on the raw texts only, with no humans labelling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifted one token (word or piece of word) to the right. The model uses internally a mask-mechanism to make sure the
predictions for the token `i` only uses the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was pretrained for however, which is generating texts from a
prompt.
## Intended uses & limitations
You can use the raw model for text generation or fine-tune it to a downstream task. See the
[model hub](https://huggingface.co/models?filter=gpt2) to look for fine-tuned versions on a task that interests you.
### How to use
You can use this model directly with a pipeline for text generation. Since the generation relies on some randomness, we
set a seed for reproducibility:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("Hello, I'm a language model,", max_length=30, num_return_sequences=5)
[{'generated_text': "Hello, I'm a language model, a language for thinking, a language for expressing thoughts."},
{'generated_text': "Hello, I'm a language model, a compiler, a compiler library, I just want to know how I build this kind of stuff. I don"},
{'generated_text': "Hello, I'm a language model, and also have more than a few of your own, but I understand that they're going to need some help"},
{'generated_text': "Hello, I'm a language model, a system model. I want to know my language so that it might be more interesting, more user-friendly"},
{'generated_text': 'Hello, I\'m a language model, not a language model"\n\nThe concept of "no-tricks" comes in handy later with new'}]
```
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import GPT2Tokenizer, GPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2Model.from_pretrained('gpt2')
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 GPT2Tokenizer, TFGPT2Model
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = TFGPT2Model.from_pretrained('gpt2')
text = "Replace me by any text you'd like."
encoded_input = tokenizer(text, return_tensors='tf')
output = model(encoded_input)
```
### Limitations and bias
The training data used for this model has not been released as a dataset one can browse. We know it contains a lot of
unfiltered content from the internet, which is far from neutral. As the openAI team themselves point out in their
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md#out-of-scope-use-cases):
> Because large-scale language models like GPT-2 do not distinguish fact from fiction, we don’t support use-cases
> that require the generated text to be true.
>
> Additionally, language models like GPT-2 reflect the biases inherent to the systems they were trained on, so we do
> not recommend that they be deployed into systems that interact with humans > unless the deployers first carry out a
> study of biases relevant to the intended use-case. We found no statistically significant difference in gender, race,
> and religious bias probes between 774M and 1.5B, implying all versions of GPT-2 should be approached with similar
> levels of caution around use cases that are sensitive to biases around human attributes.
Here's an example of how the model can have biased predictions:
```python
>>> from transformers import pipeline, set_seed
>>> generator = pipeline('text-generation', model='gpt2')
>>> set_seed(42)
>>> generator("The White man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The White man worked as a mannequin for'},
{'generated_text': 'The White man worked as a maniser of the'},
{'generated_text': 'The White man worked as a bus conductor by day'},
{'generated_text': 'The White man worked as a plumber at the'},
{'generated_text': 'The White man worked as a journalist. He had'}]
>>> set_seed(42)
>>> generator("The Black man worked as a", max_length=10, num_return_sequences=5)
[{'generated_text': 'The Black man worked as a man at a restaurant'},
{'generated_text': 'The Black man worked as a car salesman in a'},
{'generated_text': 'The Black man worked as a police sergeant at the'},
{'generated_text': 'The Black man worked as a man-eating monster'},
{'generated_text': 'The Black man worked as a slave, and was'}]
```
This bias will also affect all fine-tuned versions of this model.
## Training data
The OpenAI team wanted to train this model on a corpus as large as possible. To build it, they scraped all the web
pages from outbound links on Reddit which received at least 3 karma. Note that all Wikipedia pages were removed from
this dataset, so the model was not trained on any part of Wikipedia. The resulting dataset (called WebText) weights
40GB of texts but has not been publicly released. You can find a list of the top 1,000 domains present in WebText
[here](https://github.com/openai/gpt-2/blob/master/domains.txt).
## Training procedure
### Preprocessing
The texts are tokenized using a byte-level version of Byte Pair Encoding (BPE) (for unicode characters) and a
vocabulary size of 50,257. The inputs are sequences of 1024 consecutive tokens.
The larger model was trained on 256 cloud TPU v3 cores. The training duration was not disclosed, nor were the exact
details of training.
## Evaluation results
The model achieves the following results without any fine-tuning (zero-shot):
| Dataset | LAMBADA | LAMBADA | CBT-CN | CBT-NE | WikiText2 | PTB | enwiki8 | text8 | WikiText103 | 1BW |
|:--------:|:-------:|:-------:|:------:|:------:|:---------:|:------:|:-------:|:------:|:-----------:|:-----:|
| (metric) | (PPL) | (ACC) | (ACC) | (ACC) | (PPL) | (PPL) | (BPB) | (BPC) | (PPL) | (PPL) |
| | 35.13 | 45.99 | 87.65 | 83.4 | 29.41 | 65.85 | 1.16 | 1,17 | 37.50 | 75.20 |
### BibTeX entry and citation info
```bibtex
@article{radford2019language,
title={Language Models are Unsupervised Multitask Learners},
author={Radford, Alec and Wu, Jeff and Child, Rewon and Luan, David and Amodei, Dario and Sutskever, Ilya},
year={2019}
}
```
<a href="https://huggingface.co/exbert/?model=gpt2">
<img width="300px" src="https://cdn-media.huggingface.co/exbert/button.png">
</a>
|
BigeS/DialoGPT-small-Rick | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: bart-paraphrase-v8-e1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-paraphrase-v8-e1
This model is a fine-tuned version of [eugenesiow/bart-paraphrase](https://huggingface.co/eugenesiow/bart-paraphrase) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1597
- Rouge1: 73.0494
- Rouge2: 70.2389
- Rougel: 72.0086
- Rougelsum: 72.1
- Gen Len: 19.7365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| 0.0312 | 1.0 | 28370 | 0.1597 | 73.0494 | 70.2389 | 72.0086 | 72.1 | 19.7365 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BillelBenoudjit/jplu-wikiann | [
"fr",
"dataset:wikiann",
"model-index"
] | null | {
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} | 0 | null | ---
language: en
thumbnail: http://www.huggingtweets.com/apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot/1659995519837/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1196519479364268034/5QpniWSP_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1326378564187529216/a9fuWw48_400x400.jpg')">
</div>
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1261895681561804800/r6vOZGoH_400x400.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Humongous Ape MP & tanakhbot & GPT2-Microfic & MORTIMUS COWBOY: The Bastard of Diapers & wint & wint but Al</div>
<div style="text-align: center; font-size: 14px;">@apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Humongous Ape MP & tanakhbot & GPT2-Microfic & MORTIMUS COWBOY: The Bastard of Diapers & wint & wint but Al.
| Data | Humongous Ape MP | tanakhbot | GPT2-Microfic | MORTIMUS COWBOY: The Bastard of Diapers | wint | wint but Al |
| --- | --- | --- | --- | --- | --- | --- |
| Tweets downloaded | 3245 | 565 | 1158 | 3249 | 3226 | 3229 |
| Retweets | 197 | 0 | 11 | 0 | 497 | 47 |
| Short tweets | 610 | 1 | 9 | 143 | 287 | 57 |
| Tweets kept | 2438 | 564 | 1138 | 3106 | 2442 | 3125 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2rmkgg2i/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/6iovvvgz) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/6iovvvgz/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/apesahoy-dril-dril9999-dril_gpt2-gptmicrofic-tanakhbot')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
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} | 0 | null | ---
language: en
license: mit
datasets:
- bookcorpus
- wikipedia
---
# XLNet (base-sized model)
XLNet model pre-trained on English language. It was introduced in the paper [XLNet: Generalized Autoregressive Pretraining for Language Understanding](https://arxiv.org/abs/1906.08237) by Yang et al. and first released in [this repository](https://github.com/zihangdai/xlnet/).
Disclaimer: The team releasing XLNet did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
XLNet is a new unsupervised language representation learning method based on a novel generalized permutation language modeling objective. Additionally, XLNet employs Transformer-XL as the backbone model, exhibiting excellent performance for language tasks involving long context. Overall, XLNet achieves state-of-the-art (SOTA) results on various downstream language tasks including question answering, natural language inference, sentiment analysis, and document ranking.
## Intended uses & limitations
The model is mostly intended to be fine-tuned on a downstream task. See the [model hub](https://huggingface.co/models?search=xlnet) to look for fine-tuned versions on a task that interests you.
Note that this model is primarily aimed at being fine-tuned on tasks that use the whole sentence (potentially masked) to make decisions, such as sequence classification, token classification or question answering. For tasks such as text generation, you should look at models like GPT2.
## Usage
Here is how to use this model to get the features of a given text in PyTorch:
```python
from transformers import XLNetTokenizer, XLNetModel
tokenizer = XLNetTokenizer.from_pretrained('xlnet-base-cased')
model = XLNetModel.from_pretrained('xlnet-base-cased')
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{DBLP:journals/corr/abs-1906-08237,
author = {Zhilin Yang and
Zihang Dai and
Yiming Yang and
Jaime G. Carbonell and
Ruslan Salakhutdinov and
Quoc V. Le},
title = {XLNet: Generalized Autoregressive Pretraining for Language Understanding},
journal = {CoRR},
volume = {abs/1906.08237},
year = {2019},
url = {http://arxiv.org/abs/1906.08237},
eprinttype = {arXiv},
eprint = {1906.08237},
timestamp = {Mon, 24 Jun 2019 17:28:45 +0200},
biburl = {https://dblp.org/rec/journals/corr/abs-1906-08237.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
|
Biniam/en_ti_translate | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"translation",
"autotrain_compatible"
] | translation | {
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"MarianMTModel"
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}
} | 14 | null | Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: faster_rcnn
datasets:
- coco
---
# Faster R-CNN
## Model desription
This model is an enhanced version of the [Fast R-CNN model](https://arxiv.org/pdf/1504.08083.pdf). Due to the computation bottleneck posed by Fast-RCNN that saw the innovation of Region of Pooling. Faster-RCNN introduces the Region of Proposal Network(RPN) and reuses the same CNN results for the same proposal instead of running a selective search algorithm. The RPN is trained end-to-end to generate high-quality region proposals, which Fast R-CNN uses for detection. The model merges RPN and Fast R-CNN into a single network by sharing their convolutional features. With 'attention' mechanisms, the RPN component tells the unified network where to look. This state-of-the-art object detection networks depend on region proposal algorithms to hypothesize object locations.
*This Model is based on the Pretrained model from [OpenMMlab](https://github.com/open-mmlab/mmdetection)*
### More information on the Model, Dataset, Training and Results:
#### The model
By implementing a CNN-based region proposal network, the Faster R-CNN addresses the bottleneck issue that the Fast R-CNN raised during the proposal stage. Additionally, it uses the concept of various size anchor boxes, which accelerates the object detection model. Convolution layers receive input from images and generate a feature map. We obtain the region of proposals by adding a layer of convolution to the extracted feature map.
To output the box and class information, the convolution layer traverses across the feature map at each position using a 3X3 window to create box proposals. At each output, a K number of boxes are generated at relative coordinates position from the pre-defined anchor boxes. The final box output is the probability of whether the box contains the object.
#### Datasets
[COCO Datasets](https://cocodataset.org/#home)
#### Training
Please [read the paper](https://arxiv.org/pdf/1703.06870.pdf) for more information on training, or check OpenMMLab [repository](https://github.com/open-mmlab/mmdetection/tree/master/configs/faster_rcnn)
In four stages, the model training is done:
The RPN is trained on the COCO object detection datasets in the first stage to produce the region of proposals. The trained RPN from stage one is then used to train the Fast R-CNN. Following this training, a detector network is used to initialize the RPN's training with fixed shared convolution layers, and the network's unique layers are adjusted. Finally, the last step is fine-tuning unique layers of Fast R-CNN, forming a unified network.
#### Results Summary
- The RPN model achieves better results than the one that uses selective search.
- Pascal VOC 2007 & 2012 are used for the test sets
- The selective search model takes more time(ms) than the RPN model.
## Intended uses & limitations
Due to the efficiency in learning, the training dataset is superior to ordinary CNN algorithms. Faster R-CNN has the disadvantage that the RPN is trained with all of the size 256 mini-batch anchors being taken from a single image. The network may take a long time to attain convergence because all samples from one image may be correlated. |
BinksSachary/DialoGPT-small-shaxx | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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}
} | 12 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: mal-tls-t5-l3
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal-tls-t5-l3
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
BinksSachary/ShaxxBot2 | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-bert-yoga-finetuned
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-bert-yoga-finetuned
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: 2.2067
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5956 | 1.0 | 313 | 2.3020 |
| 2.3742 | 2.0 | 626 | 2.2212 |
| 2.3034 | 3.0 | 939 | 2.1906 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Blackmist786/DialoGPt-small-transformers4 | [
"pytorch"
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} | 4 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: mal-tls-t5-l12
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mal-tls-t5-l12
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.20.1
- TensorFlow 2.6.4
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Blaine-Mason/hackMIT-finetuned-sst2 | [
"pytorch",
"tensorboard",
"bert",
"text-classification",
"dataset:glue",
"transformers",
"generated_from_trainer"
] | text-classification | {
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"BertForSequenceClassification"
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} | 36 | null | Hugging Face's logo
---
tags:
- object-detection
- vision
library_name: mask_rcnn
datasets:
- coco
---
# Mask R-CNN
## Model desription
Mask R-CNN is a model that extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. The model locates pixels of images instead of just bounding boxes as Faster R-CNN was not designed for pixel-to-pixel alignment between network inputs and outputs.
*This Model is based on the Pretrained model from [OpenMMlab](https://github.com/open-mmlab/mmdetection)*
### More information on the model and dataset:
#### The model
Mask R-CNN works towards the approach of instance segmentation, which involves object detection, and semantic segmentation. For object detection, Mask R-CNN uses an architecture that is similar to Faster R-CNN, while it uses a Fully Convolutional Network(FCN) for semantic segmentation.
The FCN is added to the top of features of a Faster R-CNN to generate a mask segmentation output. This segmentation output is in parallel with the classification and bounding box regressor network of the Faster R-CNN model. From the advancement of Fast R-CNN Region of Interest Pooling(ROI), Mask R-CNN adds refinement called ROI aligning by addressing the loss and misalignment of ROI Pooling; the new ROI aligned leads to improved results.
#### Datasets
[COCO Datasets](https://cocodataset.org/#home)
## Training Procedure
Please [read the paper](https://arxiv.org/pdf/1703.06870.pdf) for more information on training, or check OpenMMLab [repository](https://github.com/open-mmlab/mmdetection/tree/master/configs/mask_rcnn)
The model architecture is divided into two parts:
- Region proposal network (RPN) to propose candidate object bounding boxes.
- Binary mask classifier to generate a mask for every class
#### Technical Summary.
- Mask R-CNN is quite similar to the structure of faster R-CNN.
- Outputs a binary mask for each Region of Interest.
- Applies bounding-box classification and regression in parallel, simplifying the original R-CNN's multi-stage pipeline.
- The network architectures utilized are called ResNet and ResNeXt. The depth can be either 50 or 101
#### Results Summary
- Instance Segmentation: Based on the COCO dataset, Mask R-CNN outperforms all categories compared to MNC and FCIS, which are state-of-the-art models.
- Bounding Box Detection: Mask R-CNN outperforms the base variants of all previous state-of-the-art models, including the COCO 2016 Detection Challenge winner.
## Intended uses & limitations
The identification of object relationships and the context of objects in a picture are both aided by image segmentation. Some of the applications include face recognition, number plate recognition, and satellite image analysis. With great model generality, Mask RCNN can be extended to human pose estimation; it can be used to estimate on-site approaching live traffic to aid autonomous driving.
|
Blazeolmo/Scrabunzi | [] | null | {
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} | 0 | null | ---
language: vi
---
# BART-large on Vietnamese News
Details will be available soon.
For more information, please contact [email protected] (Dương).
