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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
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| likes
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11.7k
| library_name
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pig4431/Sentiment140_BERT_5E
|
pig4431
| 2022-11-07T08:46:38Z | 10 | 1 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T08:39:06Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_BERT_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.82
---
<!-- 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. -->
# Sentiment140_BERT_5E
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7061
- Accuracy: 0.82
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6882 | 0.08 | 50 | 0.6047 | 0.7 |
| 0.6223 | 0.16 | 100 | 0.5137 | 0.8067 |
| 0.5463 | 0.24 | 150 | 0.4573 | 0.8067 |
| 0.4922 | 0.32 | 200 | 0.4790 | 0.8 |
| 0.4821 | 0.4 | 250 | 0.4207 | 0.8267 |
| 0.4985 | 0.48 | 300 | 0.4267 | 0.8067 |
| 0.4455 | 0.56 | 350 | 0.4301 | 0.8133 |
| 0.469 | 0.64 | 400 | 0.4294 | 0.82 |
| 0.4906 | 0.72 | 450 | 0.4059 | 0.8067 |
| 0.4006 | 0.8 | 500 | 0.4181 | 0.8133 |
| 0.445 | 0.88 | 550 | 0.3948 | 0.8267 |
| 0.4302 | 0.96 | 600 | 0.3976 | 0.84 |
| 0.4442 | 1.04 | 650 | 0.3887 | 0.8533 |
| 0.3424 | 1.12 | 700 | 0.4119 | 0.8267 |
| 0.3589 | 1.2 | 750 | 0.4083 | 0.8533 |
| 0.3737 | 1.28 | 800 | 0.4253 | 0.8333 |
| 0.334 | 1.36 | 850 | 0.4147 | 0.86 |
| 0.3637 | 1.44 | 900 | 0.3926 | 0.8533 |
| 0.3388 | 1.52 | 950 | 0.4084 | 0.8267 |
| 0.3375 | 1.6 | 1000 | 0.4132 | 0.8467 |
| 0.3725 | 1.68 | 1050 | 0.3965 | 0.8467 |
| 0.3649 | 1.76 | 1100 | 0.3956 | 0.8333 |
| 0.3799 | 1.84 | 1150 | 0.3923 | 0.8333 |
| 0.3695 | 1.92 | 1200 | 0.4266 | 0.84 |
| 0.3233 | 2.0 | 1250 | 0.4225 | 0.8333 |
| 0.2313 | 2.08 | 1300 | 0.4672 | 0.8333 |
| 0.231 | 2.16 | 1350 | 0.5212 | 0.8133 |
| 0.2526 | 2.24 | 1400 | 0.5392 | 0.8067 |
| 0.2721 | 2.32 | 1450 | 0.4895 | 0.82 |
| 0.2141 | 2.4 | 1500 | 0.5258 | 0.8133 |
| 0.2658 | 2.48 | 1550 | 0.5046 | 0.8267 |
| 0.2386 | 2.56 | 1600 | 0.4873 | 0.8267 |
| 0.2493 | 2.64 | 1650 | 0.4950 | 0.8333 |
| 0.2692 | 2.72 | 1700 | 0.5080 | 0.8267 |
| 0.2226 | 2.8 | 1750 | 0.5016 | 0.8467 |
| 0.2522 | 2.88 | 1800 | 0.5068 | 0.8267 |
| 0.2556 | 2.96 | 1850 | 0.4937 | 0.8267 |
| 0.2311 | 3.04 | 1900 | 0.5103 | 0.8267 |
| 0.1703 | 3.12 | 1950 | 0.5680 | 0.82 |
| 0.1744 | 3.2 | 2000 | 0.5501 | 0.82 |
| 0.1667 | 3.28 | 2050 | 0.6142 | 0.82 |
| 0.1863 | 3.36 | 2100 | 0.6355 | 0.82 |
| 0.2543 | 3.44 | 2150 | 0.6000 | 0.8133 |
| 0.1565 | 3.52 | 2200 | 0.6618 | 0.8267 |
| 0.1531 | 3.6 | 2250 | 0.6595 | 0.8133 |
| 0.1915 | 3.68 | 2300 | 0.6647 | 0.8267 |
| 0.1601 | 3.76 | 2350 | 0.6729 | 0.8267 |
| 0.176 | 3.84 | 2400 | 0.6699 | 0.82 |
| 0.1815 | 3.92 | 2450 | 0.6819 | 0.8067 |
| 0.1987 | 4.0 | 2500 | 0.6543 | 0.8333 |
| 0.1236 | 4.08 | 2550 | 0.6686 | 0.8333 |
| 0.1599 | 4.16 | 2600 | 0.6583 | 0.8267 |
| 0.1256 | 4.24 | 2650 | 0.6871 | 0.8267 |
| 0.1291 | 4.32 | 2700 | 0.6855 | 0.82 |
| 0.1198 | 4.4 | 2750 | 0.6901 | 0.82 |
| 0.1245 | 4.48 | 2800 | 0.7152 | 0.8267 |
| 0.1784 | 4.56 | 2850 | 0.7053 | 0.82 |
| 0.1705 | 4.64 | 2900 | 0.7016 | 0.82 |
| 0.1265 | 4.72 | 2950 | 0.7013 | 0.82 |
| 0.1192 | 4.8 | 3000 | 0.7084 | 0.82 |
| 0.174 | 4.88 | 3050 | 0.7062 | 0.82 |
| 0.1328 | 4.96 | 3100 | 0.7061 | 0.82 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
bofenghuang/wav2vec2-xls-r-1b-cv9-fr
|
bofenghuang
| 2022-11-07T08:37:59Z | 8 | 1 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_9_0",
"generated_from_trainer",
"hf-asr-leaderboard",
"robust-speech-event",
"fr",
"dataset:common_voice",
"dataset:mozilla-foundation/common_voice_9_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-09-12T13:09:54Z |
---
language:
- fr
license: apache-2.0
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_9_0
- generated_from_trainer
- hf-asr-leaderboard
- robust-speech-event
datasets:
- common_voice
- mozilla-foundation/common_voice_9_0
model-index:
- name: Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 9
type: mozilla-foundation/common_voice_9_0
args: fr
metrics:
- name: Test WER
type: wer
value: 12.72
- name: Test WER (+LM)
type: wer
value: 10.60
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Robust Speech Event - Dev Data
type: speech-recognition-community-v2/dev_data
args: fr
metrics:
- name: Test WER
type: wer
value: 24.28
- name: Test WER (+LM)
type: wer
value: 20.85
---
# Fine-tuned Wav2Vec2 XLS-R 1B model for ASR in French
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - FR dataset.
## Usage
1. To use on a local audio file without the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2Processor
processor = Wav2Vec2Processor.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr").cuda()
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
# decode
predicted_ids = torch.argmax(logits, dim=-1)
predicted_sentence = processor.batch_decode(predicted_ids)[0]
```
2. To use on a local audio file with the language model
```python
import torch
import torchaudio
from transformers import AutoModelForCTC, Wav2Vec2ProcessorWithLM
processor_with_lm = Wav2Vec2ProcessorWithLM.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr")
model = AutoModelForCTC.from_pretrained("bhuang/wav2vec2-xls-r-1b-cv9-fr").cuda()
model_sampling_rate = processor_with_lm.feature_extractor.sampling_rate
# path to your audio file
wav_path = "example.wav"
waveform, sample_rate = torchaudio.load(wav_path)
waveform = waveform.squeeze(axis=0) # mono
# resample
if sample_rate != 16_000:
resampler = torchaudio.transforms.Resample(sample_rate, 16_000)
waveform = resampler(waveform)
# normalize
input_dict = processor_with_lm(waveform, sampling_rate=16_000, return_tensors="pt")
with torch.inference_mode():
logits = model(input_dict.input_values.to("cuda")).logits
predicted_sentence = processor_with_lm.batch_decode(logits.cpu().numpy()).text[0]
```
## Evaluation
1. To evaluate on `mozilla-foundation/common_voice_9_0`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-cv9-fr" \
--dataset "mozilla-foundation/common_voice_9_0" \
--config "fr" \
--split "test" \
--log_outputs \
--outdir "outputs/results_mozilla-foundatio_common_voice_9_0_with_lm"
```
2. To evaluate on `speech-recognition-community-v2/dev_data`
```bash
python eval.py \
--model_id "bhuang/wav2vec2-xls-r-1b-cv9-fr" \
--dataset "speech-recognition-community-v2/dev_data" \
--config "fr" \
--split "validation" \
--chunk_length_s 5.0 \
--stride_length_s 1.0 \
--log_outputs \
--outdir "outputs/results_speech-recognition-community-v2_dev_data_with_lm"
```
|
ahmadRa/q-Taxi-v3-try1
|
ahmadRa
| 2022-11-07T08:18:35Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-07T08:18:29Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3-try1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **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="ahmadRa/q-Taxi-v3-try1", 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"])
```
|
cynthiachan/finetuned-bert-base
|
cynthiachan
| 2022-11-07T07:56:55Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:cynthiachan/FeedRef2022",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T06:51:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- cynthiachan/FeedRef2022
model-index:
- name: training
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. -->
# training
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the cynthiachan/FeedRef2022 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0514
- Attackid Precision: 0.8889
- Attackid Recall: 0.9231
- Attackid F1: 0.9057
- Attackid Number: 52
- Bitcoinaddr Precision: 0.875
- Bitcoinaddr Recall: 1.0
- Bitcoinaddr F1: 0.9333
- Bitcoinaddr Number: 7
- Cve Precision: 0.8378
- Cve Recall: 0.9538
- Cve F1: 0.8921
- Cve Number: 65
- Defenderthreat Precision: 0.875
- Defenderthreat Recall: 1.0
- Defenderthreat F1: 0.9333
- Defenderthreat Number: 7
- Domain Precision: 0.9279
- Domain Recall: 0.9369
- Domain F1: 0.9324
- Domain Number: 206
- Email Precision: 0.8333
- Email Recall: 0.9302
- Email F1: 0.8791
- Email Number: 43
- Filepath Precision: 0.8857
- Filepath Recall: 0.9195
- Filepath F1: 0.9023
- Filepath Number: 1652
- Fingerprint Precision: 0.0
- Fingerprint Recall: 0.0
- Fingerprint F1: 0.0
- Fingerprint Number: 2
- Hostname Precision: 0.8910
- Hostname Recall: 0.9653
- Hostname F1: 0.9267
- Hostname Number: 144
- Ipv4 Precision: 0.9767
- Ipv4 Recall: 0.9825
- Ipv4 F1: 0.9796
- Ipv4 Number: 171
- Ipv6 Precision: 0.3333
- Ipv6 Recall: 1.0
- Ipv6 F1: 0.5
- Ipv6 Number: 3
- Md5 Precision: 0.9141
- Md5 Recall: 0.9857
- Md5 F1: 0.9486
- Md5 Number: 421
- Sha1 Precision: 0.8545
- Sha1 Recall: 0.9592
- Sha1 F1: 0.9038
- Sha1 Number: 49
- Sha256 Precision: 0.9120
- Sha256 Recall: 0.9919
- Sha256 F1: 0.9502
- Sha256 Number: 491
- Uri Precision: 0.3333
- Uri Recall: 0.4545
- Uri F1: 0.3846
- Uri Number: 11
- Overall Precision: 0.8946
- Overall Recall: 0.9446
- Overall F1: 0.9189
- Overall Accuracy: 0.9886