### Important note
When finetuning this model on downstream tasks (e.g. text summarization), ensure that your label has the form of `tokenizer.bos_token + target + tokenizer.eos_token` before tokenizing. |
BlightZz/DialoGPT-medium-Kurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 19 | null | Access to model FernandoSinesio/cutlery is restricted and you are not in the authorized list. Visit https://huggingface.co/FernandoSinesio/cutlery to ask for access. |
BlightZz/MakiseKurisu | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-2_H-128_A-2-nan-labels-new-longer
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_uncased_L-2_H-128_A-2-nan-labels-new-longer
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 8.4563
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 6.445 | 1.0 | 14 | 6.4988 |
| 5.9033 | 2.0 | 28 | 6.4333 |
| 5.6732 | 3.0 | 42 | 6.4295 |
| 5.6525 | 4.0 | 56 | 6.3895 |
| 5.4781 | 5.0 | 70 | 6.3660 |
| 5.31 | 6.0 | 84 | 6.4213 |
| 5.1629 | 7.0 | 98 | 6.3842 |
| 5.1541 | 8.0 | 112 | 6.4043 |
| 5.0659 | 9.0 | 126 | 6.4686 |
| 4.8597 | 10.0 | 140 | 6.4760 |
| 4.983 | 11.0 | 154 | 6.4753 |
| 4.9563 | 12.0 | 168 | 6.4612 |
| 4.8113 | 13.0 | 182 | 6.5363 |
| 4.86 | 14.0 | 196 | 6.5400 |
| 4.8254 | 15.0 | 210 | 6.5595 |
| 4.8032 | 16.0 | 224 | 6.5575 |
| 4.7343 | 17.0 | 238 | 6.5832 |
| 4.835 | 18.0 | 252 | 6.5729 |
| 4.6986 | 19.0 | 266 | 6.5705 |
| 4.6607 | 20.0 | 280 | 6.6127 |
| 4.6594 | 21.0 | 294 | 6.6145 |
| 4.6936 | 22.0 | 308 | 6.6280 |
| 4.6328 | 23.0 | 322 | 6.6249 |
| 4.6668 | 24.0 | 336 | 6.6779 |
| 4.5523 | 25.0 | 350 | 6.6236 |
| 4.3964 | 26.0 | 364 | 6.6895 |
| 4.4736 | 27.0 | 378 | 6.6899 |
| 4.5163 | 28.0 | 392 | 6.6832 |
| 4.5136 | 29.0 | 406 | 6.7013 |
| 4.542 | 30.0 | 420 | 6.6983 |
| 4.4362 | 31.0 | 434 | 6.7516 |
| 4.4466 | 32.0 | 448 | 6.7302 |
| 4.4834 | 33.0 | 462 | 6.7404 |
| 4.4824 | 34.0 | 476 | 6.7525 |
| 4.4302 | 35.0 | 490 | 6.7623 |
| 4.5098 | 36.0 | 504 | 6.7622 |
| 4.396 | 37.0 | 518 | 6.8025 |
| 4.3747 | 38.0 | 532 | 6.7847 |
| 4.2892 | 39.0 | 546 | 6.8394 |
| 4.3897 | 40.0 | 560 | 6.8372 |
| 4.2554 | 41.0 | 574 | 6.7644 |
| 4.3336 | 42.0 | 588 | 6.7843 |
| 4.2943 | 43.0 | 602 | 6.7806 |
| 4.3001 | 44.0 | 616 | 6.7864 |
| 4.1161 | 45.0 | 630 | 6.8487 |
| 4.1357 | 46.0 | 644 | 6.8556 |
| 4.2713 | 47.0 | 658 | 6.7995 |
| 4.2779 | 48.0 | 672 | 6.8441 |
| 4.2564 | 49.0 | 686 | 6.8388 |
| 4.192 | 50.0 | 700 | 6.7896 |
| 4.1692 | 51.0 | 714 | 6.8261 |
| 4.2694 | 52.0 | 728 | 6.8535 |
| 4.2109 | 53.0 | 742 | 6.8533 |
| 4.1504 | 54.0 | 756 | 6.8946 |
| 4.1131 | 55.0 | 770 | 6.8696 |
| 4.1799 | 56.0 | 784 | 6.8739 |
| 4.1055 | 57.0 | 798 | 6.8651 |
| 4.0118 | 58.0 | 812 | 6.8689 |
| 4.176 | 59.0 | 826 | 6.8895 |
| 4.1773 | 60.0 | 840 | 6.8385 |
| 3.9761 | 61.0 | 854 | 6.8580 |
| 4.1002 | 62.0 | 868 | 6.8556 |
| 3.9164 | 63.0 | 882 | 6.9133 |
| 4.0154 | 64.0 | 896 | 6.8968 |
| 4.008 | 65.0 | 910 | 6.8973 |
| 4.0876 | 66.0 | 924 | 6.8768 |
| 3.9527 | 67.0 | 938 | 6.9413 |
| 3.9351 | 68.0 | 952 | 6.9106 |
| 4.0333 | 69.0 | 966 | 6.9012 |
| 3.896 | 70.0 | 980 | 6.9127 |
| 4.0042 | 71.0 | 994 | 6.9211 |
| 3.9151 | 72.0 | 1008 | 6.9585 |
| 3.9852 | 73.0 | 1022 | 6.9027 |
| 3.9913 | 74.0 | 1036 | 6.9333 |
| 3.8787 | 75.0 | 1050 | 6.9345 |
| 3.9729 | 76.0 | 1064 | 6.9165 |
| 3.9449 | 77.0 | 1078 | 6.9660 |
| 3.9416 | 78.0 | 1092 | 6.9462 |
| 3.9497 | 79.0 | 1106 | 6.9617 |
| 3.8797 | 80.0 | 1120 | 6.9356 |
| 3.8509 | 81.0 | 1134 | 6.9562 |
| 3.9021 | 82.0 | 1148 | 6.9400 |
| 3.8218 | 83.0 | 1162 | 7.0029 |
| 3.9301 | 84.0 | 1176 | 7.0166 |
| 3.7591 | 85.0 | 1190 | 6.9891 |
| 3.8889 | 86.0 | 1204 | 6.9830 |
| 3.8063 | 87.0 | 1218 | 6.9812 |
| 3.8556 | 88.0 | 1232 | 7.0051 |
| 3.7773 | 89.0 | 1246 | 6.9959 |
| 3.745 | 90.0 | 1260 | 7.0423 |
| 3.8139 | 91.0 | 1274 | 7.0781 |
| 3.7354 | 92.0 | 1288 | 7.0206 |
| 3.7696 | 93.0 | 1302 | 6.9731 |
| 3.73 | 94.0 | 1316 | 7.0572 |
| 3.7019 | 95.0 | 1330 | 7.0119 |
| 3.6302 | 96.0 | 1344 | 7.0238 |
| 3.675 | 97.0 | 1358 | 7.0348 |
| 3.746 | 98.0 | 1372 | 7.0385 |
| 3.7106 | 99.0 | 1386 | 7.0477 |
| 3.6545 | 100.0 | 1400 | 7.0762 |
| 3.7246 | 101.0 | 1414 | 7.0063 |
| 3.6707 | 102.0 | 1428 | 7.0343 |
| 3.7569 | 103.0 | 1442 | 7.0196 |
| 3.6785 | 104.0 | 1456 | 7.0255 |
| 3.7535 | 105.0 | 1470 | 7.0461 |
| 3.7011 | 106.0 | 1484 | 7.0381 |
| 3.6112 | 107.0 | 1498 | 7.0440 |
| 3.5981 | 108.0 | 1512 | 7.0831 |
| 3.6751 | 109.0 | 1526 | 7.0568 |
| 3.674 | 110.0 | 1540 | 7.0905 |
| 3.5554 | 111.0 | 1554 | 7.0955 |
| 3.5834 | 112.0 | 1568 | 7.0668 |
| 3.581 | 113.0 | 1582 | 7.0943 |
| 3.5548 | 114.0 | 1596 | 7.0490 |
| 3.6446 | 115.0 | 1610 | 7.1190 |
| 3.6142 | 116.0 | 1624 | 7.1366 |
| 3.5748 | 117.0 | 1638 | 7.1047 |
| 3.5447 | 118.0 | 1652 | 7.1332 |
| 3.6093 | 119.0 | 1666 | 7.1137 |
| 3.5694 | 120.0 | 1680 | 7.1010 |
| 3.4729 | 121.0 | 1694 | 7.1186 |
| 3.5373 | 122.0 | 1708 | 7.1258 |
| 3.6168 | 123.0 | 1722 | 7.1184 |
| 3.5654 | 124.0 | 1736 | 7.0982 |
| 3.4747 | 125.0 | 1750 | 7.0908 |
| 3.5098 | 126.0 | 1764 | 7.1134 |
| 3.6144 | 127.0 | 1778 | 7.1033 |
| 3.4529 | 128.0 | 1792 | 7.1376 |
| 3.4286 | 129.0 | 1806 | 7.1056 |
| 3.5095 | 130.0 | 1820 | 7.1148 |
| 3.4333 | 131.0 | 1834 | 7.1185 |
| 3.4879 | 132.0 | 1848 | 7.1704 |
| 3.4992 | 133.0 | 1862 | 7.1361 |
| 3.4738 | 134.0 | 1876 | 7.1597 |
| 3.4358 | 135.0 | 1890 | 7.1244 |
| 3.4648 | 136.0 | 1904 | 7.1762 |
| 3.3206 | 137.0 | 1918 | 7.1274 |
| 3.3579 | 138.0 | 1932 | 7.1208 |
| 3.3573 | 139.0 | 1946 | 7.1614 |
| 3.3504 | 140.0 | 1960 | 7.1241 |
| 3.4585 | 141.0 | 1974 | 7.1421 |
| 3.3757 | 142.0 | 1988 | 7.2070 |
| 3.3911 | 143.0 | 2002 | 7.1862 |
| 3.2857 | 144.0 | 2016 | 7.1925 |
| 3.3898 | 145.0 | 2030 | 7.2269 |
| 3.3297 | 146.0 | 2044 | 7.2391 |
| 3.3361 | 147.0 | 2058 | 7.1945 |
| 3.2097 | 148.0 | 2072 | 7.2095 |
| 3.4288 | 149.0 | 2086 | 7.1376 |
| 3.4156 | 150.0 | 2100 | 7.1984 |
| 3.187 | 151.0 | 2114 | 7.2243 |
| 3.3137 | 152.0 | 2128 | 7.2164 |
| 3.2102 | 153.0 | 2142 | 7.1742 |
| 3.2992 | 154.0 | 2156 | 7.2086 |
| 3.3271 | 155.0 | 2170 | 7.2281 |
| 3.3251 | 156.0 | 2184 | 7.2360 |
| 3.2512 | 157.0 | 2198 | 7.2302 |
| 3.2914 | 158.0 | 2212 | 7.2106 |
| 3.1992 | 159.0 | 2226 | 7.2322 |
| 3.3029 | 160.0 | 2240 | 7.2548 |
| 3.2309 | 161.0 | 2254 | 7.2132 |
| 3.1554 | 162.0 | 2268 | 7.2323 |
| 3.2555 | 163.0 | 2282 | 7.2133 |
| 3.3232 | 164.0 | 2296 | 7.2382 |
| 3.1486 | 165.0 | 2310 | 7.2681 |
| 3.2693 | 166.0 | 2324 | 7.2976 |
| 3.2643 | 167.0 | 2338 | 7.2274 |
| 3.2331 | 168.0 | 2352 | 7.3139 |
| 3.1568 | 169.0 | 2366 | 7.2261 |
| 3.1509 | 170.0 | 2380 | 7.3050 |
| 3.0284 | 171.0 | 2394 | 7.2823 |
| 3.2062 | 172.0 | 2408 | 7.2707 |
| 3.1281 | 173.0 | 2422 | 7.2902 |
| 3.1225 | 174.0 | 2436 | 7.2687 |
| 3.1591 | 175.0 | 2450 | 7.2865 |
| 3.1179 | 176.0 | 2464 | 7.2920 |
| 3.1785 | 177.0 | 2478 | 7.2559 |
| 3.2278 | 178.0 | 2492 | 7.2736 |
| 3.132 | 179.0 | 2506 | 7.3053 |
| 3.0466 | 180.0 | 2520 | 7.2746 |
| 3.1254 | 181.0 | 2534 | 7.2709 |
| 3.1826 | 182.0 | 2548 | 7.3136 |
| 3.1385 | 183.0 | 2562 | 7.3178 |
| 3.1387 | 184.0 | 2576 | 7.2538 |
| 3.0793 | 185.0 | 2590 | 7.2920 |
| 3.112 | 186.0 | 2604 | 7.3260 |
| 3.1013 | 187.0 | 2618 | 7.2720 |
| 3.1897 | 188.0 | 2632 | 7.2739 |
| 3.0557 | 189.0 | 2646 | 7.3047 |
| 3.1642 | 190.0 | 2660 | 7.3403 |
| 2.9943 | 191.0 | 2674 | 7.3406 |
| 3.0325 | 192.0 | 2688 | 7.2799 |
| 3.076 | 193.0 | 2702 | 7.2900 |
| 3.003 | 194.0 | 2716 | 7.3443 |
| 3.0765 | 195.0 | 2730 | 7.3862 |
| 2.9823 | 196.0 | 2744 | 7.3070 |
| 3.0833 | 197.0 | 2758 | 7.2606 |
| 3.0209 | 198.0 | 2772 | 7.3284 |
| 2.9679 | 199.0 | 2786 | 7.3877 |
| 3.0575 | 200.0 | 2800 | 7.3454 |
| 2.9928 | 201.0 | 2814 | 7.3847 |
| 3.092 | 202.0 | 2828 | 7.3738 |
| 2.976 | 203.0 | 2842 | 7.3941 |
| 3.0173 | 204.0 | 2856 | 7.3801 |
| 2.9659 | 205.0 | 2870 | 7.3725 |
| 3.0016 | 206.0 | 2884 | 7.3803 |
| 2.9815 | 207.0 | 2898 | 7.3499 |
| 3.0251 | 208.0 | 2912 | 7.3261 |
| 2.927 | 209.0 | 2926 | 7.3570 |
| 3.0193 | 210.0 | 2940 | 7.3972 |
| 3.0152 | 211.0 | 2954 | 7.3770 |
| 2.9104 | 212.0 | 2968 | 7.3326 |
| 2.9868 | 213.0 | 2982 | 7.3898 |
| 3.0097 | 214.0 | 2996 | 7.3658 |
| 3.0093 | 215.0 | 3010 | 7.3975 |
| 2.8546 | 216.0 | 3024 | 7.3948 |
| 2.8972 | 217.0 | 3038 | 7.3734 |
| 2.9641 | 218.0 | 3052 | 7.4320 |
| 2.9083 | 219.0 | 3066 | 7.3582 |
| 2.9185 | 220.0 | 3080 | 7.4126 |
| 3.0003 | 221.0 | 3094 | 7.3918 |
| 2.8599 | 222.0 | 3108 | 7.4171 |
| 2.8931 | 223.0 | 3122 | 7.4251 |
| 2.9109 | 224.0 | 3136 | 7.4426 |
| 2.9417 | 225.0 | 3150 | 7.4428 |
| 2.8274 | 226.0 | 3164 | 7.4145 |
| 2.921 | 227.0 | 3178 | 7.3492 |
| 2.7542 | 228.0 | 3192 | 7.4100 |
| 2.8775 | 229.0 | 3206 | 7.4288 |
| 2.7467 | 230.0 | 3220 | 7.4359 |
| 2.8301 | 231.0 | 3234 | 7.4715 |
| 2.7856 | 232.0 | 3248 | 7.4036 |
| 2.835 | 233.0 | 3262 | 7.4038 |
| 2.7665 | 234.0 | 3276 | 7.4919 |
| 2.8972 | 235.0 | 3290 | 7.4808 |
| 2.8768 | 236.0 | 3304 | 7.5259 |
| 2.9377 | 237.0 | 3318 | 7.4187 |
| 2.8489 | 238.0 | 3332 | 7.4590 |
| 2.8018 | 239.0 | 3346 | 7.4565 |
| 2.771 | 240.0 | 3360 | 7.4474 |
| 2.7378 | 241.0 | 3374 | 7.5119 |
| 2.822 | 242.0 | 3388 | 7.4734 |
| 2.8274 | 243.0 | 3402 | 7.4984 |
| 2.7732 | 244.0 | 3416 | 7.4829 |
| 2.7264 | 245.0 | 3430 | 7.4391 |
| 2.7764 | 246.0 | 3444 | 7.4456 |
| 2.7972 | 247.0 | 3458 | 7.4858 |
| 2.8231 | 248.0 | 3472 | 7.4760 |
| 2.778 | 249.0 | 3486 | 7.4380 |
| 2.7935 | 250.0 | 3500 | 7.4336 |
| 2.7348 | 251.0 | 3514 | 7.4970 |
| 2.7192 | 252.0 | 3528 | 7.4811 |
| 2.8108 | 253.0 | 3542 | 7.4547 |
| 2.837 | 254.0 | 3556 | 7.4830 |
| 2.6868 | 255.0 | 3570 | 7.5151 |
| 2.7789 | 256.0 | 3584 | 7.5115 |
| 2.7706 | 257.0 | 3598 | 7.5180 |
| 2.7904 | 258.0 | 3612 | 7.5158 |
| 2.7215 | 259.0 | 3626 | 7.5262 |
| 2.6876 | 260.0 | 3640 | 7.5114 |
| 2.7679 | 261.0 | 3654 | 7.5066 |
| 2.7742 | 262.0 | 3668 | 7.5035 |
| 2.6965 | 263.0 | 3682 | 7.4918 |
| 2.668 | 264.0 | 3696 | 7.5305 |
| 2.6808 | 265.0 | 3710 | 7.5238 |
| 2.6491 | 266.0 | 3724 | 7.5347 |
| 2.7307 | 267.0 | 3738 | 7.5175 |
| 2.6518 | 268.0 | 3752 | 7.5635 |
| 2.6685 | 269.0 | 3766 | 7.4899 |
| 2.671 | 270.0 | 3780 | 7.4855 |
| 2.596 | 271.0 | 3794 | 7.5518 |
| 2.6622 | 272.0 | 3808 | 7.5308 |
| 2.6684 | 273.0 | 3822 | 7.5955 |
| 2.6325 | 274.0 | 3836 | 7.5768 |
| 2.6334 | 275.0 | 3850 | 7.5202 |
| 2.6042 | 276.0 | 3864 | 7.6176 |
| 2.7439 | 277.0 | 3878 | 7.5369 |
| 2.6925 | 278.0 | 3892 | 7.5422 |
| 2.7106 | 279.0 | 3906 | 7.5629 |
| 2.6519 | 280.0 | 3920 | 7.5359 |
| 2.6044 | 281.0 | 3934 | 7.5619 |
| 2.6509 | 282.0 | 3948 | 7.5433 |
| 2.6777 | 283.0 | 3962 | 7.5573 |
| 2.6199 | 284.0 | 3976 | 7.5628 |
| 2.6685 | 285.0 | 3990 | 7.5710 |
| 2.6608 | 286.0 | 4004 | 7.6020 |
| 2.6579 | 287.0 | 4018 | 7.5780 |
| 2.5559 | 288.0 | 4032 | 7.5713 |
| 2.5091 | 289.0 | 4046 | 7.5912 |
| 2.6141 | 290.0 | 4060 | 7.6475 |
| 2.6832 | 291.0 | 4074 | 7.5865 |
| 2.5769 | 292.0 | 4088 | 7.6198 |
| 2.6432 | 293.0 | 4102 | 7.6058 |
| 2.5733 | 294.0 | 4116 | 7.5853 |
| 2.5782 | 295.0 | 4130 | 7.6246 |
| 2.6118 | 296.0 | 4144 | 7.5817 |
| 2.6894 | 297.0 | 4158 | 7.5868 |
| 2.5624 | 298.0 | 4172 | 7.5837 |
| 2.5449 | 299.0 | 4186 | 7.6007 |
| 2.5865 | 300.0 | 4200 | 7.6604 |
| 2.5366 | 301.0 | 4214 | 7.5909 |
| 2.4286 | 302.0 | 4228 | 7.6563 |
| 2.6909 | 303.0 | 4242 | 7.6767 |
| 2.4252 | 304.0 | 4256 | 7.6556 |
| 2.612 | 305.0 | 4270 | 7.6846 |
| 2.4793 | 306.0 | 4284 | 7.6279 |
| 2.5227 | 307.0 | 4298 | 7.6808 |
| 2.5756 | 308.0 | 4312 | 7.6703 |
| 2.5321 | 309.0 | 4326 | 7.6217 |
| 2.5568 | 310.0 | 4340 | 7.6381 |
| 2.507 | 311.0 | 4354 | 7.6528 |
| 2.5766 | 312.0 | 4368 | 7.6480 |
| 2.3532 | 313.0 | 4382 | 7.6255 |
| 2.4758 | 314.0 | 4396 | 7.6512 |
| 2.4304 | 315.0 | 4410 | 7.6606 |
| 2.4749 | 316.0 | 4424 | 7.6470 |
| 2.4886 | 317.0 | 4438 | 7.7193 |
| 2.511 | 318.0 | 4452 | 7.6670 |
| 2.4664 | 319.0 | 4466 | 7.6209 |
| 2.4981 | 320.0 | 4480 | 7.6819 |
| 2.4406 | 321.0 | 4494 | 7.6661 |
| 2.5787 | 322.0 | 4508 | 7.6903 |
| 2.4885 | 323.0 | 4522 | 7.6595 |
| 2.5796 | 324.0 | 4536 | 7.6882 |
| 2.4909 | 325.0 | 4550 | 7.7169 |
| 2.522 | 326.0 | 4564 | 7.6606 |
| 2.4206 | 327.0 | 4578 | 7.6526 |
| 2.4909 | 328.0 | 4592 | 7.6731 |
| 2.4543 | 329.0 | 4606 | 7.6822 |
| 2.4431 | 330.0 | 4620 | 7.6770 |
| 2.3963 | 331.0 | 4634 | 7.6407 |
| 2.4518 | 332.0 | 4648 | 7.6468 |
| 2.5734 | 333.0 | 4662 | 7.7206 |
| 2.4423 | 334.0 | 4676 | 7.6691 |
| 2.4418 | 335.0 | 4690 | 7.6822 |
| 2.4575 | 336.0 | 4704 | 7.6477 |
| 2.4671 | 337.0 | 4718 | 7.6888 |
| 2.3527 | 338.0 | 4732 | 7.7104 |
| 2.473 | 339.0 | 4746 | 7.7247 |
| 2.4786 | 340.0 | 4760 | 7.7340 |
| 2.4222 | 341.0 | 4774 | 7.6998 |
| 2.4812 | 342.0 | 4788 | 7.6996 |
| 2.3484 | 343.0 | 4802 | 7.6807 |
| 2.3231 | 344.0 | 4816 | 7.6972 |
| 2.4844 | 345.0 | 4830 | 7.6984 |
| 2.3757 | 346.0 | 4844 | 7.7091 |
| 2.4139 | 347.0 | 4858 | 7.7240 |
| 2.3665 | 348.0 | 4872 | 7.7681 |
| 2.3942 | 349.0 | 4886 | 7.7129 |
| 2.2922 | 350.0 | 4900 | 7.6804 |
| 2.4234 | 351.0 | 4914 | 7.7445 |
| 2.3589 | 352.0 | 4928 | 7.7599 |
| 2.3987 | 353.0 | 4942 | 7.7381 |
| 2.3545 | 354.0 | 4956 | 7.7433 |
| 2.4019 | 355.0 | 4970 | 7.7560 |
| 2.2925 | 356.0 | 4984 | 7.7393 |
| 2.3678 | 357.0 | 4998 | 7.7211 |
| 2.3588 | 358.0 | 5012 | 7.7414 |
| 2.3996 | 359.0 | 5026 | 7.7436 |
| 2.3665 | 360.0 | 5040 | 7.7966 |
| 2.3374 | 361.0 | 5054 | 7.8225 |
| 2.3667 | 362.0 | 5068 | 7.7955 |
| 2.3754 | 363.0 | 5082 | 7.7731 |
| 2.4179 | 364.0 | 5096 | 7.7899 |
| 2.3777 | 365.0 | 5110 | 7.7831 |
| 2.4064 | 366.0 | 5124 | 7.7861 |
| 2.3371 | 367.0 | 5138 | 7.7578 |
| 2.4169 | 368.0 | 5152 | 7.8429 |
| 2.3491 | 369.0 | 5166 | 7.7645 |
| 2.2275 | 370.0 | 5180 | 7.8063 |
| 2.2605 | 371.0 | 5194 | 7.8280 |
| 2.3506 | 372.0 | 5208 | 7.7356 |
| 2.403 | 373.0 | 5222 | 7.7739 |
| 2.3188 | 374.0 | 5236 | 7.7634 |
| 2.3294 | 375.0 | 5250 | 7.8033 |
| 2.2724 | 376.0 | 5264 | 7.8423 |
| 2.2704 | 377.0 | 5278 | 7.8340 |
| 2.2606 | 378.0 | 5292 | 7.7941 |
| 2.2599 | 379.0 | 5306 | 7.8336 |
| 2.326 | 380.0 | 5320 | 7.8080 |
| 2.2861 | 381.0 | 5334 | 7.8097 |
| 2.2559 | 382.0 | 5348 | 7.8201 |
| 2.2612 | 383.0 | 5362 | 7.8249 |
| 2.3161 | 384.0 | 5376 | 7.8353 |
| 2.2061 | 385.0 | 5390 | 7.8623 |
| 2.4231 | 386.0 | 5404 | 7.8568 |
| 2.2481 | 387.0 | 5418 | 7.8642 |
| 2.2319 | 388.0 | 5432 | 7.8255 |
| 2.2178 | 389.0 | 5446 | 7.8136 |
| 2.3153 | 390.0 | 5460 | 7.8549 |
| 2.2391 | 391.0 | 5474 | 7.8739 |
| 2.2736 | 392.0 | 5488 | 7.9016 |
| 2.3335 | 393.0 | 5502 | 7.8838 |
| 2.2069 | 394.0 | 5516 | 7.9268 |
| 2.303 | 395.0 | 5530 | 7.8096 |
| 2.2585 | 396.0 | 5544 | 7.8667 |
| 2.2261 | 397.0 | 5558 | 7.8651 |
| 2.2792 | 398.0 | 5572 | 7.8213 |
| 2.3172 | 399.0 | 5586 | 7.8702 |
| 2.2787 | 400.0 | 5600 | 7.8745 |
| 2.226 | 401.0 | 5614 | 7.8490 |
| 2.1491 | 402.0 | 5628 | 7.8608 |
| 2.2121 | 403.0 | 5642 | 7.8568 |
| 2.1715 | 404.0 | 5656 | 7.8996 |
| 2.1613 | 405.0 | 5670 | 7.8920 |
| 2.1886 | 406.0 | 5684 | 7.8223 |
| 2.1392 | 407.0 | 5698 | 7.8254 |
| 2.2268 | 408.0 | 5712 | 7.8583 |
| 2.2726 | 409.0 | 5726 | 7.8749 |
| 2.1648 | 410.0 | 5740 | 7.9115 |
| 2.1897 | 411.0 | 5754 | 7.9030 |
| 2.1597 | 412.0 | 5768 | 7.8699 |
| 2.1989 | 413.0 | 5782 | 7.8932 |
| 2.2705 | 414.0 | 5796 | 7.8936 |
| 2.1071 | 415.0 | 5810 | 7.8695 |
| 2.124 | 416.0 | 5824 | 7.8873 |
| 2.1948 | 417.0 | 5838 | 7.8655 |
| 2.2704 | 418.0 | 5852 | 7.9172 |
| 2.2055 | 419.0 | 5866 | 7.9646 |
| 2.276 | 420.0 | 5880 | 7.9224 |
| 2.1541 | 421.0 | 5894 | 7.8567 |
| 2.1881 | 422.0 | 5908 | 7.8945 |
| 2.1455 | 423.0 | 5922 | 7.8674 |
| 2.3452 | 424.0 | 5936 | 7.9724 |
| 2.1371 | 425.0 | 5950 | 7.9671 |
| 2.1901 | 426.0 | 5964 | 7.9274 |
| 2.1643 | 427.0 | 5978 | 7.9121 |
| 2.2229 | 428.0 | 5992 | 7.8934 |
| 2.1254 | 429.0 | 6006 | 7.9270 |
| 2.1554 | 430.0 | 6020 | 7.9205 |
| 2.0761 | 431.0 | 6034 | 7.9256 |
| 2.1551 | 432.0 | 6048 | 7.9133 |
| 2.2451 | 433.0 | 6062 | 7.9323 |
| 2.1491 | 434.0 | 6076 | 7.9551 |
| 2.1766 | 435.0 | 6090 | 7.9279 |
| 2.1239 | 436.0 | 6104 | 7.8983 |
| 2.1505 | 437.0 | 6118 | 7.9169 |
| 2.153 | 438.0 | 6132 | 7.9516 |
| 2.0899 | 439.0 | 6146 | 7.9140 |
| 2.1215 | 440.0 | 6160 | 7.9224 |
| 2.0644 | 441.0 | 6174 | 7.9485 |
| 2.1333 | 442.0 | 6188 | 7.9633 |
| 2.0807 | 443.0 | 6202 | 7.9847 |
| 2.1222 | 444.0 | 6216 | 7.9519 |
| 2.0775 | 445.0 | 6230 | 7.9995 |
| 2.1712 | 446.0 | 6244 | 7.9453 |
| 2.1816 | 447.0 | 6258 | 7.9563 |
| 2.071 | 448.0 | 6272 | 7.9443 |
| 2.083 | 449.0 | 6286 | 7.9434 |
| 2.166 | 450.0 | 6300 | 7.9449 |
| 2.1607 | 451.0 | 6314 | 7.9534 |
| 2.1057 | 452.0 | 6328 | 7.9520 |
| 2.1258 | 453.0 | 6342 | 7.9578 |
| 2.0822 | 454.0 | 6356 | 7.9709 |
| 2.0092 | 455.0 | 6370 | 8.0117 |
| 2.055 | 456.0 | 6384 | 7.9800 |
| 2.0325 | 457.0 | 6398 | 7.9150 |
| 2.0546 | 458.0 | 6412 | 7.9607 |
| 2.0677 | 459.0 | 6426 | 7.9714 |
| 2.1351 | 460.0 | 6440 | 7.9851 |
| 2.0859 | 461.0 | 6454 | 8.0055 |
| 2.0274 | 462.0 | 6468 | 7.9691 |
| 2.0006 | 463.0 | 6482 | 7.9561 |
| 2.1271 | 464.0 | 6496 | 7.9346 |
| 2.0637 | 465.0 | 6510 | 8.0015 |
| 2.0727 | 466.0 | 6524 | 8.0062 |
| 1.983 | 467.0 | 6538 | 8.0255 |
| 2.0895 | 468.0 | 6552 | 7.9777 |
| 2.1187 | 469.0 | 6566 | 7.9704 |
| 2.0874 | 470.0 | 6580 | 7.9550 |
| 2.0927 | 471.0 | 6594 | 7.9987 |
| 2.1442 | 472.0 | 6608 | 8.0421 |
| 2.0117 | 473.0 | 6622 | 8.0121 |
| 2.0647 | 474.0 | 6636 | 7.9565 |
| 2.0095 | 475.0 | 6650 | 7.9986 |
| 2.0008 | 476.0 | 6664 | 8.0411 |
| 2.0464 | 477.0 | 6678 | 7.9803 |
| 2.1314 | 478.0 | 6692 | 8.0383 |
| 2.0345 | 479.0 | 6706 | 7.9776 |
| 2.0668 | 480.0 | 6720 | 8.0702 |
| 2.0933 | 481.0 | 6734 | 8.0149 |
| 2.0612 | 482.0 | 6748 | 8.0105 |
| 1.9858 | 483.0 | 6762 | 7.9859 |
| 2.0195 | 484.0 | 6776 | 7.9764 |
| 2.0203 | 485.0 | 6790 | 8.0284 |
| 1.9986 | 486.0 | 6804 | 7.9929 |
| 2.0372 | 487.0 | 6818 | 7.9623 |
| 1.9485 | 488.0 | 6832 | 8.0172 |
| 1.9316 | 489.0 | 6846 | 7.9999 |
| 2.0008 | 490.0 | 6860 | 7.9783 |
| 2.0899 | 491.0 | 6874 | 8.0318 |
| 2.0078 | 492.0 | 6888 | 7.9986 |
| 2.0386 | 493.0 | 6902 | 8.0367 |
| 2.006 | 494.0 | 6916 | 8.0206 |
| 1.981 | 495.0 | 6930 | 8.0008 |
| 2.0464 | 496.0 | 6944 | 8.0056 |
| 1.9485 | 497.0 | 6958 | 8.0243 |
| 2.0409 | 498.0 | 6972 | 8.0541 |
| 2.0374 | 499.0 | 6986 | 8.0330 |
| 2.0073 | 500.0 | 7000 | 8.0471 |
| 2.0605 | 501.0 | 7014 | 8.0782 |
| 2.0424 | 502.0 | 7028 | 8.0568 |
| 2.0192 | 503.0 | 7042 | 8.0502 |
| 1.9831 | 504.0 | 7056 | 8.0580 |
| 2.1012 | 505.0 | 7070 | 8.0488 |
| 1.9765 | 506.0 | 7084 | 8.0293 |
| 2.0735 | 507.0 | 7098 | 8.0325 |
| 1.9964 | 508.0 | 7112 | 8.0104 |
| 1.9963 | 509.0 | 7126 | 8.0233 |
| 2.0252 | 510.0 | 7140 | 8.0312 |
| 1.9223 | 511.0 | 7154 | 8.0337 |
| 2.0063 | 512.0 | 7168 | 8.0609 |
| 2.0272 | 513.0 | 7182 | 8.0299 |
| 1.9498 | 514.0 | 7196 | 8.0298 |
| 2.0057 | 515.0 | 7210 | 8.0949 |
| 1.9598 | 516.0 | 7224 | 8.0999 |
| 1.949 | 517.0 | 7238 | 8.0914 |
| 2.0215 | 518.0 | 7252 | 8.0730 |
| 2.0068 | 519.0 | 7266 | 8.0657 |
| 1.9337 | 520.0 | 7280 | 8.0926 |
| 2.0259 | 521.0 | 7294 | 8.0900 |