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Attackid Precision | Attackid Recall | Attackid F1 | Attackid Number | Bitcoinaddr Precision | Bitcoinaddr Recall | Bitcoinaddr F1 | Bitcoinaddr Number | Cve Precision | Cve Recall | Cve F1 | Cve Number | Defenderthreat Precision | Defenderthreat Recall | Defenderthreat F1 | Defenderthreat Number | Domain Precision | Domain Recall | Domain F1 | Domain Number | Email Precision | Email Recall | Email F1 | Email Number | Filepath Precision | Filepath Recall | Filepath F1 | Filepath Number | Fingerprint Precision | Fingerprint Recall | Fingerprint F1 | Fingerprint Number | Hostname Precision | Hostname Recall | Hostname F1 | Hostname Number | Ipv4 Precision | Ipv4 Recall | Ipv4 F1 | Ipv4 Number | Ipv6 Precision | Ipv6 Recall | Ipv6 F1 | Ipv6 Number | Md5 Precision | Md5 Recall | Md5 F1 | Md5 Number | Sha1 Precision | Sha1 Recall | Sha1 F1 | Sha1 Number | Sha256 Precision | Sha256 Recall | Sha256 F1 | Sha256 Number | Uri Precision | Uri Recall | Uri F1 | Uri Number | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:-------------:|:----------:|:------:|:----------:|:------------------------:|:---------------------:|:-----------------:|:---------------------:|:----------------:|:-------------:|:---------:|:-------------:|:---------------:|:------------:|:--------:|:------------:|:------------------:|:---------------:|:-----------:|:---------------:|:---------------------:|:------------------:|:--------------:|:------------------:|:------------------:|:---------------:|:-----------:|:---------------:|:--------------:|:-----------:|:-------:|:-----------:|:--------------:|:-----------:|:-------:|:-----------:|:-------------:|:----------:|:------:|:----------:|:--------------:|:-----------:|:-------:|:-----------:|:----------------:|:-------------:|:---------:|:-------------:|:-------------:|:----------:|:------:|:----------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.3691 | 0.04 | 500 | 0.3054 | 0.0 | 0.0 | 0.0 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 206 | 0.0 | 0.0 | 0.0 | 43 | 0.1917 | 0.5975 | 0.2903 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.0 | 0.0 | 0.0 | 144 | 0.5747 | 0.5848 | 0.5797 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.4160 | 0.7648 | 0.5389 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.5131 | 0.9145 | 0.6574 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.2665 | 0.5590 | 0.3610 | 0.9297 |
| 0.2388 | 0.07 | 1000 | 0.2124 | 0.0 | 0.0 | 0.0 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7467 | 0.8615 | 0.8 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.0 | 0.0 | 0.0 | 206 | 0.0 | 0.0 | 0.0 | 43 | 0.3846 | 0.4661 | 0.4215 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.3534 | 0.6528 | 0.4585 | 144 | 0.6667 | 0.5614 | 0.6095 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5275 | 0.9097 | 0.6678 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8787 | 0.9002 | 0.8893 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.4932 | 0.5539 | 0.5218 | 0.9491 |
| 0.1817 | 0.11 | 1500 | 0.2025 | 0.4433 | 0.8269 | 0.5772 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7941 | 0.8308 | 0.8120 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.2241 | 0.6602 | 0.3346 | 206 | 0.1538 | 0.2326 | 0.1852 | 43 | 0.4561 | 0.6816 | 0.5465 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.0042 | 0.0069 | 0.0052 | 144 | 0.6522 | 0.7018 | 0.6761 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5671 | 0.8527 | 0.6812 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7623 | 0.9470 | 0.8447 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.4654 | 0.6961 | 0.5579 | 0.9563 |
| 0.1552 | 0.15 | 2000 | 0.1581 | 0.6119 | 0.7885 | 0.6891 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8235 | 0.8615 | 0.8421 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.4979 | 0.5680 | 0.5306 | 206 | 0.4795 | 0.8140 | 0.6034 | 43 | 0.4876 | 0.7960 | 0.6047 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5682 | 0.6944 | 0.625 | 144 | 0.4692 | 0.8012 | 0.5918 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5321 | 0.9240 | 0.6753 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7951 | 0.9328 | 0.8585 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.5345 | 0.7966 | 0.6398 | 0.9622 |
| 0.1567 | 0.19 | 2500 | 0.1619 | 0.6032 | 0.7308 | 0.6609 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8133 | 0.9385 | 0.8714 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6257 | 0.5680 | 0.5954 | 206 | 0.1379 | 0.1860 | 0.1584 | 43 | 0.5788 | 0.7512 | 0.6538 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.4981 | 0.9097 | 0.6437 | 144 | 0.7233 | 0.8713 | 0.7905 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7723 | 0.9264 | 0.8423 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7523 | 0.9837 | 0.8526 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6308 | 0.7876 | 0.7006 | 0.9628 |
| 0.1588 | 0.22 | 3000 | 0.1409 | 0.4050 | 0.9423 | 0.5665 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.5962 | 0.9538 | 0.7337 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6805 | 0.7961 | 0.7338 | 206 | 0.5821 | 0.9070 | 0.7091 | 43 | 0.6291 | 0.7712 | 0.6930 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6902 | 0.8819 | 0.7744 | 144 | 0.5737 | 0.8421 | 0.6825 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.5678 | 0.9454 | 0.7094 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8582 | 0.9735 | 0.9122 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6300 | 0.8228 | 0.7136 | 0.9664 |
| 0.1257 | 0.26 | 3500 | 0.1417 | 0.5541 | 0.7885 | 0.6508 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6854 | 0.9385 | 0.7922 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6828 | 0.7524 | 0.7159 | 206 | 0.5217 | 0.8372 | 0.6429 | 43 | 0.6314 | 0.7155 | 0.6708 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5261 | 0.9097 | 0.6667 | 144 | 0.7562 | 0.8889 | 0.8172 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7435 | 0.9501 | 0.8342 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.7325 | 0.9817 | 0.8390 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6627 | 0.7942 | 0.7225 | 0.9658 |
| 0.1229 | 0.3 | 4000 | 0.1455 | 0.6567 | 0.8462 | 0.7395 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7391 | 0.7846 | 0.7612 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6858 | 0.7524 | 0.7176 | 206 | 0.4321 | 0.8140 | 0.5645 | 43 | 0.6740 | 0.7809 | 0.7235 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6452 | 0.8333 | 0.7273 | 144 | 0.5455 | 0.5614 | 0.5533 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7697 | 0.8575 | 0.8112 | 421 | 0.3645 | 0.7959 | 0.5 | 49 | 0.6948 | 0.9735 | 0.8109 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6684 | 0.8029 | 0.7295 | 0.9667 |
| 0.1323 | 0.34 | 4500 | 0.1323 | 0.6719 | 0.8269 | 0.7414 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7910 | 0.8154 | 0.8030 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6064 | 0.7330 | 0.6637 | 206 | 0.74 | 0.8605 | 0.7957 | 43 | 0.6802 | 0.7391 | 0.7084 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.5935 | 0.5069 | 0.5468 | 144 | 0.7826 | 0.7368 | 0.7590 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7783 | 0.8171 | 0.7972 | 421 | 0.3810 | 0.8163 | 0.5195 | 49 | 0.8368 | 0.9715 | 0.8992 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7049 | 0.7717 | 0.7368 | 0.9680 |
| 0.1379 | 0.37 | 5000 | 0.1088 | 0.5930 | 0.9808 | 0.7391 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.725 | 0.8923 | 0.8 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7619 | 0.6990 | 0.7291 | 206 | 0.5556 | 0.9302 | 0.6957 | 43 | 0.6551 | 0.8360 | 0.7346 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7127 | 0.8958 | 0.7938 | 144 | 0.7989 | 0.8596 | 0.8282 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7665 | 0.9359 | 0.8428 | 421 | 0.3729 | 0.4490 | 0.4074 | 49 | 0.7278 | 0.9695 | 0.8314 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6886 | 0.8550 | 0.7629 | 0.9738 |
| 0.1162 | 0.41 | 5500 | 0.1205 | 0.5765 | 0.9423 | 0.7153 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8026 | 0.9385 | 0.8652 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7960 | 0.7767 | 0.7862 | 206 | 0.6032 | 0.8837 | 0.7170 | 43 | 0.6724 | 0.8099 | 0.7348 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6791 | 0.8819 | 0.7674 | 144 | 0.8041 | 0.9123 | 0.8548 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7188 | 0.9287 | 0.8104 | 421 | 0.5714 | 0.8163 | 0.6723 | 49 | 0.8088 | 0.9735 | 0.8835 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7033 | 0.8538 | 0.7713 | 0.9711 |
| 0.1128 | 0.45 | 6000 | 0.1165 | 0.6575 | 0.9231 | 0.768 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7143 | 0.9231 | 0.8054 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7703 | 0.7816 | 0.7759 | 206 | 0.6724 | 0.9070 | 0.7723 | 43 | 0.6634 | 0.7706 | 0.7130 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6580 | 0.8819 | 0.7537 | 144 | 0.8434 | 0.8187 | 0.8309 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8032 | 0.9596 | 0.8745 | 421 | 0.6066 | 0.7551 | 0.6727 | 49 | 0.8554 | 0.9756 | 0.9115 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7201 | 0.8327 | 0.7723 | 0.9736 |
| 0.11 | 0.49 | 6500 | 0.1374 | 0.7167 | 0.8269 | 0.7679 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7273 | 0.8615 | 0.7887 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7592 | 0.7039 | 0.7305 | 206 | 0.725 | 0.6744 | 0.6988 | 43 | 0.6129 | 0.7524 | 0.6755 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7151 | 0.8542 | 0.7785 | 144 | 0.7919 | 0.8012 | 0.7965 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7821 | 0.9549 | 0.8599 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.6880 | 0.9837 | 0.8097 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.6710 | 0.8005 | 0.7300 | 0.9680 |
| 0.1152 | 0.52 | 7000 | 0.1152 | 0.6933 | 1.0 | 0.8189 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6374 | 0.8923 | 0.7436 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6103 | 0.6311 | 0.6205 | 206 | 0.6739 | 0.7209 | 0.6966 | 43 | 0.6969 | 0.7960 | 0.7431 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7721 | 0.7292 | 0.75 | 144 | 0.8526 | 0.7778 | 0.8135 | 171 | 0.0192 | 0.3333 | 0.0364 | 3 | 0.8549 | 0.9097 | 0.8815 | 421 | 0.4706 | 0.8163 | 0.5970 | 49 | 0.8625 | 0.9837 | 0.9191 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7271 | 0.8216 | 0.7715 | 0.9722 |