| 1.9699 | 522.0 | 7308 | 8.0874 |
| 1.9511 | 523.0 | 7322 | 8.1374 |
| 1.8801 | 524.0 | 7336 | 8.0852 |
| 2.0123 | 525.0 | 7350 | 8.0754 |
| 1.9374 | 526.0 | 7364 | 8.0685 |
| 1.9303 | 527.0 | 7378 | 8.0832 |
| 1.963 | 528.0 | 7392 | 8.0756 |
| 1.9235 | 529.0 | 7406 | 8.1209 |
| 1.9476 | 530.0 | 7420 | 8.1116 |
| 1.8567 | 531.0 | 7434 | 8.0481 |
| 1.95 | 532.0 | 7448 | 8.0793 |
| 1.9672 | 533.0 | 7462 | 8.0927 |
| 1.8384 | 534.0 | 7476 | 8.1191 |
| 1.9117 | 535.0 | 7490 | 8.0865 |
| 2.0308 | 536.0 | 7504 | 8.0930 |
| 1.9107 | 537.0 | 7518 | 8.0952 |
| 1.9407 | 538.0 | 7532 | 8.1373 |
| 1.9409 | 539.0 | 7546 | 8.1064 |
| 1.9787 | 540.0 | 7560 | 8.1079 |
| 1.8791 | 541.0 | 7574 | 8.0920 |
| 1.9495 | 542.0 | 7588 | 8.0910 |
| 1.9265 | 543.0 | 7602 | 8.1203 |
| 1.8949 | 544.0 | 7616 | 8.1223 |
| 1.8861 | 545.0 | 7630 | 8.1458 |
| 1.9369 | 546.0 | 7644 | 8.0948 |
| 1.9234 | 547.0 | 7658 | 8.1073 |
| 1.934 | 548.0 | 7672 | 8.1285 |
| 1.947 | 549.0 | 7686 | 8.1476 |
| 1.9623 | 550.0 | 7700 | 8.1491 |
| 1.8069 | 551.0 | 7714 | 8.1058 |
| 1.9387 | 552.0 | 7728 | 8.1616 |
| 1.9291 | 553.0 | 7742 | 8.1207 |
| 1.9894 | 554.0 | 7756 | 8.1887 |
| 1.885 | 555.0 | 7770 | 8.1785 |
| 1.9515 | 556.0 | 7784 | 8.1555 |
| 1.9123 | 557.0 | 7798 | 8.1708 |
| 1.922 | 558.0 | 7812 | 8.1977 |
| 1.8818 | 559.0 | 7826 | 8.1429 |
| 1.9557 | 560.0 | 7840 | 8.1483 |
| 1.9005 | 561.0 | 7854 | 8.1108 |
| 1.91 | 562.0 | 7868 | 8.1745 |
| 1.8598 | 563.0 | 7882 | 8.1938 |
| 1.9633 | 564.0 | 7896 | 8.1294 |
| 1.8658 | 565.0 | 7910 | 8.1407 |
| 1.9256 | 566.0 | 7924 | 8.1767 |
| 1.8974 | 567.0 | 7938 | 8.1441 |
| 1.9635 | 568.0 | 7952 | 8.1219 |
| 1.9537 | 569.0 | 7966 | 8.2357 |
| 1.8828 | 570.0 | 7980 | 8.1944 |
| 1.8594 | 571.0 | 7994 | 8.1265 |
| 1.9105 | 572.0 | 8008 | 8.1458 |
| 1.9491 | 573.0 | 8022 | 8.2029 |
| 1.858 | 574.0 | 8036 | 8.1726 |
| 1.8092 | 575.0 | 8050 | 8.1803 |
| 1.9622 | 576.0 | 8064 | 8.1810 |
| 1.8717 | 577.0 | 8078 | 8.1521 |
| 1.9348 | 578.0 | 8092 | 8.1459 |
| 1.8003 | 579.0 | 8106 | 8.1740 |
| 1.9229 | 580.0 | 8120 | 8.1872 |
| 1.8093 | 581.0 | 8134 | 8.2038 |
| 1.9837 | 582.0 | 8148 | 8.1909 |
| 1.8906 | 583.0 | 8162 | 8.1823 |
| 1.8431 | 584.0 | 8176 | 8.1623 |
| 1.8505 | 585.0 | 8190 | 8.1838 |
| 1.8382 | 586.0 | 8204 | 8.1491 |
| 1.8919 | 587.0 | 8218 | 8.1562 |
| 1.8959 | 588.0 | 8232 | 8.1811 |
| 1.8002 | 589.0 | 8246 | 8.1789 |
| 1.8076 | 590.0 | 8260 | 8.2051 |
| 1.9212 | 591.0 | 8274 | 8.2004 |
| 1.8934 | 592.0 | 8288 | 8.2180 |
| 1.8699 | 593.0 | 8302 | 8.1870 |
| 1.8572 | 594.0 | 8316 | 8.1486 |
| 1.7875 | 595.0 | 8330 | 8.2182 |
| 1.8563 | 596.0 | 8344 | 8.1820 |
| 1.8471 | 597.0 | 8358 | 8.1865 |
| 1.8371 | 598.0 | 8372 | 8.1725 |
| 1.8167 | 599.0 | 8386 | 8.1666 |
| 1.8224 | 600.0 | 8400 | 8.2022 |
| 1.866 | 601.0 | 8414 | 8.2027 |
| 1.874 | 602.0 | 8428 | 8.2203 |
| 1.8575 | 603.0 | 8442 | 8.2109 |
| 1.898 | 604.0 | 8456 | 8.2598 |
| 1.8262 | 605.0 | 8470 | 8.2069 |
| 1.8849 | 606.0 | 8484 | 8.2478 |
| 1.8462 | 607.0 | 8498 | 8.1577 |
| 1.8755 | 608.0 | 8512 | 8.1992 |
| 1.7482 | 609.0 | 8526 | 8.1532 |
| 1.8244 | 610.0 | 8540 | 8.1482 |
| 1.9027 | 611.0 | 8554 | 8.2278 |
| 1.8474 | 612.0 | 8568 | 8.1938 |
| 1.8069 | 613.0 | 8582 | 8.1854 |
| 1.8422 | 614.0 | 8596 | 8.1945 |
| 1.8573 | 615.0 | 8610 | 8.2083 |
| 1.8114 | 616.0 | 8624 | 8.1864 |
| 1.873 | 617.0 | 8638 | 8.1902 |
| 1.7979 | 618.0 | 8652 | 8.2742 |
| 1.89 | 619.0 | 8666 | 8.2423 |
| 1.7861 | 620.0 | 8680 | 8.1962 |
| 1.7954 | 621.0 | 8694 | 8.2001 |
| 1.8347 | 622.0 | 8708 | 8.2490 |
| 1.8261 | 623.0 | 8722 | 8.2301 |
| 1.897 | 624.0 | 8736 | 8.2063 |
| 1.8098 | 625.0 | 8750 | 8.2428 |
| 1.7802 | 626.0 | 8764 | 8.1885 |
| 1.7771 | 627.0 | 8778 | 8.2032 |
| 1.7692 | 628.0 | 8792 | 8.2384 |
| 1.811 | 629.0 | 8806 | 8.2471 |
| 1.8642 | 630.0 | 8820 | 8.2354 |
| 1.7502 | 631.0 | 8834 | 8.2632 |
| 1.7271 | 632.0 | 8848 | 8.2109 |
| 1.8253 | 633.0 | 8862 | 8.2887 |
| 1.8223 | 634.0 | 8876 | 8.2433 |
| 1.7773 | 635.0 | 8890 | 8.2796 |
| 1.8149 | 636.0 | 8904 | 8.2290 |
| 1.8752 | 637.0 | 8918 | 8.2510 |
| 1.7794 | 638.0 | 8932 | 8.2484 |
| 1.7915 | 639.0 | 8946 | 8.2695 |
| 1.8358 | 640.0 | 8960 | 8.2513 |
| 1.7247 | 641.0 | 8974 | 8.2551 |
| 1.7591 | 642.0 | 8988 | 8.2442 |
| 1.8144 | 643.0 | 9002 | 8.2554 |
| 1.8404 | 644.0 | 9016 | 8.2240 |
| 1.7369 | 645.0 | 9030 | 8.2431 |
| 1.843 | 646.0 | 9044 | 8.2290 |
| 1.7177 | 647.0 | 9058 | 8.2729 |
| 1.8483 | 648.0 | 9072 | 8.2534 |
| 1.7889 | 649.0 | 9086 | 8.2305 |
| 1.8154 | 650.0 | 9100 | 8.2466 |
| 1.8509 | 651.0 | 9114 | 8.2273 |
| 1.7917 | 652.0 | 9128 | 8.2738 |
| 1.8168 | 653.0 | 9142 | 8.2925 |
| 1.7778 | 654.0 | 9156 | 8.2309 |
| 1.7742 | 655.0 | 9170 | 8.3030 |
| 1.8136 | 656.0 | 9184 | 8.3185 |
| 1.8294 | 657.0 | 9198 | 8.2920 |
| 1.8047 | 658.0 | 9212 | 8.3148 |
| 1.7153 | 659.0 | 9226 | 8.2667 |
| 1.7697 | 660.0 | 9240 | 8.2409 |
| 1.7956 | 661.0 | 9254 | 8.2493 |
| 1.8108 | 662.0 | 9268 | 8.2775 |
| 1.803 | 663.0 | 9282 | 8.2568 |
| 1.7745 | 664.0 | 9296 | 8.3051 |
| 1.71 | 665.0 | 9310 | 8.2639 |
| 1.8429 | 666.0 | 9324 | 8.2843 |
| 1.8034 | 667.0 | 9338 | 8.2691 |
| 1.7898 | 668.0 | 9352 | 8.2818 |
| 1.7216 | 669.0 | 9366 | 8.2681 |
| 1.7471 | 670.0 | 9380 | 8.2749 |
| 1.7109 | 671.0 | 9394 | 8.2923 |
| 1.7778 | 672.0 | 9408 | 8.2664 |
| 1.8218 | 673.0 | 9422 | 8.2983 |
| 1.7237 | 674.0 | 9436 | 8.2641 |
| 1.8237 | 675.0 | 9450 | 8.3240 |
| 1.7559 | 676.0 | 9464 | 8.3063 |
| 1.7773 | 677.0 | 9478 | 8.3280 |
| 1.7547 | 678.0 | 9492 | 8.2927 |
| 1.6821 | 679.0 | 9506 | 8.2964 |
| 1.8102 | 680.0 | 9520 | 8.2998 |
| 1.8004 | 681.0 | 9534 | 8.3655 |
| 1.7746 | 682.0 | 9548 | 8.3189 |
| 1.8222 | 683.0 | 9562 | 8.2932 |
| 1.8087 | 684.0 | 9576 | 8.3033 |
| 1.715 | 685.0 | 9590 | 8.3314 |
| 1.7371 | 686.0 | 9604 | 8.3448 |
| 1.759 | 687.0 | 9618 | 8.3417 |
| 1.7048 | 688.0 | 9632 | 8.3500 |
| 1.7708 | 689.0 | 9646 | 8.3246 |
| 1.6673 | 690.0 | 9660 | 8.2900 |
| 1.6932 | 691.0 | 9674 | 8.3128 |
| 1.7716 | 692.0 | 9688 | 8.3368 |
| 1.7829 | 693.0 | 9702 | 8.3166 |
| 1.7432 | 694.0 | 9716 | 8.3375 |
| 1.7885 | 695.0 | 9730 | 8.3004 |
| 1.6967 | 696.0 | 9744 | 8.3142 |
| 1.7928 | 697.0 | 9758 | 8.3387 |
| 1.7313 | 698.0 | 9772 | 8.3486 |
| 1.7433 | 699.0 | 9786 | 8.3254 |
| 1.7374 | 700.0 | 9800 | 8.3218 |
| 1.7113 | 701.0 | 9814 | 8.3249 |
| 1.824 | 702.0 | 9828 | 8.3451 |
| 1.7261 | 703.0 | 9842 | 8.3510 |
| 1.7163 | 704.0 | 9856 | 8.2824 |
| 1.7039 | 705.0 | 9870 | 8.3193 |
| 1.8078 | 706.0 | 9884 | 8.3046 |
| 1.6971 | 707.0 | 9898 | 8.3212 |
| 1.7463 | 708.0 | 9912 | 8.3486 |
| 1.8218 | 709.0 | 9926 | 8.3098 |
| 1.6471 | 710.0 | 9940 | 8.3217 |
| 1.7754 | 711.0 | 9954 | 8.3398 |
| 1.7055 | 712.0 | 9968 | 8.3509 |
| 1.766 | 713.0 | 9982 | 8.3146 |
| 1.7345 | 714.0 | 9996 | 8.3196 |
| 1.6768 | 715.0 | 10010 | 8.3553 |
| 1.7612 | 716.0 | 10024 | 8.3631 |
| 1.7521 | 717.0 | 10038 | 8.3646 |
| 1.6671 | 718.0 | 10052 | 8.3086 |
| 1.7135 | 719.0 | 10066 | 8.2903 |
| 1.7517 | 720.0 | 10080 | 8.3451 |
| 1.717 | 721.0 | 10094 | 8.3280 |
| 1.68 | 722.0 | 10108 | 8.3040 |
| 1.6721 | 723.0 | 10122 | 8.3266 |
| 1.754 | 724.0 | 10136 | 8.3336 |
| 1.7325 | 725.0 | 10150 | 8.3339 |
| 1.7358 | 726.0 | 10164 | 8.3686 |
| 1.7289 | 727.0 | 10178 | 8.3741 |
| 1.7527 | 728.0 | 10192 | 8.3647 |
| 1.76 | 729.0 | 10206 | 8.3406 |
| 1.6752 | 730.0 | 10220 | 8.3416 |
| 1.7191 | 731.0 | 10234 | 8.3513 |
| 1.6671 | 732.0 | 10248 | 8.3225 |
| 1.6836 | 733.0 | 10262 | 8.3430 |
| 1.6908 | 734.0 | 10276 | 8.3472 |
| 1.7494 | 735.0 | 10290 | 8.3647 |
| 1.6724 | 736.0 | 10304 | 8.3764 |
| 1.6974 | 737.0 | 10318 | 8.3277 |
| 1.7013 | 738.0 | 10332 | 8.3778 |
| 1.6729 | 739.0 | 10346 | 8.3322 |
| 1.8026 | 740.0 | 10360 | 8.3381 |
| 1.6888 | 741.0 | 10374 | 8.3736 |
| 1.6897 | 742.0 | 10388 | 8.3559 |
| 1.7499 | 743.0 | 10402 | 8.3814 |
| 1.7797 | 744.0 | 10416 | 8.3995 |
| 1.721 | 745.0 | 10430 | 8.3645 |
| 1.6995 | 746.0 | 10444 | 8.3575 |
| 1.7551 | 747.0 | 10458 | 8.4008 |
| 1.7368 | 748.0 | 10472 | 8.3599 |
| 1.7195 | 749.0 | 10486 | 8.3410 |
| 1.7507 | 750.0 | 10500 | 8.3658 |
| 1.7005 | 751.0 | 10514 | 8.4275 |
| 1.6815 | 752.0 | 10528 | 8.3222 |
| 1.6997 | 753.0 | 10542 | 8.3840 |
| 1.6468 | 754.0 | 10556 | 8.3706 |
| 1.6624 | 755.0 | 10570 | 8.3680 |
| 1.5879 | 756.0 | 10584 | 8.3976 |
| 1.6258 | 757.0 | 10598 | 8.3567 |
| 1.7074 | 758.0 | 10612 | 8.3864 |
| 1.7027 | 759.0 | 10626 | 8.3520 |
| 1.6504 | 760.0 | 10640 | 8.3882 |
| 1.6845 | 761.0 | 10654 | 8.3807 |
| 1.7722 | 762.0 | 10668 | 8.3434 |
| 1.6156 | 763.0 | 10682 | 8.3492 |
| 1.6982 | 764.0 | 10696 | 8.3609 |
| 1.5971 | 765.0 | 10710 | 8.3505 |
| 1.6788 | 766.0 | 10724 | 8.3395 |
| 1.6914 | 767.0 | 10738 | 8.3510 |
| 1.6657 | 768.0 | 10752 | 8.3490 |
| 1.7517 | 769.0 | 10766 | 8.4295 |
| 1.7012 | 770.0 | 10780 | 8.4127 |
| 1.7077 | 771.0 | 10794 | 8.3815 |
| 1.6822 | 772.0 | 10808 | 8.3643 |
| 1.6829 | 773.0 | 10822 | 8.3655 |
| 1.6339 | 774.0 | 10836 | 8.3908 |
| 1.7569 | 775.0 | 10850 | 8.4011 |
| 1.6163 | 776.0 | 10864 | 8.3726 |
| 1.6662 | 777.0 | 10878 | 8.3592 |
| 1.7537 | 778.0 | 10892 | 8.3498 |
| 1.7627 | 779.0 | 10906 | 8.3889 |
| 1.6896 | 780.0 | 10920 | 8.3925 |
| 1.6477 | 781.0 | 10934 | 8.4438 |
| 1.7155 | 782.0 | 10948 | 8.3910 |
| 1.6333 | 783.0 | 10962 | 8.4093 |
| 1.6535 | 784.0 | 10976 | 8.3662 |
| 1.6402 | 785.0 | 10990 | 8.3895 |
| 1.6792 | 786.0 | 11004 | 8.3827 |
| 1.7202 | 787.0 | 11018 | 8.4082 |
| 1.6361 | 788.0 | 11032 | 8.3915 |