| 0.1084 | 0.56 | 7500 | 0.1073 | 0.75 | 0.8077 | 0.7778 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6484 | 0.9077 | 0.7564 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7313 | 0.8058 | 0.7667 | 206 | 0.6452 | 0.9302 | 0.7619 | 43 | 0.6933 | 0.8196 | 0.7512 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6818 | 0.9375 | 0.7895 | 144 | 0.6872 | 0.9123 | 0.7839 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8789 | 0.9477 | 0.9120 | 421 | 0.7451 | 0.7755 | 0.76 | 49 | 0.8374 | 0.9857 | 0.9055 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7277 | 0.8643 | 0.7902 | 0.9741 |
| 0.0789 | 0.6 | 8000 | 0.0958 | 0.7719 | 0.8462 | 0.8073 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7403 | 0.8769 | 0.8028 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7731 | 0.8107 | 0.7915 | 206 | 0.74 | 0.8605 | 0.7957 | 43 | 0.7408 | 0.7924 | 0.7657 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6749 | 0.9514 | 0.7896 | 144 | 0.8011 | 0.8480 | 0.8239 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8299 | 0.9620 | 0.8911 | 421 | 0.5686 | 0.5918 | 0.58 | 49 | 0.8770 | 0.9878 | 0.9291 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7700 | 0.8469 | 0.8066 | 0.9760 |
| 0.1149 | 0.64 | 8500 | 0.1334 | 1.0 | 0.7692 | 0.8696 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6795 | 0.8154 | 0.7413 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7336 | 0.7621 | 0.7476 | 206 | 0.3824 | 0.6047 | 0.4685 | 43 | 0.6318 | 0.5454 | 0.5854 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8227 | 0.8056 | 0.8140 | 144 | 0.7707 | 0.7076 | 0.7378 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8776 | 0.9026 | 0.8899 | 421 | 0.6129 | 0.7755 | 0.6847 | 49 | 0.8339 | 0.9817 | 0.9018 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7231 | 0.6961 | 0.7094 | 0.9673 |
| 0.1155 | 0.67 | 9000 | 0.1052 | 0.6267 | 0.9038 | 0.7402 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7294 | 0.9538 | 0.8267 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7232 | 0.7864 | 0.7535 | 206 | 0.7391 | 0.7907 | 0.7640 | 43 | 0.7494 | 0.7312 | 0.7402 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7531 | 0.8472 | 0.7974 | 144 | 0.8708 | 0.9064 | 0.8883 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8340 | 0.9667 | 0.8955 | 421 | 0.5714 | 0.5714 | 0.5714 | 49 | 0.8709 | 0.9756 | 0.9203 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7664 | 0.8135 | 0.7893 | 0.9742 |
| 0.0926 | 0.71 | 9500 | 0.1048 | 0.6438 | 0.9038 | 0.752 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6610 | 0.6 | 0.6290 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7541 | 0.6699 | 0.7095 | 206 | 0.7308 | 0.8837 | 0.8 | 43 | 0.6768 | 0.8456 | 0.7519 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7119 | 0.875 | 0.7850 | 144 | 0.8343 | 0.8830 | 0.8580 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8712 | 0.9477 | 0.9078 | 421 | 0.7193 | 0.8367 | 0.7736 | 49 | 0.8476 | 0.9857 | 0.9115 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7322 | 0.8604 | 0.7911 | 0.9760 |
| 0.0982 | 0.75 | 10000 | 0.0985 | 0.6533 | 0.9423 | 0.7717 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7027 | 0.8 | 0.7482 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7671 | 0.8155 | 0.7906 | 206 | 0.7143 | 0.9302 | 0.8081 | 43 | 0.7465 | 0.8039 | 0.7741 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6507 | 0.9444 | 0.7705 | 144 | 0.9106 | 0.9532 | 0.9314 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8008 | 0.9264 | 0.8590 | 421 | 0.5641 | 0.8980 | 0.6929 | 49 | 0.8460 | 0.9735 | 0.9053 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7633 | 0.8568 | 0.8074 | 0.9769 |
| 0.085 | 0.79 | 10500 | 0.0972 | 0.6184 | 0.9038 | 0.7344 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8154 | 0.8154 | 0.8154 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7236 | 0.8641 | 0.7876 | 206 | 0.7755 | 0.8837 | 0.8261 | 43 | 0.7544 | 0.8105 | 0.7814 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7081 | 0.9097 | 0.7964 | 144 | 0.8778 | 0.9240 | 0.9003 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8976 | 0.9572 | 0.9264 | 421 | 0.8039 | 0.8367 | 0.8200 | 49 | 0.8432 | 0.9857 | 0.9089 | 491 | 0.1111 | 0.0909 | 0.1000 | 11 | 0.7852 | 0.8643 | 0.8229 | 0.9779 |
| 0.0981 | 0.82 | 11000 | 0.1092 | 0.6944 | 0.9615 | 0.8065 | 52 | 0.2 | 0.1429 | 0.1667 | 7 | 0.7262 | 0.9385 | 0.8188 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6842 | 0.8835 | 0.7712 | 206 | 0.6667 | 0.7907 | 0.7234 | 43 | 0.7117 | 0.8251 | 0.7642 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7159 | 0.875 | 0.7875 | 144 | 0.9337 | 0.9064 | 0.9199 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7175 | 0.9715 | 0.8254 | 421 | 0.0 | 0.0 | 0.0 | 49 | 0.8620 | 0.9796 | 0.9171 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7399 | 0.8610 | 0.7959 | 0.9737 |
| 0.0892 | 0.86 | 11500 | 0.0969 | 0.6049 | 0.9423 | 0.7368 | 52 | 0.4545 | 0.7143 | 0.5556 | 7 | 0.0 | 0.0 | 0.0 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8 | 0.8155 | 0.8077 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.6975 | 0.8571 | 0.7691 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7397 | 0.75 | 0.7448 | 144 | 0.8841 | 0.8480 | 0.8657 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8821 | 0.9596 | 0.9192 | 421 | 0.9474 | 0.7347 | 0.8276 | 49 | 0.8251 | 0.9511 | 0.8836 | 491 | 0.25 | 0.1818 | 0.2105 | 11 | 0.7557 | 0.8544 | 0.8020 | 0.9759 |
| 0.0924 | 0.9 | 12000 | 0.0971 | 0.7059 | 0.9231 | 0.8000 | 52 | 0.4615 | 0.8571 | 0.6 | 7 | 0.8108 | 0.9231 | 0.8633 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7331 | 0.8932 | 0.8053 | 206 | 0.8 | 0.9302 | 0.8602 | 43 | 0.7544 | 0.8535 | 0.8009 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7697 | 0.8819 | 0.8220 | 144 | 0.8947 | 0.8947 | 0.8947 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7758 | 0.9454 | 0.8522 | 421 | 0.4516 | 0.8571 | 0.5915 | 49 | 0.8618 | 0.9776 | 0.9160 | 491 | 0.08 | 0.1818 | 0.1111 | 11 | 0.7664 | 0.8875 | 0.8225 | 0.9782 |
| 0.0784 | 0.94 | 12500 | 0.1113 | 0.6623 | 0.9808 | 0.7907 | 52 | 0.6667 | 0.8571 | 0.75 | 7 | 0.8406 | 0.8923 | 0.8657 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.6865 | 0.8398 | 0.7555 | 206 | 0.7547 | 0.9302 | 0.8333 | 43 | 0.7858 | 0.7863 | 0.7861 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8026 | 0.8472 | 0.8243 | 144 | 0.8629 | 0.8830 | 0.8728 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8462 | 0.9406 | 0.8909 | 421 | 0.56 | 0.8571 | 0.6774 | 49 | 0.9119 | 0.9695 | 0.9398 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.8022 | 0.8466 | 0.8238 | 0.9774 |
| 0.1063 | 0.97 | 13000 | 0.0932 | 0.6538 | 0.9808 | 0.7846 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7838 | 0.8923 | 0.8345 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7738 | 0.8301 | 0.8009 | 206 | 0.75 | 0.8372 | 0.7912 | 43 | 0.6979 | 0.8529 | 0.7676 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7086 | 0.8611 | 0.7774 | 144 | 0.8703 | 0.9415 | 0.9045 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.6184 | 0.8931 | 0.7308 | 421 | 0.2424 | 0.1633 | 0.1951 | 49 | 0.8511 | 0.9776 | 0.9100 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7176 | 0.8646 | 0.7843 | 0.9760 |
| 0.0765 | 1.01 | 13500 | 0.0892 | 0.6806 | 0.9423 | 0.7903 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6714 | 0.7231 | 0.6963 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8416 | 0.8252 | 0.8333 | 206 | 0.7917 | 0.8837 | 0.8352 | 43 | 0.7330 | 0.8559 | 0.7897 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7105 | 0.9375 | 0.8084 | 144 | 0.8757 | 0.9474 | 0.9101 | 171 | 0.125 | 1.0 | 0.2222 | 3 | 0.8769 | 0.9810 | 0.9260 | 421 | 0.5970 | 0.8163 | 0.6897 | 49 | 0.8761 | 0.9796 | 0.925 | 491 | 0.0 | 0.0 | 0.0 | 11 | 0.7696 | 0.8881 | 0.8246 | 0.9790 |
| 0.0677 | 1.05 | 14000 | 0.0804 | 0.6667 | 0.9231 | 0.7742 | 52 | 0.3333 | 0.7143 | 0.4545 | 7 | 0.7941 | 0.8308 | 0.8120 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8112 | 0.7718 | 0.7910 | 206 | 0.7234 | 0.7907 | 0.7556 | 43 | 0.7725 | 0.8487 | 0.8088 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7949 | 0.8611 | 0.8267 | 144 | 0.9401 | 0.9181 | 0.9290 | 171 | 0.1765 | 1.0 | 0.3 | 3 | 0.8613 | 0.9739 | 0.9142 | 421 | 0.4868 | 0.7551 | 0.592 | 49 | 0.8881 | 0.9857 | 0.9344 | 491 | 0.2222 | 0.1818 | 0.2000 | 11 | 0.7978 | 0.8782 | 0.8360 | 0.9805 |
| 0.0544 | 1.09 | 14500 | 0.0924 | 0.9216 | 0.9038 | 0.9126 | 52 | 0.1875 | 0.4286 | 0.2609 | 7 | 0.7973 | 0.9077 | 0.8489 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.7511 | 0.8641 | 0.8036 | 206 | 0.78 | 0.9070 | 0.8387 | 43 | 0.7361 | 0.8747 | 0.7994 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.6569 | 0.9306 | 0.7701 | 144 | 0.9253 | 0.9415 | 0.9333 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9146 | 0.9667 | 0.9400 | 421 | 0.6308 | 0.8367 | 0.7193 | 49 | 0.8121 | 0.9857 | 0.8905 | 491 | 0.0833 | 0.1818 | 0.1143 | 11 | 0.7679 | 0.9025 | 0.8298 | 0.9793 |
| 0.0797 | 1.12 | 15000 | 0.0851 | 0.9057 | 0.9231 | 0.9143 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7294 | 0.9538 | 0.8267 | 65 | 0.5 | 0.5714 | 0.5333 | 7 | 0.7909 | 0.8447 | 0.8169 | 206 | 0.8125 | 0.9070 | 0.8571 | 43 | 0.8104 | 0.8432 | 0.8265 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7 | 0.8264 | 0.7580 | 144 | 0.8804 | 0.9474 | 0.9127 | 171 | 0.2222 | 0.6667 | 0.3333 | 3 | 0.8834 | 0.9359 | 0.9089 | 421 | 0.5056 | 0.9184 | 0.6522 | 49 | 0.8436 | 0.9776 | 0.9057 | 491 | 0.0625 | 0.0909 | 0.0741 | 11 | 0.8077 | 0.8794 | 0.8420 | 0.9793 |