| 1.6595 | 789.0 | 11046 | 8.4216 |
| 1.769 | 790.0 | 11060 | 8.4089 |
| 1.6114 | 791.0 | 11074 | 8.4081 |
| 1.5996 | 792.0 | 11088 | 8.4095 |
| 1.7636 | 793.0 | 11102 | 8.3521 |
| 1.784 | 794.0 | 11116 | 8.3744 |
| 1.5987 | 795.0 | 11130 | 8.4044 |
| 1.658 | 796.0 | 11144 | 8.3545 |
| 1.6428 | 797.0 | 11158 | 8.4194 |
| 1.6785 | 798.0 | 11172 | 8.4275 |
| 1.7494 | 799.0 | 11186 | 8.4095 |
| 1.6773 | 800.0 | 11200 | 8.4406 |
| 1.6574 | 801.0 | 11214 | 8.4203 |
| 1.6295 | 802.0 | 11228 | 8.4089 |
| 1.6853 | 803.0 | 11242 | 8.4302 |
| 1.721 | 804.0 | 11256 | 8.3972 |
| 1.6818 | 805.0 | 11270 | 8.4153 |
| 1.6791 | 806.0 | 11284 | 8.3946 |
| 1.6934 | 807.0 | 11298 | 8.4320 |
| 1.6146 | 808.0 | 11312 | 8.3993 |
| 1.6572 | 809.0 | 11326 | 8.3965 |
| 1.6294 | 810.0 | 11340 | 8.4332 |
| 1.6696 | 811.0 | 11354 | 8.3637 |
| 1.6836 | 812.0 | 11368 | 8.3735 |
| 1.6584 | 813.0 | 11382 | 8.3858 |
| 1.6558 | 814.0 | 11396 | 8.4076 |
| 1.6694 | 815.0 | 11410 | 8.4261 |
| 1.6832 | 816.0 | 11424 | 8.3389 |
| 1.6495 | 817.0 | 11438 | 8.3707 |
| 1.6634 | 818.0 | 11452 | 8.4203 |
| 1.6297 | 819.0 | 11466 | 8.3698 |
| 1.707 | 820.0 | 11480 | 8.4409 |
| 1.6803 | 821.0 | 11494 | 8.4208 |
| 1.6937 | 822.0 | 11508 | 8.3948 |
| 1.6568 | 823.0 | 11522 | 8.4195 |
| 1.6149 | 824.0 | 11536 | 8.3845 |
| 1.7053 | 825.0 | 11550 | 8.4389 |
| 1.6266 | 826.0 | 11564 | 8.4311 |
| 1.6433 | 827.0 | 11578 | 8.4266 |
| 1.6457 | 828.0 | 11592 | 8.4125 |
| 1.6661 | 829.0 | 11606 | 8.4158 |
| 1.658 | 830.0 | 11620 | 8.3599 |
| 1.6571 | 831.0 | 11634 | 8.3702 |
| 1.6013 | 832.0 | 11648 | 8.3815 |
| 1.7019 | 833.0 | 11662 | 8.4036 |
| 1.7593 | 834.0 | 11676 | 8.3840 |
| 1.6475 | 835.0 | 11690 | 8.4442 |
| 1.7178 | 836.0 | 11704 | 8.4266 |
| 1.6553 | 837.0 | 11718 | 8.3928 |
| 1.6011 | 838.0 | 11732 | 8.4062 |
| 1.7054 | 839.0 | 11746 | 8.4588 |
| 1.6839 | 840.0 | 11760 | 8.4208 |
| 1.6801 | 841.0 | 11774 | 8.4557 |
| 1.6917 | 842.0 | 11788 | 8.4300 |
| 1.6058 | 843.0 | 11802 | 8.4644 |
| 1.6321 | 844.0 | 11816 | 8.4319 |
| 1.6348 | 845.0 | 11830 | 8.4124 |
| 1.6118 | 846.0 | 11844 | 8.4667 |
| 1.6816 | 847.0 | 11858 | 8.4325 |
| 1.7574 | 848.0 | 11872 | 8.4365 |
| 1.7383 | 849.0 | 11886 | 8.4195 |
| 1.6522 | 850.0 | 11900 | 8.4343 |
| 1.61 | 851.0 | 11914 | 8.3775 |
| 1.5419 | 852.0 | 11928 | 8.4417 |
| 1.6468 | 853.0 | 11942 | 8.3903 |
| 1.5909 | 854.0 | 11956 | 8.4087 |
| 1.6376 | 855.0 | 11970 | 8.4391 |
| 1.6814 | 856.0 | 11984 | 8.3896 |
| 1.709 | 857.0 | 11998 | 8.4093 |
| 1.6551 | 858.0 | 12012 | 8.4793 |
| 1.6193 | 859.0 | 12026 | 8.4586 |
| 1.5831 | 860.0 | 12040 | 8.4748 |
| 1.6869 | 861.0 | 12054 | 8.4088 |
| 1.5926 | 862.0 | 12068 | 8.4639 |
| 1.6037 | 863.0 | 12082 | 8.4009 |
| 1.6878 | 864.0 | 12096 | 8.4111 |
| 1.6304 | 865.0 | 12110 | 8.4439 |
| 1.6106 | 866.0 | 12124 | 8.4202 |
| 1.706 | 867.0 | 12138 | 8.4072 |
| 1.6757 | 868.0 | 12152 | 8.4280 |
| 1.6875 | 869.0 | 12166 | 8.4363 |
| 1.6446 | 870.0 | 12180 | 8.4161 |
| 1.6064 | 871.0 | 12194 | 8.4567 |
| 1.6919 | 872.0 | 12208 | 8.3782 |
| 1.7078 | 873.0 | 12222 | 8.4134 |
| 1.6615 | 874.0 | 12236 | 8.4309 |
| 1.6502 | 875.0 | 12250 | 8.4540 |
| 1.6191 | 876.0 | 12264 | 8.4217 |
| 1.5853 | 877.0 | 12278 | 8.4459 |
| 1.6026 | 878.0 | 12292 | 8.4472 |
| 1.5958 | 879.0 | 12306 | 8.4375 |
| 1.578 | 880.0 | 12320 | 8.4241 |
| 1.6237 | 881.0 | 12334 | 8.4755 |
| 1.6813 | 882.0 | 12348 | 8.4056 |
| 1.6387 | 883.0 | 12362 | 8.3981 |
| 1.6507 | 884.0 | 12376 | 8.3909 |
| 1.6125 | 885.0 | 12390 | 8.4215 |
| 1.5733 | 886.0 | 12404 | 8.4279 |
| 1.6605 | 887.0 | 12418 | 8.4636 |
| 1.5989 | 888.0 | 12432 | 8.3820 |
| 1.6497 | 889.0 | 12446 | 8.4128 |
| 1.7232 | 890.0 | 12460 | 8.3742 |
| 1.5997 | 891.0 | 12474 | 8.4520 |
| 1.6266 | 892.0 | 12488 | 8.4202 |
| 1.5987 | 893.0 | 12502 | 8.3965 |
| 1.61 | 894.0 | 12516 | 8.4320 |
| 1.6795 | 895.0 | 12530 | 8.4613 |
| 1.6943 | 896.0 | 12544 | 8.4632 |
| 1.684 | 897.0 | 12558 | 8.4431 |
| 1.5806 | 898.0 | 12572 | 8.4409 |
| 1.6391 | 899.0 | 12586 | 8.4435 |
| 1.5754 | 900.0 | 12600 | 8.4244 |
| 1.617 | 901.0 | 12614 | 8.5100 |
| 1.6577 | 902.0 | 12628 | 8.4541 |
| 1.6852 | 903.0 | 12642 | 8.4127 |
| 1.6827 | 904.0 | 12656 | 8.4485 |
| 1.7296 | 905.0 | 12670 | 8.4526 |
| 1.6258 | 906.0 | 12684 | 8.4508 |
| 1.6527 | 907.0 | 12698 | 8.4121 |
| 1.6281 | 908.0 | 12712 | 8.4373 |
| 1.669 | 909.0 | 12726 | 8.4089 |
| 1.6757 | 910.0 | 12740 | 8.4098 |
| 1.5908 | 911.0 | 12754 | 8.4633 |
| 1.5935 | 912.0 | 12768 | 8.4115 |
| 1.7143 | 913.0 | 12782 | 8.4141 |
| 1.6203 | 914.0 | 12796 | 8.4084 |
| 1.5932 | 915.0 | 12810 | 8.4168 |
| 1.5663 | 916.0 | 12824 | 8.4124 |
| 1.7151 | 917.0 | 12838 | 8.4289 |
| 1.5585 | 918.0 | 12852 | 8.4381 |
| 1.5971 | 919.0 | 12866 | 8.4276 |
| 1.618 | 920.0 | 12880 | 8.5005 |
| 1.6389 | 921.0 | 12894 | 8.4777 |
| 1.6325 | 922.0 | 12908 | 8.4180 |
| 1.5971 | 923.0 | 12922 | 8.4586 |
| 1.662 | 924.0 | 12936 | 8.4755 |
| 1.5642 | 925.0 | 12950 | 8.4858 |
| 1.6417 | 926.0 | 12964 | 8.4075 |
| 1.5845 | 927.0 | 12978 | 8.4482 |
| 1.6328 | 928.0 | 12992 | 8.4674 |
| 1.6089 | 929.0 | 13006 | 8.4809 |
| 1.6248 | 930.0 | 13020 | 8.4445 |
| 1.6356 | 931.0 | 13034 | 8.4150 |
| 1.5573 | 932.0 | 13048 | 8.4527 |
| 1.5802 | 933.0 | 13062 | 8.4293 |
| 1.6374 | 934.0 | 13076 | 8.4326 |
| 1.6386 | 935.0 | 13090 | 8.4387 |
| 1.6332 | 936.0 | 13104 | 8.4280 |
| 1.6449 | 937.0 | 13118 | 8.4479 |
| 1.6463 | 938.0 | 13132 | 8.5086 |
| 1.6683 | 939.0 | 13146 | 8.5132 |
| 1.6339 | 940.0 | 13160 | 8.4324 |
| 1.6254 | 941.0 | 13174 | 8.3933 |
| 1.6181 | 942.0 | 13188 | 8.4453 |
| 1.6687 | 943.0 | 13202 | 8.4307 |
| 1.6226 | 944.0 | 13216 | 8.4446 |
| 1.6073 | 945.0 | 13230 | 8.4396 |
| 1.6811 | 946.0 | 13244 | 8.4554 |
| 1.7156 | 947.0 | 13258 | 8.4664 |
| 1.6668 | 948.0 | 13272 | 8.4237 |
| 1.5831 | 949.0 | 13286 | 8.4896 |
| 1.6013 | 950.0 | 13300 | 8.4042 |
| 1.6187 | 951.0 | 13314 | 8.4638 |
| 1.6534 | 952.0 | 13328 | 8.4318 |
| 1.5723 | 953.0 | 13342 | 8.4765 |
| 1.6962 | 954.0 | 13356 | 8.4333 |
| 1.6276 | 955.0 | 13370 | 8.4123 |
| 1.6247 | 956.0 | 13384 | 8.4544 |
| 1.5247 | 957.0 | 13398 | 8.5119 |
| 1.6938 | 958.0 | 13412 | 8.4676 |
| 1.5473 | 959.0 | 13426 | 8.4564 |
| 1.6351 | 960.0 | 13440 | 8.4216 |
| 1.5666 | 961.0 | 13454 | 8.4527 |
| 1.5694 | 962.0 | 13468 | 8.4845 |
| 1.6145 | 963.0 | 13482 | 8.4874 |
| 1.6097 | 964.0 | 13496 | 8.4375 |
| 1.5509 | 965.0 | 13510 | 8.4756 |
| 1.6273 | 966.0 | 13524 | 8.4434 |
| 1.6753 | 967.0 | 13538 | 8.4436 |
| 1.6287 | 968.0 | 13552 | 8.4596 |
| 1.6815 | 969.0 | 13566 | 8.4737 |
| 1.5847 | 970.0 | 13580 | 8.4677 |
| 1.5944 | 971.0 | 13594 | 8.4672 |
| 1.6673 | 972.0 | 13608 | 8.4842 |
| 1.6222 | 973.0 | 13622 | 8.4797 |
| 1.5753 | 974.0 | 13636 | 8.4548 |
| 1.5424 | 975.0 | 13650 | 8.4425 |
| 1.7219 | 976.0 | 13664 | 8.4936 |
| 1.6176 | 977.0 | 13678 | 8.4755 |
| 1.641 | 978.0 | 13692 | 8.5016 |
| 1.6122 | 979.0 | 13706 | 8.4846 |
| 1.6079 | 980.0 | 13720 | 8.4741 |
| 1.5988 | 981.0 | 13734 | 8.4980 |
| 1.6562 | 982.0 | 13748 | 8.4493 |
| 1.6119 | 983.0 | 13762 | 8.4512 |
| 1.5294 | 984.0 | 13776 | 8.4362 |
| 1.632 | 985.0 | 13790 | 8.4692 |
| 1.5564 | 986.0 | 13804 | 8.4567 |
| 1.6513 | 987.0 | 13818 | 8.4790 |
| 1.6117 | 988.0 | 13832 | 8.4878 |
| 1.6394 | 989.0 | 13846 | 8.4349 |
| 1.6707 | 990.0 | 13860 | 8.4176 |
| 1.6212 | 991.0 | 13874 | 8.4704 |
| 1.5721 | 992.0 | 13888 | 8.4319 |
| 1.5873 | 993.0 | 13902 | 8.4356 |
| 1.6527 | 994.0 | 13916 | 8.4670 |
| 1.6499 | 995.0 | 13930 | 8.4717 |
| 1.5975 | 996.0 | 13944 | 8.4682 |
| 1.6759 | 997.0 | 13958 | 8.4861 |
| 1.5962 | 998.0 | 13972 | 8.4686 |
| 1.6465 | 999.0 | 13986 | 8.5004 |
| 1.6321 | 1000.0 | 14000 | 8.4563 |
### Framework versions
- Transformers 4.13.0
- Pytorch 1.12.0+cu113
- Datasets 1.16.1
- Tokenizers 0.10.3
|
BlindMan820/Sarcastic-News-Headlines | [
"pytorch",
"distilbert",
"text-classification",
"English",
"dataset:Kaggle Dataset",
"transformers",
"Text",
"Sequence-Classification",
"Sarcasm",
"DistilBert"
] | text-classification | {
"architectures": [
"DistilBertForSequenceClassification"
],
"model_type": "distilbert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
"early_stopping": null,
"length_penalty": null,
"max_length": null,
"min_length": null,
"no_repeat_ngram_size": null,
"num_beams": null,
"prefix": null
},
"text-generation": {
"do_sample": null,
"max_length": null
},
"translation_en_to_de": {
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} | 28 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-009901
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 1.7899
---
<!-- 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. -->
# distilled-mt5-small-009901
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4697
- Bleu: 1.7899
- Gen Len: 46.5638
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert_uncased_L-2_H-128_A-2-finetuned-parsed
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_uncased_L-2_H-128_A-2-finetuned-parsed
This model is a fine-tuned version of [google/bert_uncased_L-2_H-128_A-2](https://huggingface.co/google/bert_uncased_L-2_H-128_A-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2883
## 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: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log | 1.0 | 59 | 4.6900 |
| No log | 2.0 | 118 | 4.6347 |
| No log | 3.0 | 177 | 4.6578 |
| No log | 4.0 | 236 | 4.5731 |
| No log | 5.0 | 295 | 4.6258 |
| No log | 6.0 | 354 | 4.6365 |
| No log | 7.0 | 413 | 4.7292 |
| No log | 8.0 | 472 | 4.4789 |
| 4.5634 | 9.0 | 531 | 4.3161 |
| 4.5634 | 10.0 | 590 | 4.6929 |
| 4.5634 | 11.0 | 649 | 4.5543 |
| 4.5634 | 12.0 | 708 | 4.3739 |
| 4.5634 | 13.0 | 767 | 4.6118 |
| 4.5634 | 14.0 | 826 | 4.4036 |
| 4.5634 | 15.0 | 885 | 4.3940 |
| 4.5634 | 16.0 | 944 | 4.5944 |
| 4.0896 | 17.0 | 1003 | 4.3630 |
| 4.0896 | 18.0 | 1062 | 4.0447 |
| 4.0896 | 19.0 | 1121 | 4.3832 |
| 4.0896 | 20.0 | 1180 | 4.0535 |
| 4.0896 | 21.0 | 1239 | 4.5213 |
| 4.0896 | 22.0 | 1298 | 4.5887 |
| 4.0896 | 23.0 | 1357 | 4.5211 |
| 4.0896 | 24.0 | 1416 | 4.1876 |
| 4.0896 | 25.0 | 1475 | 4.5861 |
| 3.9145 | 26.0 | 1534 | 4.3581 |
| 3.9145 | 27.0 | 1593 | 4.6545 |
| 3.9145 | 28.0 | 1652 | 4.4919 |
| 3.9145 | 29.0 | 1711 | 4.1109 |
| 3.9145 | 30.0 | 1770 | 4.2736 |
| 3.9145 | 31.0 | 1829 | 4.6461 |
| 3.9145 | 32.0 | 1888 | 4.3111 |
| 3.9145 | 33.0 | 1947 | 4.2909 |
| 3.8088 | 34.0 | 2006 | 4.1168 |
| 3.8088 | 35.0 | 2065 | 4.2329 |
| 3.8088 | 36.0 | 2124 | 4.5285 |
| 3.8088 | 37.0 | 2183 | 4.4841 |
| 3.8088 | 38.0 | 2242 | 4.2489 |
| 3.8088 | 39.0 | 2301 | 4.2384 |
| 3.8088 | 40.0 | 2360 | 4.3610 |
| 3.8088 | 41.0 | 2419 | 4.2758 |
| 3.8088 | 42.0 | 2478 | 4.2895 |
| 3.7034 | 43.0 | 2537 | 4.2824 |
| 3.7034 | 44.0 | 2596 | 4.4997 |
| 3.7034 | 45.0 | 2655 | 4.5091 |
| 3.7034 | 46.0 | 2714 | 4.0883 |
| 3.7034 | 47.0 | 2773 | 4.2018 |
| 3.7034 | 48.0 | 2832 | 4.3701 |
| 3.7034 | 49.0 | 2891 | 4.0764 |
| 3.7034 | 50.0 | 2950 | 4.6149 |
| 3.6455 | 51.0 | 3009 | 4.3629 |
| 3.6455 | 52.0 | 3068 | 4.2199 |
| 3.6455 | 53.0 | 3127 | 4.3543 |
| 3.6455 | 54.0 | 3186 | 4.7006 |
| 3.6455 | 55.0 | 3245 | 4.1633 |
| 3.6455 | 56.0 | 3304 | 4.5183 |
| 3.6455 | 57.0 | 3363 | 4.1918 |
| 3.6455 | 58.0 | 3422 | 4.4810 |
| 3.6455 | 59.0 | 3481 | 4.1398 |
| 3.5468 | 60.0 | 3540 | 3.9632 |
| 3.5468 | 61.0 | 3599 | 4.4640 |
| 3.5468 | 62.0 | 3658 | 4.0500 |
| 3.5468 | 63.0 | 3717 | 4.3956 |
| 3.5468 | 64.0 | 3776 | 4.3922 |
| 3.5468 | 65.0 | 3835 | 4.2513 |
| 3.5468 | 66.0 | 3894 | 4.4475 |
| 3.5468 | 67.0 | 3953 | 4.3037 |
| 3.4975 | 68.0 | 4012 | 4.1568 |
| 3.4975 | 69.0 | 4071 | 4.2253 |
| 3.4975 | 70.0 | 4130 | 4.1202 |
| 3.4975 | 71.0 | 4189 | 4.4421 |
| 3.4975 | 72.0 | 4248 | 4.3548 |
| 3.4975 | 73.0 | 4307 | 4.1671 |
| 3.4975 | 74.0 | 4366 | 4.4090 |
| 3.4975 | 75.0 | 4425 | 4.1064 |
| 3.4975 | 76.0 | 4484 | 4.2109 |
| 3.44 | 77.0 | 4543 | 4.3244 |
| 3.44 | 78.0 | 4602 | 4.1995 |
| 3.44 | 79.0 | 4661 | 4.4518 |
| 3.44 | 80.0 | 4720 | 4.1991 |
| 3.44 | 81.0 | 4779 | 4.4183 |
| 3.44 | 82.0 | 4838 | 4.2173 |
| 3.44 | 83.0 | 4897 | 4.1721 |
| 3.44 | 84.0 | 4956 | 4.1931 |
| 3.3916 | 85.0 | 5015 | 4.3280 |
| 3.3916 | 86.0 | 5074 | 4.3347 |
| 3.3916 | 87.0 | 5133 | 4.3243 |
| 3.3916 | 88.0 | 5192 | 4.2708 |
| 3.3916 | 89.0 | 5251 | 4.1580 |
| 3.3916 | 90.0 | 5310 | 4.0348 |
| 3.3916 | 91.0 | 5369 | 4.0605 |
| 3.3916 | 92.0 | 5428 | 4.2083 |
| 3.3916 | 93.0 | 5487 | 4.2378 |
| 3.3817 | 94.0 | 5546 | 4.2171 |
| 3.3817 | 95.0 | 5605 | 3.9581 |
| 3.3817 | 96.0 | 5664 | 4.1668 |
| 3.3817 | 97.0 | 5723 | 4.0394 |
| 3.3817 | 98.0 | 5782 | 4.2231 |
| 3.3817 | 99.0 | 5841 | 4.1900 |
| 3.3817 | 100.0 | 5900 | 4.3041 |
| 3.3817 | 101.0 | 5959 | 4.3827 |
| 3.3526 | 102.0 | 6018 | 4.0975 |
| 3.3526 | 103.0 | 6077 | 4.3543 |
| 3.3526 | 104.0 | 6136 | 4.2104 |
| 3.3526 | 105.0 | 6195 | 4.2408 |
| 3.3526 | 106.0 | 6254 | 4.4281 |
| 3.3526 | 107.0 | 6313 | 4.4816 |
| 3.3526 | 108.0 | 6372 | 4.1995 |
| 3.3526 | 109.0 | 6431 | 4.1844 |
| 3.3526 | 110.0 | 6490 | 4.2414 |
| 3.3035 | 111.0 | 6549 | 4.3478 |
| 3.3035 | 112.0 | 6608 | 3.9579 |
| 3.3035 | 113.0 | 6667 | 4.2558 |
| 3.3035 | 114.0 | 6726 | 4.0050 |
| 3.3035 | 115.0 | 6785 | 4.1944 |
| 3.3035 | 116.0 | 6844 | 4.0384 |
| 3.3035 | 117.0 | 6903 | 4.5749 |
| 3.3035 | 118.0 | 6962 | 4.3816 |
| 3.2884 | 119.0 | 7021 | 4.0829 |
| 3.2884 | 120.0 | 7080 | 4.1100 |
| 3.2884 | 121.0 | 7139 | 4.3181 |
| 3.2884 | 122.0 | 7198 | 4.2051 |
| 3.2884 | 123.0 | 7257 | 4.1495 |
| 3.2884 | 124.0 | 7316 | 4.2398 |
| 3.2884 | 125.0 | 7375 | 4.2553 |
| 3.2884 | 126.0 | 7434 | 4.0788 |
| 3.2884 | 127.0 | 7493 | 4.4999 |
| 3.2817 | 128.0 | 7552 | 4.4331 |
| 3.2817 | 129.0 | 7611 | 4.3983 |
| 3.2817 | 130.0 | 7670 | 4.1597 |
| 3.2817 | 131.0 | 7729 | 4.2732 |
| 3.2817 | 132.0 | 7788 | 4.1203 |
| 3.2817 | 133.0 | 7847 | 4.4417 |
| 3.2817 | 134.0 | 7906 | 4.0591 |
| 3.2817 | 135.0 | 7965 | 4.0435 |
| 3.252 | 136.0 | 8024 | 4.0461 |
| 3.252 | 137.0 | 8083 | 4.2521 |
| 3.252 | 138.0 | 8142 | 4.2749 |
| 3.252 | 139.0 | 8201 | 4.1346 |
| 3.252 | 140.0 | 8260 | 4.0411 |
| 3.252 | 141.0 | 8319 | 4.0656 |
| 3.252 | 142.0 | 8378 | 4.3978 |
| 3.252 | 143.0 | 8437 | 4.0533 |
| 3.252 | 144.0 | 8496 | 3.9734 |
| 3.217 | 145.0 | 8555 | 4.2113 |
| 3.217 | 146.0 | 8614 | 4.5480 |
| 3.217 | 147.0 | 8673 | 4.1805 |
| 3.217 | 148.0 | 8732 | 4.2144 |
| 3.217 | 149.0 | 8791 | 4.1457 |
| 3.217 | 150.0 | 8850 | 4.3311 |
| 3.217 | 151.0 | 8909 | 4.1565 |
| 3.217 | 152.0 | 8968 | 4.3584 |
| 3.2183 | 153.0 | 9027 | 4.3837 |
| 3.2183 | 154.0 | 9086 | 4.0912 |
| 3.2183 | 155.0 | 9145 | 4.0785 |
| 3.2183 | 156.0 | 9204 | 4.2501 |
| 3.2183 | 157.0 | 9263 | 4.1515 |
| 3.2183 | 158.0 | 9322 | 4.0559 |
| 3.2183 | 159.0 | 9381 | 3.9969 |
| 3.2183 | 160.0 | 9440 | 4.0528 |
| 3.2183 | 161.0 | 9499 | 3.9618 |
| 3.2109 | 162.0 | 9558 | 4.2596 |
| 3.2109 | 163.0 | 9617 | 4.0760 |
| 3.2109 | 164.0 | 9676 | 4.2589 |
| 3.2109 | 165.0 | 9735 | 4.2227 |
| 3.2109 | 166.0 | 9794 | 4.3354 |
| 3.2109 | 167.0 | 9853 | 4.3471 |
| 3.2109 | 168.0 | 9912 | 4.1578 |
| 3.2109 | 169.0 | 9971 | 4.4163 |
| 3.1868 | 170.0 | 10030 | 4.0754 |
| 3.1868 | 171.0 | 10089 | 4.2543 |
| 3.1868 | 172.0 | 10148 | 3.9498 |
| 3.1868 | 173.0 | 10207 | 4.0863 |
| 3.1868 | 174.0 | 10266 | 4.3090 |
| 3.1868 | 175.0 | 10325 | 4.2731 |
| 3.1868 | 176.0 | 10384 | 4.1997 |
| 3.1868 | 177.0 | 10443 | 4.2273 |
| 3.1905 | 178.0 | 10502 | 4.3560 |
| 3.1905 | 179.0 | 10561 | 4.3330 |
| 3.1905 | 180.0 | 10620 | 4.1770 |
| 3.1905 | 181.0 | 10679 | 3.8779 |
| 3.1905 | 182.0 | 10738 | 4.2199 |
| 3.1905 | 183.0 | 10797 | 4.1409 |
| 3.1905 | 184.0 | 10856 | 4.3601 |
| 3.1905 | 185.0 | 10915 | 4.2380 |
| 3.1905 | 186.0 | 10974 | 4.4688 |
| 3.1774 | 187.0 | 11033 | 4.2305 |
| 3.1774 | 188.0 | 11092 | 3.9129 |
| 3.1774 | 189.0 | 11151 | 4.2889 |
| 3.1774 | 190.0 | 11210 | 3.8790 |
| 3.1774 | 191.0 | 11269 | 4.4458 |
| 3.1774 | 192.0 | 11328 | 4.2899 |
| 3.1774 | 193.0 | 11387 | 4.4378 |
| 3.1774 | 194.0 | 11446 | 4.2316 |
| 3.179 | 195.0 | 11505 | 4.0360 |
| 3.179 | 196.0 | 11564 | 4.1284 |
| 3.179 | 197.0 | 11623 | 4.3879 |
| 3.179 | 198.0 | 11682 | 4.0715 |
| 3.179 | 199.0 | 11741 | 4.1888 |
| 3.179 | 200.0 | 11800 | 4.3268 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BobBraico/bert-finetuned-ner | [] | null | {
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} | 0 | null | ---
library_name: FastAI
tags:
- FastAI
---
# British Columbia Invasive Plants Identifier
## Model Details
An invasive plant classifier trained on BingSearch Images scraped dataset with FastAi.
Model is able to detect 6 species (marked for Provincial Containment) of invasive plants defined by the government of British Columbia.
Hosted in a HuggingFace Space accessible here: https://huggingface.co/spaces/et-do/bc_invasive_plant_classifier
## Notebook Details
In the main notebook (https://github.com/et-do/invasive_plant_classifier), I aim to apply transfer learning methods to a PyTorch image classification CNN (resnet34) to be able to identify both the species and the level of invasiveness to British Columbia as deemed by https://www2.gov.bc.ca/gov/content/environment/plants-animals-ecosystems/invasive-species/priority-species/priority-plants
Currently, the BC government identifies invasive plants across 5 categories:
Prevent: Species determined to be high risk to BC and not yet established. Management objective is prevent the introduction and establishment.
Provincial EDRR: Species is high risk to B.C. and is new to the Province. Management objective is eradication.
Provincial Containment: Species is high risk with limited extent in B.C. but significant potential to spread. Management objective is to prevent further expansion into new areas with the ultimate goal of reducing the overall extent.
Regional containment/Control: Species is high risk and well established, or medium risk with high potential for spread. Management objective is to prevent further expansion into new areas within the region through establishment of containment lines and identification of occurrences outside the line to control.
Management: Species is more widespread but may be of concern in specific situations with certain high values - e.g., conservation lands, specific agriculture crops. Management objective is to reduce the invasive species impacts locally or regionally, where resources are available.
All of these categories could be extremely relevant to a free-to-use plant-identifier web app. However, in the sake of API costs, resource management, and model complexity, the first version of the model will only be trained to recognize plants under the Provincial Containment category (n=6). As the web app won't be geographically restricted, being able to use it both inside BC and outside BC to identify these plants that have a management objective of limiting outer-provincial occurrences could provide immense value.