| 0.0544 | 1.16 | 15500 | 0.0905 | 0.7 | 0.9423 | 0.8033 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6421 | 0.9385 | 0.7625 | 65 | 0.25 | 0.2857 | 0.2667 | 7 | 0.8018 | 0.8447 | 0.8227 | 206 | 0.7273 | 0.9302 | 0.8163 | 43 | 0.7642 | 0.8571 | 0.8080 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8098 | 0.9167 | 0.8599 | 144 | 0.9261 | 0.9532 | 0.9395 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.6976 | 0.9810 | 0.8154 | 421 | 0.6066 | 0.7551 | 0.6727 | 49 | 0.8948 | 0.9878 | 0.9390 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.7664 | 0.8953 | 0.8259 | 0.9793 |
| 0.0815 | 1.2 | 16000 | 0.0799 | 0.9804 | 0.9615 | 0.9709 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.6593 | 0.9231 | 0.7692 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.825 | 0.8010 | 0.8128 | 206 | 0.6667 | 0.9302 | 0.7767 | 43 | 0.7140 | 0.8523 | 0.7770 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7016 | 0.9306 | 0.8000 | 144 | 0.9096 | 0.9415 | 0.9253 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.7203 | 0.9359 | 0.8140 | 421 | 0.3193 | 0.7755 | 0.4524 | 49 | 0.8548 | 0.9470 | 0.8986 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.7339 | 0.8794 | 0.8001 | 0.9780 |
| 0.0647 | 1.24 | 16500 | 0.0739 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7692 | 0.9231 | 0.8392 | 65 | 0.0 | 0.0 | 0.0 | 7 | 0.8077 | 0.8155 | 0.8116 | 206 | 0.8 | 0.9302 | 0.8602 | 43 | 0.7750 | 0.8717 | 0.8205 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8467 | 0.8819 | 0.8639 | 144 | 0.92 | 0.9415 | 0.9306 | 171 | 0.0682 | 1.0 | 0.1277 | 3 | 0.8515 | 0.9810 | 0.9117 | 421 | 0.9318 | 0.8367 | 0.8817 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.1875 | 0.2727 | 0.2222 | 11 | 0.8066 | 0.8998 | 0.8507 | 0.9820 |
| 0.0532 | 1.27 | 17000 | 0.0870 | 0.8491 | 0.8654 | 0.8571 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.8657 | 0.8923 | 0.8788 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.7404 | 0.8447 | 0.7891 | 206 | 0.8163 | 0.9302 | 0.8696 | 43 | 0.8296 | 0.8547 | 0.8420 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8217 | 0.8958 | 0.8571 | 144 | 0.8931 | 0.8304 | 0.8606 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8369 | 0.9382 | 0.8847 | 421 | 0.9574 | 0.9184 | 0.9375 | 49 | 0.9026 | 0.9817 | 0.9405 | 491 | 0.5714 | 0.3636 | 0.4444 | 11 | 0.8367 | 0.8815 | 0.8585 | 0.9810 |
| 0.0673 | 1.31 | 17500 | 0.0851 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.7024 | 0.9077 | 0.7919 | 65 | 0.4 | 0.2857 | 0.3333 | 7 | 0.7817 | 0.8689 | 0.8230 | 206 | 0.7959 | 0.9070 | 0.8478 | 43 | 0.8198 | 0.8511 | 0.8352 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7738 | 0.9028 | 0.8333 | 144 | 0.9162 | 0.9591 | 0.9371 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8655 | 0.9786 | 0.9186 | 421 | 0.775 | 0.6327 | 0.6966 | 49 | 0.8377 | 0.9776 | 0.9023 | 491 | 0.2143 | 0.2727 | 0.2400 | 11 | 0.8231 | 0.8902 | 0.8553 | 0.9816 |
| 0.0715 | 1.35 | 18000 | 0.0821 | 0.8868 | 0.9038 | 0.8952 | 52 | 0.1 | 1.0 | 0.1818 | 7 | 0.6778 | 0.9385 | 0.7871 | 65 | 0.8 | 0.5714 | 0.6667 | 7 | 0.7653 | 0.7913 | 0.7780 | 206 | 0.78 | 0.9070 | 0.8387 | 43 | 0.7410 | 0.8989 | 0.8124 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7458 | 0.9167 | 0.8224 | 144 | 0.8713 | 0.8713 | 0.8713 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.8008 | 0.9644 | 0.875 | 421 | 0.4333 | 0.7959 | 0.5612 | 49 | 0.8920 | 0.9756 | 0.9319 | 491 | 0.8333 | 0.4545 | 0.5882 | 11 | 0.7578 | 0.9082 | 0.8262 | 0.9793 |
| 0.0778 | 1.39 | 18500 | 0.0661 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.0714 | 0.1429 | 0.0952 | 7 | 0.8 | 0.9231 | 0.8571 | 65 | 1.0 | 0.2857 | 0.4444 | 7 | 0.8757 | 0.7864 | 0.8286 | 206 | 0.7547 | 0.9302 | 0.8333 | 43 | 0.7831 | 0.8674 | 0.8231 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8323 | 0.9306 | 0.8787 | 144 | 0.8859 | 0.9532 | 0.9183 | 171 | 0.1875 | 1.0 | 0.3158 | 3 | 0.9138 | 0.9572 | 0.9350 | 421 | 0.7963 | 0.8776 | 0.8350 | 49 | 0.8544 | 0.9919 | 0.9180 | 491 | 0.2 | 0.1818 | 0.1905 | 11 | 0.8172 | 0.8971 | 0.8553 | 0.9829 |
| 0.0672 | 1.42 | 19000 | 0.0841 | 0.6538 | 0.9808 | 0.7846 | 52 | 0.2593 | 1.0 | 0.4118 | 7 | 0.6703 | 0.9385 | 0.7821 | 65 | 0.4 | 0.2857 | 0.3333 | 7 | 0.8162 | 0.7330 | 0.7724 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.7510 | 0.8747 | 0.8082 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7432 | 0.9444 | 0.8318 | 144 | 0.8477 | 0.9766 | 0.9076 | 171 | 0.1579 | 1.0 | 0.2727 | 3 | 0.8103 | 0.9739 | 0.8846 | 421 | 0.6327 | 0.6327 | 0.6327 | 49 | 0.7970 | 0.9674 | 0.8740 | 491 | 0.1190 | 0.4545 | 0.1887 | 11 | 0.7558 | 0.8977 | 0.8207 | 0.9787 |
| 0.0802 | 1.46 | 19500 | 0.0682 | 0.8276 | 0.9231 | 0.8727 | 52 | 0.4615 | 0.8571 | 0.6 | 7 | 0.7468 | 0.9077 | 0.8194 | 65 | 0.3333 | 0.2857 | 0.3077 | 7 | 0.7621 | 0.8398 | 0.7991 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.7958 | 0.8801 | 0.8359 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7735 | 0.9722 | 0.8615 | 144 | 0.9357 | 0.9357 | 0.9357 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.8385 | 0.9620 | 0.8960 | 421 | 0.5556 | 0.9184 | 0.6923 | 49 | 0.8845 | 0.9674 | 0.9241 | 491 | 0.2778 | 0.4545 | 0.3448 | 11 | 0.8074 | 0.9070 | 0.8543 | 0.9819 |
| 0.0886 | 1.5 | 20000 | 0.0633 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.7568 | 0.8615 | 0.8058 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.8980 | 0.8544 | 0.8756 | 206 | 0.9302 | 0.9302 | 0.9302 | 43 | 0.8470 | 0.8916 | 0.8688 | 1652 | 0.25 | 1.0 | 0.4 | 2 | 0.8373 | 0.9653 | 0.8968 | 144 | 0.9032 | 0.9825 | 0.9412 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9044 | 0.9667 | 0.9346 | 421 | 0.7931 | 0.9388 | 0.8598 | 49 | 0.8342 | 0.9939 | 0.9071 | 491 | 0.1053 | 0.3636 | 0.1633 | 11 | 0.8471 | 0.9185 | 0.8814 | 0.9833 |
| 0.0525 | 1.54 | 20500 | 0.0632 | 0.8197 | 0.9615 | 0.8850 | 52 | 0.7 | 1.0 | 0.8235 | 7 | 0.6742 | 0.9231 | 0.7792 | 65 | 0.4444 | 0.5714 | 0.5 | 7 | 0.7819 | 0.9223 | 0.8463 | 206 | 0.6721 | 0.9535 | 0.7885 | 43 | 0.8220 | 0.8723 | 0.8464 | 1652 | 0.0909 | 0.5 | 0.1538 | 2 | 0.7812 | 0.8681 | 0.8224 | 144 | 0.9180 | 0.9825 | 0.9492 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8838 | 0.9572 | 0.9190 | 421 | 0.5 | 0.9592 | 0.6573 | 49 | 0.8173 | 0.9837 | 0.8928 | 491 | 0.25 | 0.3636 | 0.2963 | 11 | 0.8092 | 0.9097 | 0.8565 | 0.9828 |
| 0.0664 | 1.57 | 21000 | 0.0671 | 0.8197 | 0.9615 | 0.8850 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.6778 | 0.9385 | 0.7871 | 65 | 0.375 | 0.4286 | 0.4000 | 7 | 0.7932 | 0.9126 | 0.8488 | 206 | 0.72 | 0.8372 | 0.7742 | 43 | 0.7546 | 0.8935 | 0.8182 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7571 | 0.9306 | 0.8349 | 144 | 0.8777 | 0.9649 | 0.9192 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8867 | 0.9667 | 0.9250 | 421 | 0.8846 | 0.9388 | 0.9109 | 49 | 0.8199 | 0.9919 | 0.8977 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.7829 | 0.9221 | 0.8468 | 0.9830 |
| 0.0524 | 1.61 | 21500 | 0.0674 | 0.8305 | 0.9423 | 0.8829 | 52 | 0.5833 | 1.0 | 0.7368 | 7 | 0.7763 | 0.9077 | 0.8369 | 65 | 0.375 | 0.4286 | 0.4000 | 7 | 0.8889 | 0.8544 | 0.8713 | 206 | 0.7692 | 0.9302 | 0.8421 | 43 | 0.8235 | 0.8838 | 0.8526 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9041 | 0.9167 | 0.9103 | 144 | 0.9527 | 0.9415 | 0.9471 | 171 | 0.4286 | 1.0 | 0.6 | 3 | 0.9470 | 0.9762 | 0.9614 | 421 | 0.7857 | 0.8980 | 0.8381 | 49 | 0.8857 | 0.9939 | 0.9367 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8555 | 0.9140 | 0.8838 | 0.9844 |
| 0.0603 | 1.65 | 22000 | 0.0735 | 0.7812 | 0.9615 | 0.8621 | 52 | 0.0 | 0.0 | 0.0 | 7 | 0.9206 | 0.8923 | 0.9062 | 65 | 0.8 | 0.5714 | 0.6667 | 7 | 0.8062 | 0.8883 | 0.8453 | 206 | 0.6721 | 0.9535 | 0.7885 | 43 | 0.8402 | 0.8051 | 0.8223 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8036 | 0.9375 | 0.8654 | 144 | 0.9167 | 0.9649 | 0.9402 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9249 | 0.9359 | 0.9303 | 421 | 0.7077 | 0.9388 | 0.8070 | 49 | 0.9198 | 0.9817 | 0.9498 | 491 | 0.6667 | 0.5455 | 0.6 | 11 | 0.8558 | 0.8715 | 0.8636 | 0.9822 |
| 0.0674 | 1.69 | 22500 | 0.0639 | 0.8103 | 0.9038 | 0.8545 | 52 | 0.2 | 0.2857 | 0.2353 | 7 | 0.7838 | 0.8923 | 0.8345 | 65 | 1.0 | 0.5714 | 0.7273 | 7 | 0.8852 | 0.8981 | 0.8916 | 206 | 0.8163 | 0.9302 | 0.8696 | 43 | 0.8393 | 0.8759 | 0.8572 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8618 | 0.9097 | 0.8851 | 144 | 0.8771 | 0.9181 | 0.8971 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9400 | 0.9667 | 0.9532 | 421 | 0.9388 | 0.9388 | 0.9388 | 49 | 0.9030 | 0.9857 | 0.9426 | 491 | 0.3846 | 0.4545 | 0.4167 | 11 | 0.8633 | 0.9064 | 0.8844 | 0.9843 |
| 0.0693 | 1.72 | 23000 | 0.0773 | 0.7143 | 0.9615 | 0.8197 | 52 | 0.8571 | 0.8571 | 0.8571 | 7 | 0.8356 | 0.9385 | 0.8841 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8009 | 0.8786 | 0.8380 | 206 | 0.7119 | 0.9767 | 0.8235 | 43 | 0.7847 | 0.9001 | 0.8385 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7640 | 0.9444 | 0.8447 | 144 | 0.8836 | 0.9766 | 0.9278 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7143 | 0.9501 | 0.8155 | 421 | 0.3780 | 0.9796 | 0.5455 | 49 | 0.8134 | 0.9674 | 0.8837 | 491 | 0.5714 | 0.3636 | 0.4444 | 11 | 0.7688 | 0.9212 | 0.8381 | 0.9808 |
| 0.0383 | 1.76 | 23500 | 0.0667 | 0.6410 | 0.9615 | 0.7692 | 52 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.7692 | 0.9231 | 0.8392 | 65 | 0.8333 | 0.7143 | 0.7692 | 7 | 0.8326 | 0.8689 | 0.8504 | 206 | 0.7636 | 0.9767 | 0.8571 | 43 | 0.8580 | 0.8777 | 0.8677 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8571 | 0.9167 | 0.8859 | 144 | 0.9405 | 0.9240 | 0.9322 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8901 | 0.9810 | 0.9333 | 421 | 0.88 | 0.8980 | 0.8889 | 49 | 0.9112 | 0.9817 | 0.9451 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.8628 | 0.9097 | 0.8856 | 0.9845 |