The notebook will walkthrough:
- Data gathering and validation
- Data preprocessing & augmentation via FastAi dataloaders
- Training the model on the new dataset, and using results to further clean the data
- Serving the model under a huggingface space
|
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} | 0 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-900010
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 1.1552
---
<!-- 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. -->
# distilled-mt5-small-900010
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.8177
- Bleu: 1.1552
- Gen Len: 98.4712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Bosio/full-sentence-distillroberta3-finetuned-wikitext2 | [] | null | {
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} | 0 | null | ---
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
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9245
- name: F1
type: f1
value: 0.9247291070290931
---
<!-- 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: 0.2109
- Accuracy: 0.9245
- F1: 0.9247
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8203 | 1.0 | 250 | 0.3080 | 0.909 | 0.9072 |
| 0.2412 | 2.0 | 500 | 0.2109 | 0.9245 | 0.9247 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Brayan/CNN_Brain_Tumor | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-1000-samples
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-1000-samples
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1604
- Accuracy: 0.74
## 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
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BritishLibraryLabs/bl-books-genre | [
"pytorch",
"distilbert",
"text-classification",
"multilingual",
"dataset:blbooksgenre",
"transformers",
"genre",
"books",
"library",
"historic",
"glam ",
"lam",
"license:mit",
"has_space"
] | text-classification | {
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"DistilBertForSequenceClassification"
],
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} | 76 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-010099
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 5.9209
---
<!-- 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. -->
# distilled-mt5-small-010099
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9787
- Bleu: 5.9209
- Gen Len: 50.1856
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Broadus20/DialoGPT-small-joshua | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 12 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 182.89 +/- 52.91
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
Brokette/projetCS | [
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"transformers"
] | automatic-speech-recognition | {
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}
} | 4 | null | ---
tags:
- generated_from_trainer
model-index:
- name: roberta-large-finetuned-wholemasking
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-large-finetuned-wholemasking
This model is a fine-tuned version of [klue/roberta-large](https://huggingface.co/klue/roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3587
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.4224 | 1.0 | 6149 | 0.3820 |
| 0.3838 | 2.0 | 12298 | 0.3644 |
| 0.3643 | 3.0 | 18447 | 0.3512 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
BrunoNogueira/DialoGPT-kungfupanda | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 10 | null | ---
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
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9183870967741935
---
<!-- 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: 0.7721
- Accuracy: 0.9184
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2896 | 1.0 | 318 | 3.2890 | 0.7432 |
| 2.6284 | 2.0 | 636 | 1.8756 | 0.8377 |
| 1.5483 | 3.0 | 954 | 1.1572 | 0.8961 |
| 1.015 | 4.0 | 1272 | 0.8573 | 0.9132 |
| 0.7953 | 5.0 | 1590 | 0.7721 | 0.9184 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_keras_callback
model-index:
- name: leabum/distilbert-base-uncased-finetuned-cuad
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# leabum/distilbert-base-uncased-finetuned-cuad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5490
- Train End Logits Accuracy: 0.9403
- Train Start Logits Accuracy: 0.9403
- Validation Loss: 0.3567
- Validation End Logits Accuracy: 0.9612
- Validation Start Logits Accuracy: 0.9612
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'learning_rate': {'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 220, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.2209 | 0.9205 | 0.9017 | 0.3867 | 0.9612 | 0.9612 | 0 |
| 0.5490 | 0.9403 | 0.9403 | 0.3567 | 0.9612 | 0.9612 | 1 |
### Framework versions
- Transformers 4.21.1
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Brykee/DialoGPT-medium-Morty | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 10 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-3000-samples_fr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# finetuning-sentiment-model-3000-samples_fr
This model is a fine-tuned version of [zboxi7/finetuning-sentiment-model-3000-samples](https://huggingface.co/zboxi7/finetuning-sentiment-model-3000-samples) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4052
- Accuracy: 0.7033
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Bryson575x/riceboi | [] | null | {
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} | 0 | null | ---
language:
- en
thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
tags:
- question-answering
license: apache-2.0
datasets:
- squad
metrics:
- squad
---
# DistilBERT with a second step of distillation
## Model description
This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
In this version, the following pre-trained models were used:
* Student: `distilbert-base-uncased`
* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
## Training data
This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
```python
from datasets import load_dataset
squad = load_dataset('squad')
```
## Training procedure
## Eval results
| | Exact Match | F1 |
|------------------|-------------|------|
| DistilBERT paper | 79.1 | 86.9 |
| Ours | 78.4 | 86.5 |
The scores were calculated using the `squad` metric from `datasets`.
### BibTeX entry and citation info
```bibtex
@misc{sanh2020distilbert,
title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter},
author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
year={2020},
eprint={1910.01108},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
BumBelDumBel/ZORK-AI-TEST | [
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"transformers",
"generated_from_trainer",
"license:mit"
] | text-generation | {
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} | 9 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-hiddentest
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 0.4773
---
<!-- 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. -->
# distilled-mt5-small-hiddentest
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 3.9223
- Bleu: 0.4773
- Gen Len: 51.3902
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: token_fine_tunned_flipkart_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# token_fine_tunned_flipkart_2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3435
- Precision: 0.8797
- Recall: 0.9039
- F1: 0.8916
- Accuracy: 0.9061
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 109 | 0.5647 | 0.7398 | 0.8123 | 0.7744 | 0.8111 |
| No log | 2.0 | 218 | 0.3863 | 0.8165 | 0.8751 | 0.8448 | 0.8716 |
| No log | 3.0 | 327 | 0.3367 | 0.8599 | 0.8847 | 0.8721 | 0.8869 |
| No log | 4.0 | 436 | 0.3266 | 0.8688 | 0.8911 | 0.8798 | 0.8977 |
| 0.527 | 5.0 | 545 | 0.3508 | 0.8595 | 0.8898 | 0.8744 | 0.8909 |
| 0.527 | 6.0 | 654 | 0.3410 | 0.8748 | 0.9045 | 0.8894 | 0.9009 |
| 0.527 | 7.0 | 763 | 0.3431 | 0.8754 | 0.9045 | 0.8897 | 0.9049 |
| 0.527 | 8.0 | 872 | 0.3435 | 0.8797 | 0.9039 | 0.8916 | 0.9061 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.2
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: cc-by-sa-4.0
tags:
- generated_from_trainer
datasets:
- cuad
model-index:
- name: legal-bert-small-uncased-cuad
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. -->
# legal-bert-small-uncased-cuad
This model is a fine-tuned version of [nlpaueb/legal-bert-small-uncased](https://huggingface.co/nlpaueb/legal-bert-small-uncased) on the cuad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0371
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0565 | 1.0 | 11081 | 0.0411 |
| 0.0489 | 2.0 | 22162 | 0.0364 |
| 0.0409 | 3.0 | 33243 | 0.0371 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
---
# **Q-Learning** Agent playing **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="apurva19/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
Buntan/bert-finetuned-ner | [
"pytorch",
"tensorboard",
"bert",
"token-classification",
"dataset:conll2003",
"transformers",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible"
] | token-classification | {
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} | 8 | null | ---
language:
- en
- ro
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: distilled-mt5-small-010099-full
results:
- task:
name: Translation
type: translation
dataset:
name: wmt16 ro-en
type: wmt16
args: ro-en
metrics:
- name: Bleu
type: bleu
value: 21.2377
---
<!-- 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. -->
# distilled-mt5-small-010099-full
This model is a fine-tuned version of [google/mt5-small](https://huggingface.co/google/mt5-small) on the wmt16 ro-en dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9934
- Bleu: 21.2377
- Gen Len: 44.0745
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- 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
### Training results
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
Bwehfuk/Ron | [] | null | {
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} | 0 | null | ---
language: ja
thumbnail: https://1.bp.blogspot.com/-pOL-P7Mvgkg/YEGQAdidksI/AAAAAAABdc0/SbD0lC_X8iY_t5xLFtQYFC3FHFgziBuzgCNcBGAsYHQ/s932/buranko_businesswoman_sad.png
license: mit
tags:
- ja
- japanese
- gpt2
- text-generation
- lm
- nlp
widget:
- text: "御社を志望した理由は"
---
# ESを書くAI
Japanese GPT-2 modelをファインチューニングしました
ファインチューニングには、あらゆる分野から140,000件ほどのESを用いました。
webアプリ<br>
http://www.eswrite.com
The model was trained using code from Github repository [rinnakk/japanese-pretrained-models](https://github.com/rinnakk/japanese-pretrained-models) by [rinna Co., Ltd.](https://corp.rinna.co.jp/) |
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} | 0 | null | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
Abbreviations:
- p31 = Updated version of p3 with new prompts
- xp3 = Multilingual version of P3
- cap = Example Capping (100K / dataset)
- mix = Validation is 5% of train (Else it is the validation set of the datasets used)
- brack = old model with a bug (targets had brackets around them, so it always generates brackets)
- lossseq: Uses https://github.com/bigscience-workshop/Megatron-DeepSpeed/pull/326 (The idea is to give every target the same weight regardless of its length)
Code:
- Training Code is MegDS
- xp3 creation; eval scripts; training scripts are all here: https://github.com/bigscience-workshop/bigscience/pull/57
Known issues:
- xP3 has leakage across languages (I.e. in the mixed setup the same sample in the training set may appear in the validation set in a different language)
- The non-mixed xp3 versions have validation sets with a different distribution than xp3 with some langs missing entirely as there are no val sets
XP3 language composition (Training dataset for XP3 has ~the same distribution / language as the below XP3 percentages):
```
Language & Code & ROOTS (perc) & xP3 (perc) & xP3 (MB) & xP3 (M tokens)\\
English & en & 30.038 & 43.139 & 36944.9 & 11083.5\\
Chinese & zh & 16.215 & 4.532 & 3881.2 & 1164.4\\
French & fr & 12.898 & 6.51 & 5575.1 & 1672.5\\
Spanish & es & 10.846 & 7.668 & 6566.6 & 1970.0\\
Programming Languages & code & 10.821 & 0.739 & 633.0 & 189.9\\
Portuguese & pt & 4.91 & 5.976 & 5117.8 & 1535.3\\
Arabic & ar & 4.636 & 6.29 & 5386.9 & 1616.1\\
Vietnamese & vi & 2.707 & 3.325 & 2847.8 & 854.3\\
Hindi & hi & 1.525 & 2.642 & 2262.9 & 678.9\\
Indonesian & id & 1.237 & 4.802 & 4112.8 & 1233.8\\
Bengali & bn & 1.152 & 0.801 & 686.0 & 205.8\\
Catalan & ca & 1.102 & 0.169 & 145.0 & 43.5\\
Tamil & ta & 0.495 & 0.357 & 306.0 & 91.8\\
Malayalam & ml & 0.227 & 0.354 & 303.4 & 91.0\\
Telugu & te & 0.185 & 1.429 & 1223.4 & 367.0\\
Urdu & ur & 0.172 & 0.306 & 261.8 & 78.5\\
Nepali & ne & 0.158 & 0.311 & 266.3 & 79.9\\
Basque & eu & 0.146 & 0.163 & 139.7 & 41.9\\
Kannada & kn & 0.13 & 0.365 & 312.6 & 93.8\\
Marathi & mr & 0.11 & 0.322 & 276.1 & 82.8\\
Punjabi & pa & 0.097 & 0.314 & 268.7 & 80.6\\
Gujarati & gu & 0.074 & 0.306 & 261.8 & 78.5\\
Odia & or & 0.072 & 0.323 & 276.8 & 83.0\\
Assamese & as & 0.018 & 0.31 & 265.3 & 79.6\\
Swahili & sw & 0.015 & 0.759 & 649.7 & 194.9\\
Yoruba & yo & 0.006 & 0.632 & 541.1 & 162.3\\
Kinyarwanda & rw & 0.003 & 0.552 & 473.0 & 141.9\\
Xhosa & xh & 0.001 & 0.55 & 471.4 & 141.4\\
Igbo & ig & 0.001 & 0.571 & 489.2 & 146.8\\
Isi Zulu & zu & 0.001 & 0.574 & 491.8 & 147.5\\
Chi Shona & sn & 0.0 & 0.55 & 471.0 & 141.3\\
Luganda & lg & 0.0 & 0.499 & 427.4 & 128.2\\
Wolof & wo & 0.0 & 0.208 & 178.2 & 53.5\\
Kirundi & rn & 0.0 & 0.165 & 141.5 & 42.4\\
Fon & fon & 0.0 & 0.204 & 174.9 & 52.5\\
Northern Sotho & nso & 0.0 & 0.473 & 405.1 & 121.5\\
Lingala & ln & 0.0 & 0.213 & 182.8 & 54.8\\
Setswana & tn & 0.0 & 0.382 & 326.7 & 98.0\\
Twi & tw & 0.0 & 0.172 & 147.4 & 44.2\\
Chi Chewa & ny & 0.0 & 0.596 & 510.4 & 153.1\\
Sesotho & st & 0.0 & 0.17 & 145.8 & 43.7\\
Xitsonga & ts & 0.0 & 0.557 & 476.9 & 143.1\\
Akan & ak & 0.0 & 0.176 & 151.1 & 45.3\\
Bambara & bm & 0.0 & 0.175 & 149.7 & 44.9\\
Kikuyu & ki & 0.0 & 0.191 & 163.7 & 49.1\\
Chi Tumbuka & tum & 0.0 & 0.175 & 149.8 & 44.9\\
```
|
CALM/backup | [
"lean_albert",
"transformers"
] | null | {
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} | 4 | null | ---
license: apache-2.0
---
60c14be097a0f25e5da8f7cca500f6f9
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-ner | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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"BertForTokenClassification"
],
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} | 85 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
model-index:
- name: t5-small-xsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-poetry | [
"pytorch",
"tf",
"bert",
"text-classification",
"ar",
"arxiv:1905.05700",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
"model_type": "bert",
"task_specific_params": {
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} | 42 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2
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. -->
# covid-twitter-bert-v2-no_description-stance-loss-hyp-unprocess2
This model is a fine-tuned version of [digitalepidemiologylab/covid-twitter-bert-v2](https://huggingface.co/digitalepidemiologylab/covid-twitter-bert-v2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5816
- Accuracy: 0.0901
## 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: 1.4275469935864394e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 40
- 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 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8511 | 1.0 | 700 | 0.6372 | 0.1478 |
| 0.6146 | 2.0 | 1400 | 0.5816 | 0.0901 |
| 0.365 | 3.0 | 2100 | 0.6170 | 0.0749 |
| 0.2686 | 4.0 | 2800 | 0.7259 | 0.0688 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu102
- Datasets 2.2.1
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-glf | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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} | 18 | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
---
# **Q-Learning** Agent playing **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="apurva19/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
evaluate_agent(env, model["max_steps"], model["n_eval_episodes"], model["qtable"], model["eval_seed"])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-ca-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | token-classification | {
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}
} | 71 | null | ---
language:
- ru
tags:
- PyTorch
- OCR
- Segmentation
- HTR
datasets:
- "sberbank-ai/Peter"
license: mit
---
This is a weights storage for models trained by [ReadingPipeline](https://github.com/ai-forever/ReadingPipeline)
The weights are for ocr and segmentations models trained on [Peter dataset](https://huggingface.co/datasets/sberbank-ai/Peter)
|
CAMeL-Lab/bert-base-arabic-camelbert-ca | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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}
} | 580 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: xlm-roberta-base-misogyny-sexism-decay0.025-fr-indomain
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-misogyny-sexism-decay0.025-fr-indomain
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0883
- Accuracy: 0.8689
- F1: 0.0059
- Precision: 0.0811
- Recall: 0.0031
- Mae: 0.1311
## 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Mae |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| 0.3615 | 1.0 | 2302 | 0.7374 | 0.8655 | 0.0134 | 0.0986 | 0.0072 | 0.1345 |
| 0.3163 | 2.0 | 4604 | 0.8129 | 0.8674 | 0.0117 | 0.1111 | 0.0062 | 0.1326 |
| 0.2869 | 3.0 | 6906 | 0.8174 | 0.8700 | 0.0100 | 0.1562 | 0.0051 | 0.1300 |
| 0.2442 | 4.0 | 9208 | 0.8953 | 0.8693 | 0.0 | 0.0 | 0.0 | 0.1307 |
| 0.2196 | 5.0 | 11510 | 0.9983 | 0.8710 | 0.0020 | 0.0588 | 0.0010 | 0.1290 |
| 0.1855 | 6.0 | 13812 | 1.0662 | 0.8691 | 0.0060 | 0.0857 | 0.0031 | 0.1309 |
| 0.1631 | 7.0 | 16114 | 1.0883 | 0.8689 | 0.0059 | 0.0811 | 0.0031 | 0.1311 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.12.0+cu102
- Datasets 2.3.2
- Tokenizers 0.12.1
|
CAMeL-Lab/bert-base-arabic-camelbert-da-pos-msa | [
"pytorch",
"tf",
"bert",
"token-classification",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
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} | 27 | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- metrics:
- type: mean_reward
value: 374.00 +/- 214.89
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