| 0.0496 | 1.8 | 24000 | 0.0712 | 0.8 | 0.9231 | 0.8571 | 52 | 0.8571 | 0.8571 | 0.8571 | 7 | 0.7262 | 0.9385 | 0.8188 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8390 | 0.8350 | 0.8370 | 206 | 0.8889 | 0.9302 | 0.9091 | 43 | 0.8522 | 0.8692 | 0.8607 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8867 | 0.9236 | 0.9048 | 144 | 0.9598 | 0.9766 | 0.9681 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8963 | 0.9857 | 0.9389 | 421 | 0.7015 | 0.9592 | 0.8103 | 49 | 0.9412 | 0.9776 | 0.9590 | 491 | 0.25 | 0.5455 | 0.3429 | 11 | 0.8659 | 0.9073 | 0.8861 | 0.9848 |
| 0.0465 | 1.84 | 24500 | 0.0612 | 0.6667 | 0.9615 | 0.7874 | 52 | 0.75 | 0.8571 | 0.8000 | 7 | 0.7625 | 0.9385 | 0.8414 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8287 | 0.8689 | 0.8483 | 206 | 0.7407 | 0.9302 | 0.8247 | 43 | 0.8236 | 0.8904 | 0.8557 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.7919 | 0.9514 | 0.8644 | 144 | 0.9326 | 0.9708 | 0.9513 | 171 | 0.0513 | 0.6667 | 0.0952 | 3 | 0.9079 | 0.9834 | 0.9441 | 421 | 0.8958 | 0.8776 | 0.8866 | 49 | 0.9186 | 0.9878 | 0.9519 | 491 | 0.1765 | 0.2727 | 0.2143 | 11 | 0.8355 | 0.9212 | 0.8762 | 0.9853 |
| 0.0446 | 1.87 | 25000 | 0.0662 | 0.6410 | 0.9615 | 0.7692 | 52 | 0.6364 | 1.0 | 0.7778 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.8333 | 0.7143 | 0.7692 | 7 | 0.9378 | 0.8786 | 0.9073 | 206 | 0.8333 | 0.9302 | 0.8791 | 43 | 0.8362 | 0.8747 | 0.8550 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8447 | 0.9444 | 0.8918 | 144 | 0.9598 | 0.9766 | 0.9681 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.92 | 0.9834 | 0.9506 | 421 | 0.9070 | 0.7959 | 0.8478 | 49 | 0.9186 | 0.9878 | 0.9519 | 491 | 0.3636 | 0.3636 | 0.3636 | 11 | 0.8659 | 0.9131 | 0.8889 | 0.9851 |
| 0.0496 | 1.91 | 25500 | 0.0653 | 0.7612 | 0.9808 | 0.8571 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8472 | 0.9385 | 0.8905 | 65 | 0.5 | 0.5714 | 0.5333 | 7 | 0.9158 | 0.8981 | 0.9069 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8487 | 0.8729 | 0.8606 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8889 | 0.9444 | 0.9158 | 144 | 0.9586 | 0.9474 | 0.9529 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9077 | 0.9810 | 0.9429 | 421 | 0.7895 | 0.9184 | 0.8491 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.5 | 0.2727 | 0.3529 | 11 | 0.8714 | 0.9137 | 0.8921 | 0.9854 |
| 0.0689 | 1.95 | 26000 | 0.0689 | 0.8596 | 0.9423 | 0.8991 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7887 | 0.8615 | 0.8235 | 65 | 0.5714 | 0.5714 | 0.5714 | 7 | 0.9064 | 0.8932 | 0.8998 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8217 | 0.9122 | 0.8646 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8232 | 0.9375 | 0.8766 | 144 | 0.9222 | 0.9708 | 0.9459 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.8827 | 0.9834 | 0.9303 | 421 | 0.9744 | 0.7755 | 0.8636 | 49 | 0.8574 | 0.9919 | 0.9197 | 491 | 0.4286 | 0.2727 | 0.3333 | 11 | 0.8441 | 0.9299 | 0.8849 | 0.9842 |
| 0.0465 | 1.99 | 26500 | 0.1060 | 0.8136 | 0.9231 | 0.8649 | 52 | 0.5 | 1.0 | 0.6667 | 7 | 0.7778 | 0.8615 | 0.8175 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8552 | 0.9175 | 0.8852 | 206 | 0.82 | 0.9535 | 0.8817 | 43 | 0.8698 | 0.8977 | 0.8835 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8904 | 0.9028 | 0.8966 | 144 | 0.9643 | 0.9474 | 0.9558 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.7361 | 0.9739 | 0.8384 | 421 | 0.25 | 0.0612 | 0.0984 | 49 | 0.8832 | 0.9552 | 0.9178 | 491 | 0.1 | 0.1818 | 0.1290 | 11 | 0.8384 | 0.9040 | 0.8700 | 0.9796 |
| 0.0448 | 2.02 | 27000 | 0.0686 | 0.7385 | 0.9231 | 0.8205 | 52 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8714 | 0.9385 | 0.9037 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.8545 | 0.9126 | 0.8826 | 206 | 0.6727 | 0.8605 | 0.7551 | 43 | 0.8778 | 0.8959 | 0.8868 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9116 | 0.9306 | 0.9210 | 144 | 0.9538 | 0.9649 | 0.9593 | 171 | 0.0 | 0.0 | 0.0 | 3 | 0.9157 | 0.9549 | 0.9349 | 421 | 0.875 | 0.8571 | 0.8660 | 49 | 0.8855 | 0.9919 | 0.9356 | 491 | 0.4 | 0.3636 | 0.3810 | 11 | 0.8790 | 0.9200 | 0.8990 | 0.9854 |
| 0.0379 | 2.06 | 27500 | 0.0633 | 0.8421 | 0.9231 | 0.8807 | 52 | 0.2308 | 0.4286 | 0.3 | 7 | 0.8824 | 0.9231 | 0.9023 | 65 | 0.4545 | 0.7143 | 0.5556 | 7 | 0.8451 | 0.9272 | 0.8843 | 206 | 0.7037 | 0.8837 | 0.7835 | 43 | 0.8901 | 0.8674 | 0.8786 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8303 | 0.9514 | 0.8867 | 144 | 0.9706 | 0.9649 | 0.9677 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9300 | 0.9786 | 0.9537 | 421 | 0.9149 | 0.8776 | 0.8958 | 49 | 0.8385 | 0.9939 | 0.9096 | 491 | 0.2 | 0.3636 | 0.2581 | 11 | 0.8719 | 0.9116 | 0.8913 | 0.9859 |
| 0.0352 | 2.1 | 28000 | 0.0653 | 0.8772 | 0.9615 | 0.9174 | 52 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8158 | 0.9538 | 0.8794 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8733 | 0.9369 | 0.9040 | 206 | 0.8913 | 0.9535 | 0.9213 | 43 | 0.8272 | 0.9128 | 0.8679 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8824 | 0.9375 | 0.9091 | 144 | 0.9706 | 0.9649 | 0.9677 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.8790 | 0.9834 | 0.9283 | 421 | 0.9 | 0.9184 | 0.9091 | 49 | 0.8692 | 0.9878 | 0.9247 | 491 | 0.25 | 0.4545 | 0.3226 | 11 | 0.8493 | 0.9377 | 0.8913 | 0.9844 |
| 0.0328 | 2.14 | 28500 | 0.0599 | 0.8772 | 0.9615 | 0.9174 | 52 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8806 | 0.9077 | 0.8939 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8804 | 0.8932 | 0.8867 | 206 | 0.8542 | 0.9535 | 0.9011 | 43 | 0.8680 | 0.9074 | 0.8872 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.85 | 0.9444 | 0.8947 | 144 | 0.9701 | 0.9474 | 0.9586 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.9452 | 0.9834 | 0.9639 | 421 | 0.6714 | 0.9592 | 0.7899 | 49 | 0.8937 | 0.9756 | 0.9328 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8786 | 0.9293 | 0.9032 | 0.9867 |
| 0.0473 | 2.17 | 29000 | 0.0595 | 0.7692 | 0.9615 | 0.8547 | 52 | 0.2222 | 0.2857 | 0.25 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.6667 | 0.5714 | 0.6154 | 7 | 0.8889 | 0.9320 | 0.9100 | 206 | 0.8367 | 0.9535 | 0.8913 | 43 | 0.8341 | 0.9189 | 0.8744 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8466 | 0.9583 | 0.8990 | 144 | 0.9711 | 0.9825 | 0.9767 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9234 | 0.9739 | 0.9480 | 421 | 0.75 | 0.9184 | 0.8257 | 49 | 0.8844 | 0.9817 | 0.9305 | 491 | 0.5556 | 0.4545 | 0.5 | 11 | 0.8557 | 0.9386 | 0.8953 | 0.9855 |
| 0.0511 | 2.21 | 29500 | 0.0668 | 0.6849 | 0.9615 | 0.8000 | 52 | 0.1522 | 1.0 | 0.2642 | 7 | 0.7561 | 0.9538 | 0.8435 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.8761 | 0.9272 | 0.9009 | 206 | 0.8039 | 0.9535 | 0.8723 | 43 | 0.8154 | 0.9195 | 0.8643 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8491 | 0.9375 | 0.8911 | 144 | 0.9709 | 0.9766 | 0.9738 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.8939 | 0.9810 | 0.9354 | 421 | 0.5789 | 0.8980 | 0.704 | 49 | 0.8403 | 0.9857 | 0.9072 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8214 | 0.9407 | 0.8770 | 0.9845 |
| 0.0369 | 2.25 | 30000 | 0.0695 | 0.6579 | 0.9615 | 0.7812 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.625 | 0.7143 | 0.6667 | 7 | 0.9154 | 0.8932 | 0.9042 | 206 | 0.9535 | 0.9535 | 0.9535 | 43 | 0.8883 | 0.9001 | 0.8942 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9013 | 0.9514 | 0.9257 | 144 | 0.9527 | 0.9415 | 0.9471 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9126 | 0.9430 | 0.9276 | 421 | 0.5104 | 1.0 | 0.6759 | 49 | 0.9286 | 0.9796 | 0.9534 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8837 | 0.9233 | 0.9030 | 0.9854 |
| 0.041 | 2.29 | 30500 | 0.0623 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.4375 | 1.0 | 0.6087 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9061 | 0.9369 | 0.9212 | 206 | 0.8723 | 0.9535 | 0.9111 | 43 | 0.8486 | 0.9225 | 0.8840 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8940 | 0.9375 | 0.9153 | 144 | 0.9708 | 0.9708 | 0.9708 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9556 | 0.9715 | 0.9635 | 421 | 0.7705 | 0.9592 | 0.8545 | 49 | 0.9310 | 0.9898 | 0.9595 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.8803 | 0.9428 | 0.9105 | 0.9853 |
| 0.0385 | 2.32 | 31000 | 0.0632 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7848 | 0.9538 | 0.8611 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.8915 | 0.9175 | 0.9043 | 206 | 0.9111 | 0.9535 | 0.9318 | 43 | 0.8486 | 0.9092 | 0.8778 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8961 | 0.9583 | 0.9262 | 144 | 0.9709 | 0.9766 | 0.9738 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9180 | 0.9834 | 0.9495 | 421 | 0.8478 | 0.7959 | 0.8211 | 49 | 0.8959 | 0.9817 | 0.9368 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8724 | 0.9338 | 0.9021 | 0.9849 |
| 0.0415 | 2.36 | 31500 | 0.0647 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9363 | 0.9272 | 0.9317 | 206 | 0.875 | 0.9767 | 0.9231 | 43 | 0.8679 | 0.9025 | 0.8849 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8726 | 0.9514 | 0.9103 | 144 | 0.9429 | 0.9649 | 0.9538 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9154 | 0.9762 | 0.9448 | 421 | 0.7719 | 0.8980 | 0.8302 | 49 | 0.9067 | 0.9898 | 0.9464 | 491 | 0.1579 | 0.5455 | 0.2449 | 11 | 0.8767 | 0.9329 | 0.9039 | 0.9847 |
| 0.0454 | 2.4 | 32000 | 0.0606 | 0.9091 | 0.9615 | 0.9346 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8971 | 0.9385 | 0.9173 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9139 | 0.9272 | 0.9205 | 206 | 0.8542 | 0.9535 | 0.9011 | 43 | 0.8652 | 0.9056 | 0.8849 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9122 | 0.9375 | 0.9247 | 144 | 0.9483 | 0.9649 | 0.9565 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9321 | 0.9786 | 0.9548 | 421 | 0.8136 | 0.9796 | 0.8889 | 49 | 0.9455 | 0.9898 | 0.9672 | 491 | 0.2143 | 0.5455 | 0.3077 | 11 | 0.888 | 0.9350 | 0.9109 | 0.9869 |