```
# Download model and save it into the logs/ folder
python -m utils.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Mahmoud7 -f logs/
python enjoy.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python train.py --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m utils.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Mahmoud7
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 10000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
|
CAMeL-Lab/bert-base-arabic-camelbert-msa | [
"pytorch",
"tf",
"jax",
"bert",
"fill-mask",
"ar",
"arxiv:2103.06678",
"transformers",
"license:apache-2.0",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 2,967 | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: VanessaSchenkel/padrao-mbart-finetuned-opus_books
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# VanessaSchenkel/padrao-mbart-finetuned-opus_books
This model is a fine-tuned version of [Narrativa/mbart-large-50-finetuned-opus-en-pt-translation](https://huggingface.co/Narrativa/mbart-large-50-finetuned-opus-en-pt-translation) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8117
- Validation Loss: 1.8626
- Train Bleu: 25.9399
- Train Gen Len: 29.7018
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Bleu | Train Gen Len | Epoch |
|:----------:|:---------------:|:----------:|:-------------:|:-----:|
| 2.8117 | 1.8626 | 25.9399 | 29.7018 | 0 |
### Framework versions
- Transformers 4.21.1
- TensorFlow 2.8.2
- Datasets 2.4.0
- Tokenizers 0.12.1
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
model-index:
- name: beit-base-patch16-224-pt22k-finetuned-eurosat
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8585858585858586
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224-pt22k-finetuned-eurosat
This model is a fine-tuned version of [microsoft/beit-base-patch16-224-pt22k](https://huggingface.co/microsoft/beit-base-patch16-224-pt22k) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3045
- Accuracy: 0.8586
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.5181 | 0.7071 |
| 0.6727 | 2.0 | 14 | 0.4030 | 0.8182 |
| 0.3522 | 3.0 | 21 | 0.3045 | 0.8586 |
### Framework versions
- Transformers 4.17.0
- Pytorch 1.11.0+cpu
- Datasets 2.2.0
- Tokenizers 0.12.1
|
CLTL/icf-levels-att | [
"pytorch",
"roberta",
"text-classification",
"nl",
"transformers",
"license:mit"
] | text-classification | {
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"RobertaForSequenceClassification"
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} | 32 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ft1500_norm500_aug5
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-ft1500_norm500_aug5
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8927
- Mse: 2.9755
- Mae: 1.0176
- R2: 0.4184
- Accuracy: 0.5003
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mse | Mae | R2 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:--------:|
| 0.4176 | 1.0 | 3952 | 1.0499 | 3.4996 | 1.0853 | 0.3160 | 0.4593 |
| 0.3196 | 2.0 | 7904 | 0.8670 | 2.8901 | 1.0503 | 0.4351 | 0.4600 |
| 0.2084 | 3.0 | 11856 | 0.8927 | 2.9755 | 1.0176 | 0.4184 | 0.5003 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
CM-CA/DialoGPT-small-cartman | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-large-xlsr-korean-demo-no-LM
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-korean-demo-no-LM
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3215
- Wer: 0.2209
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 4.7322 | 1.08 | 400 | 3.1660 | 1.0 |
| 1.6742 | 2.16 | 800 | 0.5714 | 0.5945 |
| 0.6009 | 3.23 | 1200 | 0.3934 | 0.4298 |
| 0.4335 | 4.31 | 1600 | 0.3855 | 0.4100 |
| 0.3615 | 5.39 | 2000 | 0.3226 | 0.3525 |
| 0.2975 | 6.47 | 2400 | 0.3079 | 0.3176 |
| 0.2822 | 7.55 | 2800 | 0.3226 | 0.3091 |
| 0.2468 | 8.63 | 3200 | 0.2935 | 0.2907 |
| 0.2307 | 9.7 | 3600 | 0.2826 | 0.2728 |
| 0.2035 | 10.78 | 4000 | 0.2876 | 0.2728 |
| 0.1959 | 11.86 | 4400 | 0.2988 | 0.2667 |
| 0.1714 | 12.94 | 4800 | 0.3176 | 0.2751 |
| 0.1728 | 14.02 | 5200 | 0.2889 | 0.2649 |
| 0.1552 | 15.09 | 5600 | 0.2893 | 0.2490 |
| 0.144 | 16.17 | 6000 | 0.2909 | 0.2548 |
| 0.1402 | 17.25 | 6400 | 0.2999 | 0.2494 |
| 0.1297 | 18.33 | 6800 | 0.3704 | 0.2584 |
| 0.1268 | 19.41 | 7200 | 0.3464 | 0.2497 |
| 0.1162 | 20.49 | 7600 | 0.3620 | 0.2461 |
| 0.1117 | 21.56 | 8000 | 0.2935 | 0.2387 |
| 0.1081 | 22.64 | 8400 | 0.3588 | 0.2427 |
| 0.0984 | 23.72 | 8800 | 0.4317 | 0.2507 |
| 0.0996 | 24.8 | 9200 | 0.3023 | 0.2277 |
| 0.0925 | 25.88 | 9600 | 0.3224 | 0.2292 |
| 0.0923 | 26.95 | 10000 | 0.3009 | 0.2243 |
| 0.0839 | 28.03 | 10400 | 0.3118 | 0.2219 |
| 0.0814 | 29.11 | 10800 | 0.3215 | 0.2209 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cameron/BERT-Jigsaw | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 35 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6369 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Cameron/BERT-SBIC-offensive | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 31 | null | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, cls pooling.
sentence_embeddings = cls_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 4643 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 1000,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'transformers.optimization.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Cameron/BERT-jigsaw-identityhate | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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} | 37 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-base-qa
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-qa
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: 1.0
### Training results
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
Cameron/BERT-mdgender-convai-binary | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
],
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} | 33 | null | ---
license: apache-2.0
tags:
- text-classification
widget:
- text: "This love has taken its toll on me"
example_title: "sadness"
---
# EMO demo 00
## TODO
### incorporate with EMO_AI
### put pretrained weight here |
Cameron/BERT-mdgender-wizard | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
"architectures": [
"BertForSequenceClassification"
],
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} | 30 | null | ---
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
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
- name: F1
type: f1
value: 0.9225903813139017
---
<!-- 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: 0.2064
- Accuracy: 0.922
- F1: 0.9226
## 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: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 125 | 0.2322 | 0.916 | 0.9164 |
| 0.2717 | 2.0 | 250 | 0.2064 | 0.922 | 0.9226 |
### Framework versions
- Transformers 4.22.0.dev0
- Pytorch 1.11.0+cu102
- Datasets 1.2.1
- Tokenizers 0.12.1
|
Cameron/BERT-rtgender-opgender-annotations | [
"pytorch",
"jax",
"bert",
"text-classification",
"transformers"
] | text-classification | {
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"BertForSequenceClassification"
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} | 33 | null | ---
datasets:
- relbert/conceptnet_high_confidence
model-index:
- name: relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
results:
- task:
name: Relation Mapping
type: sorting-task
dataset:
name: Relation Mapping
args: relbert/relation_mapping
type: relation-mapping
metrics:
- name: Accuracy
type: accuracy
value: 0.8786507936507937
- task:
name: Analogy Questions (SAT full)
type: multiple-choice-qa
dataset:
name: SAT full
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.4919786096256685
- task:
name: Analogy Questions (SAT)
type: multiple-choice-qa
dataset:
name: SAT
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.49554896142433236
- task:
name: Analogy Questions (BATS)
type: multiple-choice-qa
dataset:
name: BATS
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.7937743190661478
- task:
name: Analogy Questions (Google)
type: multiple-choice-qa
dataset:
name: Google
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.918
- task:
name: Analogy Questions (U2)
type: multiple-choice-qa
dataset:
name: U2
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6271929824561403
- task:
name: Analogy Questions (U4)
type: multiple-choice-qa
dataset:
name: U4
args: relbert/analogy_questions
type: analogy-questions
metrics:
- name: Accuracy
type: accuracy
value: 0.6527777777777778
- task:
name: Lexical Relation Classification (BLESS)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9215006780171764
- name: F1 (macro)
type: f1_macro
value: 0.9174763167950964
- task:
name: Lexical Relation Classification (CogALexV)
type: classification
dataset:
name: CogALexV
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.8678403755868545
- name: F1 (macro)
type: f1_macro
value: 0.7086241190414728
- task:
name: Lexical Relation Classification (EVALution)
type: classification
dataset:
name: BLESS
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.6825568797399784
- name: F1 (macro)
type: f1_macro
value: 0.6689609208642026
- task:
name: Lexical Relation Classification (K&H+N)
type: classification
dataset:
name: K&H+N
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.962092230646171
- name: F1 (macro)
type: f1_macro
value: 0.8907595805779478
- task:
name: Lexical Relation Classification (ROOT09)
type: classification
dataset:
name: ROOT09
args: relbert/lexical_relation_classification
type: relation-classification
metrics:
- name: F1
type: f1
value: 0.9025383892196804
- name: F1 (macro)
type: f1_macro
value: 0.900780083743733
---
# relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce
RelBERT fine-tuned from [roberta-large](https://huggingface.co/roberta-large) on
[relbert/conceptnet_high_confidence](https://huggingface.co/datasets/relbert/conceptnet_high_confidence).
Fine-tuning is done via [RelBERT](https://github.com/asahi417/relbert) library (see the repository for more detail).
It achieves the following results on the relation understanding tasks:
- Analogy Question ([dataset](https://huggingface.co/datasets/relbert/analogy_questions), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/analogy.json)):
- Accuracy on SAT (full): 0.4919786096256685
- Accuracy on SAT: 0.49554896142433236
- Accuracy on BATS: 0.7937743190661478
- Accuracy on U2: 0.6271929824561403
- Accuracy on U4: 0.6527777777777778
- Accuracy on Google: 0.918
- Lexical Relation Classification ([dataset](https://huggingface.co/datasets/relbert/lexical_relation_classification), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/classification.json)):
- Micro F1 score on BLESS: 0.9215006780171764
- Micro F1 score on CogALexV: 0.8678403755868545
- Micro F1 score on EVALution: 0.6825568797399784
- Micro F1 score on K&H+N: 0.962092230646171
- Micro F1 score on ROOT09: 0.9025383892196804
- Relation Mapping ([dataset](https://huggingface.co/datasets/relbert/relation_mapping), [full result](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/relation_mapping.json)):
- Accuracy on Relation Mapping: 0.8786507936507937
### Usage
This model can be used through the [relbert library](https://github.com/asahi417/relbert). Install the library via pip
```shell
pip install relbert
```
and activate model as below.
```python
from relbert import RelBERT
model = RelBERT("relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce")
vector = model.get_embedding(['Tokyo', 'Japan']) # shape of (1024, )
```
### Training hyperparameters
The following hyperparameters were used during training:
- model: roberta-large
- max_length: 64
- mode: average_no_mask
- data: relbert/conceptnet_high_confidence
- template_mode: manual
- template: Today, I finally discovered the relation between <subj> and <obj> : <mask>
- loss_function: nce_logout
- temperature_nce_constant: 0.05
- temperature_nce_rank: {'min': 0.01, 'max': 0.05, 'type': 'linear'}
- epoch: 196
- batch: 128
- lr: 5e-06
- lr_decay: False
- lr_warmup: 1
- weight_decay: 0
- random_seed: 0
- exclude_relation: None
- n_sample: 640
- gradient_accumulation: 8
The full configuration can be found at [fine-tuning parameter file](https://huggingface.co/relbert/roberta-large-conceptnet-average-no-mask-prompt-c-nce/raw/main/trainer_config.json).
### Reference
If you use any resource from RelBERT, please consider to cite our [paper](https://aclanthology.org/2021.eacl-demos.7/).
```
@inproceedings{ushio-etal-2021-distilling-relation-embeddings,
title = "{D}istilling {R}elation {E}mbeddings from {P}re-trained {L}anguage {M}odels",
author = "Ushio, Asahi and
Schockaert, Steven and
Camacho-Collados, Jose",
booktitle = "EMNLP 2021",
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
}
```
|
Camzure/MaamiBot-test | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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} | 9 | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- metrics:
- type: mean_reward
value: 278.68 +/- 24.01
name: mean_reward
task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-finetuned-my_dear_watson
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-my_dear_watson
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8264
## 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: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.7977 | 1.0 | 240 | 1.9607 |
| 2.0249 | 2.0 | 480 | 1.8608 |
| 1.9661 | 3.0 | 720 | 1.8150 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Capreolus/birch-bert-large-mb | [
"pytorch",
"tf",
"jax",
"bert",
"next-sentence-prediction",
"transformers"
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} | 1 | null | ---
tags:
- generated_from_trainer
datasets:
- samsum
model-index:
- name: pegasus-samsum
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# pegasus-samsum
This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Framework versions
- Transformers 4.11.3
- Pytorch 1.10.0
- Datasets 2.0.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- xsum
metrics:
- rouge
model-index:
- name: t5-small-finetuned-xsum
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: xsum
type: xsum
config: default
split: train
args: default
metrics:
- name: Rouge1
type: rouge
value: 28.3127
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4785
- Rouge1: 28.3127
- Rouge2: 7.7376
- Rougel: 22.2445
- Rougelsum: 22.2505
- Gen Len: 18.8304
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 2.7174 | 1.0 | 12753 | 2.4785 | 28.3127 | 7.7376 | 22.2445 | 22.2505 | 18.8304 |
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.0+cu113
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish-finetuned-pawsx | [
"pytorch",
"albert",
"text-classification",
"transformers"
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} | 25 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-uncased-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-ner
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0712
- Precision: 0.8945
- Recall: 0.9182
- F1: 0.9062
- Accuracy: 0.9793
## 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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: IPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- total_eval_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- training precision: Mixed Precision
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1318 | 1.0 | 219 | 0.0967 | 0.8371 | 0.8714 | 0.8539 | 0.9705 |
| 0.0597 | 2.0 | 438 | 0.0735 | 0.8912 | 0.9052 | 0.8981 | 0.9779 |
| 0.0523 | 3.0 | 657 | 0.0712 | 0.8945 | 0.9182 | 0.9062 | 0.9793 |
### Framework versions
- Transformers 4.20.0
- Pytorch 1.10.0+cpu
- Datasets 2.4.0
- Tokenizers 0.12.1
|
dccuchile/albert-large-spanish-finetuned-pos | [
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"albert",
"token-classification",
"transformers",
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} | 1 | null | ---
library_name: sklearn
tags:
- sklearn
- tabular-classification
- skops
widget:
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---
# Model description
[More Information Needed]
## Intended uses & limitations
[More Information Needed]
## Training Procedure
### Hyperparameters
The model is trained with below hyperparameters.
<details>
<summary> Click to expand </summary>
| Hyperparameter | Value |
|---------------------|----------|
| activation | relu |
| alpha | 0.0001 |
| batch_size | auto |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| early_stopping | False |
| epsilon | 1e-08 |
| hidden_layer_sizes | (100,) |
| learning_rate | constant |
| learning_rate_init | 0.001 |
| max_fun | 15000 |
| max_iter | 200 |
| momentum | 0.9 |
| n_iter_no_change | 10 |
| nesterovs_momentum | True |
| power_t | 0.5 |
| random_state | |
| shuffle | True |
| solver | adam |
| tol | 0.0001 |
| validation_fraction | 0.1 |
| verbose | False |
| warm_start | False |
</details>
### Model Plot
The model plot is below.
<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: "▸";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: "▾";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: "";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: "";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id="sk-container-id-1" class="sk-top-container"><div class="sk-text-repr-fallback"><pre>MLPClassifier()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class="sk-container" hidden><div class="sk-item"><div class="sk-estimator sk-toggleable"><input class="sk-toggleable__control sk-hidden--visually" id="sk-estimator-id-1" type="checkbox" checked><label for="sk-estimator-id-1" class="sk-toggleable__label sk-toggleable__label-arrow">MLPClassifier</label><div class="sk-toggleable__content"><pre>MLPClassifier()</pre></div></div></div></div></div>
# How to Get Started with the Model
Use the code below to get started with the model.
<details>
<summary> Click to expand </summary>
```python
[More Information Needed]
```
</details>
# Model Card Authors
This model card is written by following authors:
[More Information Needed]
# Model Card Contact
You can contact the model card authors through following channels:
[More Information Needed]
# Citation
Below you can find information related to citation.
**BibTeX:**
```
[More Information Needed]
``` |
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