| 0.0334 | 2.44 | 32500 | 0.0610 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8714 | 0.9385 | 0.9037 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8977 | 0.9369 | 0.9169 | 206 | 0.9130 | 0.9767 | 0.9438 | 43 | 0.8463 | 0.9068 | 0.8755 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9007 | 0.9444 | 0.9220 | 144 | 0.9532 | 0.9532 | 0.9532 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9536 | 0.9762 | 0.9648 | 421 | 0.8545 | 0.9592 | 0.9038 | 49 | 0.9419 | 0.9898 | 0.9652 | 491 | 0.25 | 0.5455 | 0.3429 | 11 | 0.8813 | 0.9356 | 0.9076 | 0.9867 |
| 0.0453 | 2.47 | 33000 | 0.0610 | 0.8929 | 0.9615 | 0.9259 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8732 | 0.9538 | 0.9118 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.9057 | 0.9320 | 0.9187 | 206 | 0.9302 | 0.9302 | 0.9302 | 43 | 0.8668 | 0.9098 | 0.8878 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8882 | 0.9375 | 0.9122 | 144 | 0.9588 | 0.9532 | 0.9560 | 171 | 0.375 | 1.0 | 0.5455 | 3 | 0.9303 | 0.9834 | 0.9561 | 421 | 1.0 | 0.8980 | 0.9462 | 49 | 0.9455 | 0.9898 | 0.9672 | 491 | 0.7143 | 0.4545 | 0.5556 | 11 | 0.8952 | 0.9353 | 0.9148 | 0.9875 |
| 0.0225 | 2.51 | 33500 | 0.0607 | 0.9259 | 0.9615 | 0.9434 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.7949 | 0.9538 | 0.8671 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8733 | 0.9369 | 0.9040 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8641 | 0.9007 | 0.8820 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8616 | 0.9514 | 0.9043 | 144 | 0.9412 | 0.9357 | 0.9384 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9281 | 0.9810 | 0.9538 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.9365 | 0.9919 | 0.9634 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8844 | 0.9323 | 0.9077 | 0.9876 |
| 0.0276 | 2.55 | 34000 | 0.0603 | 0.8909 | 0.9423 | 0.9159 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.775 | 0.9538 | 0.8552 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.8894 | 0.9369 | 0.9125 | 206 | 0.9111 | 0.9535 | 0.9318 | 43 | 0.8661 | 0.9201 | 0.8923 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8688 | 0.9653 | 0.9145 | 144 | 0.9649 | 0.9649 | 0.9649 | 171 | 0.1667 | 1.0 | 0.2857 | 3 | 0.9649 | 0.9786 | 0.9717 | 421 | 0.9020 | 0.9388 | 0.92 | 49 | 0.9222 | 0.9898 | 0.9548 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8868 | 0.9428 | 0.9140 | 0.9877 |
| 0.0291 | 2.59 | 34500 | 0.0605 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9091 | 0.9223 | 0.9157 | 206 | 0.9524 | 0.9302 | 0.9412 | 43 | 0.8707 | 0.9213 | 0.8953 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8947 | 0.9444 | 0.9189 | 144 | 0.9758 | 0.9415 | 0.9583 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9448 | 0.9762 | 0.9603 | 421 | 0.9787 | 0.9388 | 0.9583 | 49 | 0.8952 | 0.9919 | 0.9411 | 491 | 0.2632 | 0.4545 | 0.3333 | 11 | 0.8885 | 0.9401 | 0.9136 | 0.9881 |
| 0.0264 | 2.62 | 35000 | 0.0616 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.75 | 0.8571 | 0.8000 | 7 | 0.9019 | 0.9369 | 0.9190 | 206 | 0.8913 | 0.9535 | 0.9213 | 43 | 0.8694 | 0.9310 | 0.8992 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8782 | 0.9514 | 0.9133 | 144 | 0.9422 | 0.9532 | 0.9477 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9258 | 0.9786 | 0.9515 | 421 | 0.8679 | 0.9388 | 0.9020 | 49 | 0.9272 | 0.9857 | 0.9556 | 491 | 0.1852 | 0.4545 | 0.2632 | 11 | 0.8837 | 0.9465 | 0.9140 | 0.9875 |
| 0.0343 | 2.66 | 35500 | 0.0595 | 0.7083 | 0.9808 | 0.8226 | 52 | 0.6667 | 0.8571 | 0.75 | 7 | 0.7949 | 0.9538 | 0.8671 | 65 | 0.7143 | 0.7143 | 0.7143 | 7 | 0.8858 | 0.9417 | 0.9129 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.8556 | 0.9110 | 0.8824 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8616 | 0.9514 | 0.9043 | 144 | 0.9270 | 0.9649 | 0.9456 | 171 | 1.0 | 1.0 | 1.0 | 3 | 0.9388 | 0.9834 | 0.9606 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.8919 | 0.9919 | 0.9392 | 491 | 0.625 | 0.4545 | 0.5263 | 11 | 0.8728 | 0.9389 | 0.9046 | 0.9871 |
| 0.0284 | 2.7 | 36000 | 0.0569 | 0.9074 | 0.9423 | 0.9245 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.5556 | 0.7143 | 0.6250 | 7 | 0.9143 | 0.9320 | 0.9231 | 206 | 0.9070 | 0.9070 | 0.9070 | 43 | 0.8724 | 0.9189 | 0.8950 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9145 | 0.9653 | 0.9392 | 144 | 0.9540 | 0.9708 | 0.9623 | 171 | 0.15 | 1.0 | 0.2609 | 3 | 0.9605 | 0.9810 | 0.9706 | 421 | 0.8364 | 0.9388 | 0.8846 | 49 | 0.8907 | 0.9959 | 0.9404 | 491 | 0.625 | 0.4545 | 0.5263 | 11 | 0.8865 | 0.9425 | 0.9137 | 0.9878 |
| 0.0377 | 2.74 | 36500 | 0.0554 | 0.7083 | 0.9808 | 0.8226 | 52 | 0.5833 | 1.0 | 0.7368 | 7 | 0.7654 | 0.9538 | 0.8493 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.8981 | 0.9417 | 0.9194 | 206 | 0.9091 | 0.9302 | 0.9195 | 43 | 0.8700 | 0.9237 | 0.8961 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9205 | 0.9653 | 0.9424 | 144 | 0.9540 | 0.9708 | 0.9623 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9063 | 0.9881 | 0.9455 | 421 | 0.9020 | 0.9388 | 0.92 | 49 | 0.8825 | 0.9939 | 0.9349 | 491 | 0.4545 | 0.4545 | 0.4545 | 11 | 0.8755 | 0.9477 | 0.9101 | 0.9883 |
| 0.0316 | 2.77 | 37000 | 0.0562 | 0.6711 | 0.9808 | 0.7969 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.9143 | 0.9320 | 0.9231 | 206 | 0.9524 | 0.9302 | 0.9412 | 43 | 0.8721 | 0.9243 | 0.8974 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.9026 | 0.9653 | 0.9329 | 144 | 0.9653 | 0.9766 | 0.9709 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9202 | 0.9857 | 0.9518 | 421 | 0.8070 | 0.9388 | 0.8679 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.5 | 0.4545 | 0.4762 | 11 | 0.8801 | 0.9471 | 0.9123 | 0.9885 |
| 0.0454 | 2.81 | 37500 | 0.0555 | 0.8333 | 0.9615 | 0.8929 | 52 | 0.5 | 1.0 | 0.6667 | 7 | 0.8052 | 0.9538 | 0.8732 | 65 | 0.6667 | 0.8571 | 0.75 | 7 | 0.9023 | 0.9417 | 0.9216 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8782 | 0.9249 | 0.9009 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8642 | 0.9722 | 0.9150 | 144 | 0.9337 | 0.9883 | 0.9602 | 171 | 0.2143 | 1.0 | 0.3529 | 3 | 0.9498 | 0.9881 | 0.9686 | 421 | 0.94 | 0.9592 | 0.9495 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8845 | 0.9492 | 0.9157 | 0.9881 |
| 0.0445 | 2.85 | 38000 | 0.0521 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.5385 | 1.0 | 0.7000 | 7 | 0.8493 | 0.9538 | 0.8986 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9019 | 0.9369 | 0.9190 | 206 | 0.8511 | 0.9302 | 0.8889 | 43 | 0.8769 | 0.9183 | 0.8971 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8797 | 0.9653 | 0.9205 | 144 | 0.9767 | 0.9825 | 0.9796 | 171 | 0.25 | 1.0 | 0.4 | 3 | 0.9243 | 0.9857 | 0.9540 | 421 | 0.8103 | 0.9592 | 0.8785 | 49 | 0.8954 | 0.9939 | 0.9421 | 491 | 0.3125 | 0.4545 | 0.3704 | 11 | 0.8845 | 0.9443 | 0.9134 | 0.9884 |
| 0.0379 | 2.89 | 38500 | 0.0524 | 0.8727 | 0.9231 | 0.8972 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8611 | 0.9538 | 0.9051 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9147 | 0.9369 | 0.9257 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8903 | 0.9183 | 0.9041 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8742 | 0.9653 | 0.9175 | 144 | 0.9711 | 0.9825 | 0.9767 | 171 | 0.2308 | 1.0 | 0.375 | 3 | 0.9101 | 0.9857 | 0.9464 | 421 | 0.8214 | 0.9388 | 0.8762 | 49 | 0.9067 | 0.9898 | 0.9464 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8932 | 0.9434 | 0.9176 | 0.9885 |
| 0.0372 | 2.92 | 39000 | 0.0514 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9279 | 0.9369 | 0.9324 | 206 | 0.8333 | 0.9302 | 0.8791 | 43 | 0.8857 | 0.9195 | 0.9023 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8910 | 0.9653 | 0.9267 | 144 | 0.9767 | 0.9825 | 0.9796 | 171 | 0.3333 | 1.0 | 0.5 | 3 | 0.9141 | 0.9857 | 0.9486 | 421 | 0.8545 | 0.9592 | 0.9038 | 49 | 0.9120 | 0.9919 | 0.9502 | 491 | 0.3333 | 0.4545 | 0.3846 | 11 | 0.8946 | 0.9446 | 0.9189 | 0.9886 |
| 0.0263 | 2.96 | 39500 | 0.0515 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8378 | 0.9538 | 0.8921 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9190 | 0.9369 | 0.9279 | 206 | 0.8511 | 0.9302 | 0.8889 | 43 | 0.8868 | 0.9201 | 0.9031 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8910 | 0.9653 | 0.9267 | 144 | 0.9825 | 0.9825 | 0.9825 | 171 | 0.3 | 1.0 | 0.4615 | 3 | 0.9326 | 0.9857 | 0.9584 | 421 | 0.8868 | 0.9592 | 0.9216 | 49 | 0.9137 | 0.9919 | 0.9512 | 491 | 0.3571 | 0.4545 | 0.4 | 11 | 0.8982 | 0.9449 | 0.9210 | 0.9885 |
| 0.0242 | 3.0 | 40000 | 0.0518 | 0.8889 | 0.9231 | 0.9057 | 52 | 0.875 | 1.0 | 0.9333 | 7 | 0.8267 | 0.9538 | 0.8857 | 65 | 0.875 | 1.0 | 0.9333 | 7 | 0.9190 | 0.9369 | 0.9279 | 206 | 0.8696 | 0.9302 | 0.8989 | 43 | 0.8827 | 0.9201 | 0.9010 | 1652 | 0.0 | 0.0 | 0.0 | 2 | 0.8688 | 0.9653 | 0.9145 | 144 | 0.9825 | 0.9825 | 0.9825 | 171 | 0.2727 | 1.0 | 0.4286 | 3 | 0.9220 | 0.9834 | 0.9517 | 421 | 0.9038 | 0.9592 | 0.9307 | 49 | 0.9086 | 0.9919 | 0.9484 | 491 | 0.3846 | 0.4545 | 0.4167 | 11 | 0.8933 | 0.9446 | 0.9183 | 0.9885 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pig4431/amazonPolarity_fewshot
|
pig4431
| 2022-11-07T07:23:26Z | 2 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-07T07:23:13Z |
---
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
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 160 with parameters:
```
{'batch_size': 8, '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": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 160,
"warmup_steps": 16,
"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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
pig4431/Sentiment140_ELECTRA_5E
|
pig4431
| 2022-11-07T07:08:03Z | 7 | 1 |
transformers
|
[
"transformers",
"pytorch",
"electra",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T07:06:08Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_ELECTRA_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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. -->
# Sentiment140_ELECTRA_5E
This model is a fine-tuned version of [google/electra-base-discriminator](https://huggingface.co/google/electra-base-discriminator) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5410
- Accuracy: 0.84
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6896 | 0.08 | 50 | 0.6605 | 0.7133 |
| 0.6664 | 0.16 | 100 | 0.6054 | 0.7133 |
| 0.5915 | 0.24 | 150 | 0.4777 | 0.8333 |
| 0.5053 | 0.32 | 200 | 0.4735 | 0.7733 |
| 0.4946 | 0.4 | 250 | 0.3847 | 0.8267 |
| 0.4578 | 0.48 | 300 | 0.4025 | 0.8067 |
| 0.4724 | 0.56 | 350 | 0.3642 | 0.8333 |
| 0.4309 | 0.64 | 400 | 0.3762 | 0.86 |
| 0.4818 | 0.72 | 450 | 0.3829 | 0.84 |
| 0.416 | 0.8 | 500 | 0.3599 | 0.8467 |
| 0.4201 | 0.88 | 550 | 0.3469 | 0.8533 |
| 0.3664 | 0.96 | 600 | 0.3462 | 0.8467 |
| 0.4289 | 1.04 | 650 | 0.3470 | 0.86 |
| 0.3859 | 1.12 | 700 | 0.3440 | 0.8533 |
| 0.3599 | 1.2 | 750 | 0.3475 | 0.8533 |
| 0.3287 | 1.28 | 800 | 0.3524 | 0.8467 |
| 0.3331 | 1.36 | 850 | 0.3475 | 0.8733 |
| 0.3236 | 1.44 | 900 | 0.3657 | 0.8467 |
| 0.3502 | 1.52 | 950 | 0.3525 | 0.84 |
| 0.3702 | 1.6 | 1000 | 0.3655 | 0.8333 |
| 0.3323 | 1.68 | 1050 | 0.3405 | 0.84 |
| 0.3452 | 1.76 | 1100 | 0.3376 | 0.8533 |
| 0.3742 | 1.84 | 1150 | 0.3481 | 0.8533 |
| 0.3145 | 1.92 | 1200 | 0.3472 | 0.86 |
| 0.3657 | 2.0 | 1250 | 0.3302 | 0.8733 |
| 0.2601 | 2.08 | 1300 | 0.3612 | 0.86 |
| 0.2954 | 2.16 | 1350 | 0.3640 | 0.8533 |
| 0.2888 | 2.24 | 1400 | 0.3670 | 0.8467 |
| 0.2572 | 2.32 | 1450 | 0.4118 | 0.84 |
| 0.2955 | 2.4 | 1500 | 0.3811 | 0.86 |
| 0.2431 | 2.48 | 1550 | 0.4221 | 0.84 |
| 0.318 | 2.56 | 1600 | 0.3844 | 0.8467 |
| 0.2615 | 2.64 | 1650 | 0.4109 | 0.8333 |
| 0.2389 | 2.72 | 1700 | 0.4420 | 0.8467 |
| 0.2983 | 2.8 | 1750 | 0.4203 | 0.8467 |
| 0.2828 | 2.88 | 1800 | 0.3629 | 0.8733 |
| 0.2897 | 2.96 | 1850 | 0.3916 | 0.8733 |
| 0.2239 | 3.04 | 1900 | 0.4143 | 0.86 |
| 0.2093 | 3.12 | 1950 | 0.4521 | 0.84 |
| 0.2438 | 3.2 | 2000 | 0.4271 | 0.8467 |
| 0.2282 | 3.28 | 2050 | 0.4548 | 0.8333 |
| 0.1918 | 3.36 | 2100 | 0.4533 | 0.86 |
| 0.1698 | 3.44 | 2150 | 0.5177 | 0.84 |
| 0.2765 | 3.52 | 2200 | 0.4884 | 0.84 |
| 0.2282 | 3.6 | 2250 | 0.4697 | 0.8533 |
| 0.239 | 3.68 | 2300 | 0.4766 | 0.8533 |
| 0.2219 | 3.76 | 2350 | 0.4628 | 0.8533 |
| 0.2375 | 3.84 | 2400 | 0.4704 | 0.8533 |
| 0.1883 | 3.92 | 2450 | 0.4744 | 0.84 |
| 0.2049 | 4.0 | 2500 | 0.4977 | 0.84 |
| 0.1958 | 4.08 | 2550 | 0.4906 | 0.84 |
| 0.1656 | 4.16 | 2600 | 0.5219 | 0.8333 |
| 0.1543 | 4.24 | 2650 | 0.5379 | 0.8333 |
| 0.2082 | 4.32 | 2700 | 0.5107 | 0.84 |
| 0.1724 | 4.4 | 2750 | 0.5208 | 0.84 |
| 0.1778 | 4.48 | 2800 | 0.5238 | 0.84 |
| 0.1914 | 4.56 | 2850 | 0.5325 | 0.84 |
| 0.2436 | 4.64 | 2900 | 0.5279 | 0.84 |
| 0.1662 | 4.72 | 2950 | 0.5295 | 0.84 |
| 0.1288 | 4.8 | 3000 | 0.5392 | 0.84 |
| 0.2087 | 4.88 | 3050 | 0.5409 | 0.84 |
| 0.1612 | 4.96 | 3100 | 0.5410 | 0.84 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
pig4431/IMDB_fewshot
|
pig4431
| 2022-11-07T06:51:38Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-06T21:07:06Z |
---
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
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 160 with parameters:
```
{'batch_size': 8, '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": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 160,
"warmup_steps": 16,
"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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
pig4431/Sentiment140_XLNET_5E
|
pig4431
| 2022-11-07T06:22:19Z | 89 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-07T06:20:23Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_XLNET_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.84
---
<!-- 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. -->
# Sentiment140_XLNET_5E
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3797
- Accuracy: 0.84
## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6687 | 0.08 | 50 | 0.5194 | 0.76 |
| 0.5754 | 0.16 | 100 | 0.4500 | 0.7867 |
| 0.5338 | 0.24 | 150 | 0.3725 | 0.8333 |
| 0.5065 | 0.32 | 200 | 0.4093 | 0.8133 |
| 0.4552 | 0.4 | 250 | 0.3910 | 0.8267 |
| 0.5352 | 0.48 | 300 | 0.3888 | 0.82 |
| 0.415 | 0.56 | 350 | 0.3887 | 0.8267 |
| 0.4716 | 0.64 | 400 | 0.3888 | 0.84 |
| 0.4565 | 0.72 | 450 | 0.3619 | 0.84 |
| 0.4447 | 0.8 | 500 | 0.3758 | 0.8333 |
| 0.4407 | 0.88 | 550 | 0.3664 | 0.8133 |
| 0.46 | 0.96 | 600 | 0.3797 | 0.84 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
huggingtweets/mhhmmad_
|
huggingtweets
| 2022-11-07T04:41:10Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-07T04:41:03Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1355122703036936198/SDlJIKsr_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Mohammad Hassan</div>
<div style="text-align: center; font-size: 14px;">@mhhmmad_</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 Mohammad Hassan.
| Data | Mohammad Hassan |
| --- | --- |
| Tweets downloaded | 3017 |
| Retweets | 679 |
| Short tweets | 201 |
| Tweets kept | 2137 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2wifnwvu/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 @mhhmmad_'s tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/23y6lfe2/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/mhhmmad_')
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)
|
tkubotake/xlm-roberta-base-finetuned-panx-fr
|
tkubotake
| 2022-11-07T04:39:39Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-07T02:57:03Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.fr
split: train
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8635672020287405
---
<!-- 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-fr
This model is a fine-tuned version of [tkubotake/xlm-roberta-base-finetuned-panx-de](https://huggingface.co/tkubotake/xlm-roberta-base-finetuned-panx-de) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4157
- F1: 0.8636
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0847 | 1.0 | 191 | 0.4066 | 0.8524 |
| 0.0574 | 2.0 | 382 | 0.4025 | 0.8570 |
| 0.0333 | 3.0 | 573 | 0.4157 | 0.8636 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Marve271/BartConditionalGeneration-bart-large-finetuned-insult
|
Marve271
| 2022-11-07T04:05:25Z | 182 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-06T19:15:18Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: BartConditionalGeneration-bart-large-finetuned-insult
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. -->
# BartConditionalGeneration-bart-large-finetuned-insult
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7901
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 5.6217 | 1.0 | 568 | 4.5864 |
| 4.7444 | 2.0 | 1136 | nan |
| 4.2308 | 3.0 | 1704 | 3.7590 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Formzu/bart-large-japanese
|
Formzu
| 2022-11-07T03:06:32Z | 6 | 1 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bart",
"ja",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-31T06:53:19Z |
---
language:
- ja
license: mit
tags:
- bart
- pytorch
datasets:
- wikipedia
---
# bart-large-japanese
This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University.
Both the encoder and decoder outputs are identical to the original Fairseq model.
### How to use the model
The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-large-japanese/blob/main/tokenization_bart_japanese.py).
Tokenizer requirements:
* [Juman++](https://github.com/ku-nlp/jumanpp)
* [zenhan](https://pypi.org/project/zenhan/)
* [pyknp](https://pypi.org/project/pyknp/)
* [sentencepiece](https://pypi.org/project/sentencepiece/)
#### Simple FillMaskPipeline
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
out = fill_mask(masked_text)
print(out)
# [{'score': 0.03228279948234558, 'token': 2566, 'token_str': 'いい', 'sequence': '天気 が いい から 散歩 し ましょう 。'},
# {'score': 0.023878807201981544, 'token': 27365, 'token_str': '晴れ', 'sequence': '天気 が 晴れ から 散歩 し ましょう 。'},
# {'score': 0.020059829577803612, 'token': 267, 'token_str': '南', 'sequence': '天気 が 南 から 散歩 し ましょう 。'},
# {'score': 0.013921134173870087, 'token': 17, 'token_str': 'な', 'sequence': '天気 が な から 散歩 し ましょう 。'},
# {'score': 0.013069136068224907, 'token': 1718, 'token_str': 'よく', 'sequence': '天気 が よく から 散歩 し ましょう 。'}]
```
#### Text Generation
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "Formzu/bart-large-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
inp = tokenizer(masked_text, return_tensors='pt').to(device)
out = model.generate(**inp, num_beams=1, min_length=0, max_length=20, early_stopping=True, no_repeat_ngram_size=2)
res = "".join(tokenizer.decode(out.squeeze(0).tolist(), skip_special_tokens=True).split(" "))
print(res)
# 天気がいいから散歩しましょう。天気のいいへやから、ここから
```
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Tokenizers 0.12.1
|
Formzu/bart-base-japanese
|
Formzu
| 2022-11-07T02:13:39Z | 7 | 2 |
transformers
|
[
"transformers",
"pytorch",
"mbart",
"text2text-generation",
"bart",
"ja",
"dataset:wikipedia",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-10-31T06:52:38Z |
---
language:
- ja
license: mit
tags:
- bart
- pytorch
datasets:
- wikipedia
---
# bart-base-japanese
This model is converted from the original [Japanese BART Pretrained model](https://nlp.ist.i.kyoto-u.ac.jp/?BART%E6%97%A5%E6%9C%AC%E8%AA%9EPretrained%E3%83%A2%E3%83%87%E3%83%AB) released by Kyoto University.
Both the encoder and decoder outputs are identical to the original Fairseq model.
### How to use the model
The input text should be tokenized by [BartJapaneseTokenizer](https://huggingface.co/Formzu/bart-base-japanese/blob/main/tokenization_bart_japanese.py).
Tokenizer requirements:
* [Juman++](https://github.com/ku-nlp/jumanpp)
* [zenhan](https://pypi.org/project/zenhan/)
* [pyknp](https://pypi.org/project/pyknp/)
* [sentencepiece](https://pypi.org/project/sentencepiece/)
#### Simple FillMaskPipeline
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, pipeline
model_name = "Formzu/bart-base-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
fill_mask = pipeline("fill-mask", model=model, tokenizer=tokenizer)
out = fill_mask(masked_text)
print(out)
# [{'score': 0.19255658984184265, 'token': 1718, 'token_str': 'よく', 'sequence': '天気 が よく から 散歩 し ましょう 。'},
# {'score': 0.14426815509796143, 'token': 5478, 'token_str': '良く', 'sequence': '天気 が 良く から 散歩 し ましょう 。'},
# {'score': 0.05554169788956642, 'token': 6561, 'token_str': '悪い', 'sequence': '天気 が 悪い から 散歩 し ましょう 。'},
# {'score': 0.05524599179625511, 'token': 3553, 'token_str': '良い', 'sequence': '天気 が 良い から 散歩 し ましょう 。'},
# {'score': 0.03720080852508545, 'token': 1370, 'token_str': '良', 'sequence': '天気 が 良 から 散歩 し ましょう 。'}]
```
#### Text Generation
```python
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
import torch
device = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu")
model_name = "Formzu/bart-base-japanese"
model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(device)
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
masked_text = "天気が<mask>から散歩しましょう。"
inp = tokenizer(masked_text, return_tensors='pt').to(device)
out = model.generate(**inp, num_beams=1, min_length=0, max_length=20, early_stopping=True, no_repeat_ngram_size=2)
res = "".join(tokenizer.decode(out.squeeze(0).tolist(), skip_special_tokens=True).split(" "))
print(res)
# 天気がよくなってから散歩しましょう。天気のよく合っているところにいる
```
### Framework versions
- Transformers 4.21.2
- Pytorch 1.12.1+cu116
- Tokenizers 0.12.1
|
kaejo98/bart-base_question_generation
|
kaejo98
| 2022-11-06T23:27:56Z | 8 | 4 |
transformers
|
[
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-01T22:36:20Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bart-base_question_generation
results: []
---
# BART-base Question Generation
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on different questions and answering dataset. It was trained to generation question using two different approaches, <b> Casual-Generation </b> and <b> Context-based-Generation </b>.
## Model description
The model takes context as an input sequence, and will generate a full question sentence as an output sequence. There are two ways the model can be queried produce the questions:
- <b> Casual-Generation </b>: where the model is tasked to generate questions answerable by a given passage. The input should be follow the structure or format: '\<generate_questions\> paragraph: put your passage text here'. <br/>
Example: <br/>
\<generate_questions\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity.
- <b> Context-based-Generation </b>: given a section of a passage (context), the model is tasked to generate questions from the passage about the selected section or context. The input should be follow the structure or format: \<generate_context_questions\> \<section\> put your context here \</section\> paragraph: put your passage text here'. <br/>
Example: <br/>
\<generate_context_questions\> \<section\> Mohorovičić discontinuity \</section\> paragraph: The lithosphere is broken into tectonic plates whose motion allows heat to escape from the interior of the Earth into space. The crust lies on top of the mantle, a configuration that is stable because the upper mantle is made of peridotite and is therefore significantly denser than the crust. The boundary between the crust and mantle is conventionally placed at the Mohorovičić discontinuity, a boundary defined by a contrast in seismic velocity.
The input sequence can then be encoded and passed as the input_ids argument in the model's generate() method.
## limitations
The model was trained on only a limited amount of data hence questions might be poor quality. In addition the questions generated have style similar to that of the training data.
## Training and evaluation data
The dataset used to train the model comprises the training datasets from:
- Reasoning Over Paragraph Effects in Situations (ROPES): https://allenai.org/data/ropes
- SQUAD:
- DROP (Discrete Reasoning Over Paragraphs): https://allenai.org/data/drop
- SciQ
After preprocessing the data from the above listed datasets, we had 408372 examples for training the model and 25k for development and 18k for testing.
## Training procedure
The model is trained (finetuned) for 5 epochs with the hyperparameters listed below:
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: cosine
- lr_scheduler_warmup_ratio: 0.25
- num_epochs: 5
At the end of 5 epochs, the Evaluation loss was: 1.64 and the training loss was: 0.9671.
### Framework versions
- Transformers 4.23.1
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
holacaracola/Arlenas_room
|
holacaracola
| 2022-11-06T23:12:26Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-06T23:12:07Z |
Room with a big wardrobe which contains lots of sport suits, make up and complements Room with a big wardrobe which contains lots of sport suits, make up and complements
|
halflings/house_price_prediction_ser2
|
halflings
| 2022-11-06T21:40:14Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"tabular-regression",
"dataset:house_price_prediction",
"license:unknown",
"model-index",
"region:us"
] |
tabular-regression
| 2022-11-06T21:40:10Z |
---
license: unknown
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- house_price_prediction
model-index:
- name: house_price_prediction_ser2
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: house_price_prediction
name: house_price_prediction
metrics:
- type: mae
name: Mean absolute error
value: 5.011783599853516
- type: loss
name: Model loss
value: 43.01755905151367
---
# regression model trained on "house_price_prediction"
🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_ser2) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
|
halflings/house_price_prediction_dev
|
halflings
| 2022-11-06T21:34:02Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"tabular-regression",
"dataset:house_price_prediction",
"license:unknown",
"model-index",
"region:us"
] |
tabular-regression
| 2022-11-06T21:33:58Z |
---
license: unknown
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- house_price_prediction
model-index:
- name: house_price_prediction_dev
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: house_price_prediction
name: house_price_prediction
metrics:
- type: mae
name: Mean absolute error
value: 7.064809322357178
- type: loss
name: Model loss
value: 98.9962387084961
---
# regression model trained on "house_price_prediction"
🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_dev) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
|
pig4431/Sentiment140_roBERTa_5E
|
pig4431
| 2022-11-06T21:17:53Z | 17 | 1 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:sentiment140",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T21:15:53Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- sentiment140
metrics:
- accuracy
model-index:
- name: Sentiment140_roBERTa_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: sentiment140
type: sentiment140
config: sentiment140
split: train
args: sentiment140
metrics:
- name: Accuracy
type: accuracy
value: 0.8933333333333333
---
<!-- 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. -->
# Sentiment140_roBERTa_5E
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the sentiment140 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4796
- Accuracy: 0.8933
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.699 | 0.08 | 50 | 0.6734 | 0.5467 |
| 0.6099 | 0.16 | 100 | 0.4322 | 0.8 |
| 0.4906 | 0.24 | 150 | 0.3861 | 0.84 |
| 0.4652 | 0.32 | 200 | 0.4288 | 0.7933 |
| 0.4874 | 0.4 | 250 | 0.3872 | 0.84 |
| 0.4735 | 0.48 | 300 | 0.3401 | 0.8667 |
| 0.3909 | 0.56 | 350 | 0.3484 | 0.84 |
| 0.4277 | 0.64 | 400 | 0.3207 | 0.88 |
| 0.3894 | 0.72 | 450 | 0.3310 | 0.8733 |
| 0.4523 | 0.8 | 500 | 0.3389 | 0.8667 |
| 0.4087 | 0.88 | 550 | 0.3515 | 0.8467 |
| 0.3973 | 0.96 | 600 | 0.3513 | 0.8467 |
| 0.4016 | 1.04 | 650 | 0.3501 | 0.8667 |
| 0.3613 | 1.12 | 700 | 0.3327 | 0.8667 |
| 0.343 | 1.2 | 750 | 0.3518 | 0.86 |
| 0.314 | 1.28 | 800 | 0.3555 | 0.88 |
| 0.3407 | 1.36 | 850 | 0.3849 | 0.86 |
| 0.2944 | 1.44 | 900 | 0.3576 | 0.8667 |
| 0.3267 | 1.52 | 950 | 0.3461 | 0.8733 |
| 0.3251 | 1.6 | 1000 | 0.3411 | 0.8667 |
| 0.321 | 1.68 | 1050 | 0.3371 | 0.88 |
| 0.3057 | 1.76 | 1100 | 0.3322 | 0.88 |
| 0.3335 | 1.84 | 1150 | 0.3106 | 0.8667 |
| 0.3363 | 1.92 | 1200 | 0.3158 | 0.8933 |
| 0.2972 | 2.0 | 1250 | 0.3122 | 0.88 |
| 0.2453 | 2.08 | 1300 | 0.3327 | 0.8867 |
| 0.2467 | 2.16 | 1350 | 0.3767 | 0.8667 |
| 0.273 | 2.24 | 1400 | 0.3549 | 0.8667 |
| 0.2672 | 2.32 | 1450 | 0.3470 | 0.88 |
| 0.2352 | 2.4 | 1500 | 0.4092 | 0.8667 |
| 0.2763 | 2.48 | 1550 | 0.3472 | 0.9 |
| 0.2858 | 2.56 | 1600 | 0.3440 | 0.9 |
| 0.2206 | 2.64 | 1650 | 0.3770 | 0.88 |
| 0.2928 | 2.72 | 1700 | 0.3280 | 0.8867 |
| 0.2478 | 2.8 | 1750 | 0.3426 | 0.8867 |
| 0.2362 | 2.88 | 1800 | 0.3578 | 0.8933 |
| 0.2107 | 2.96 | 1850 | 0.3986 | 0.8933 |
| 0.2191 | 3.04 | 1900 | 0.3819 | 0.8933 |
| 0.2267 | 3.12 | 1950 | 0.4047 | 0.8867 |
| 0.2076 | 3.2 | 2000 | 0.4303 | 0.8867 |
| 0.1868 | 3.28 | 2050 | 0.4385 | 0.8933 |
| 0.2239 | 3.36 | 2100 | 0.4175 | 0.8933 |
| 0.2082 | 3.44 | 2150 | 0.4142 | 0.8933 |
| 0.2423 | 3.52 | 2200 | 0.4002 | 0.8867 |
| 0.1878 | 3.6 | 2250 | 0.4662 | 0.88 |
| 0.1892 | 3.68 | 2300 | 0.4783 | 0.88 |
| 0.2259 | 3.76 | 2350 | 0.4487 | 0.88 |
| 0.1859 | 3.84 | 2400 | 0.4456 | 0.8933 |
| 0.2042 | 3.92 | 2450 | 0.4468 | 0.8933 |
| 0.2096 | 4.0 | 2500 | 0.4153 | 0.8867 |
| 0.178 | 4.08 | 2550 | 0.4100 | 0.8933 |
| 0.1621 | 4.16 | 2600 | 0.4292 | 0.8933 |
| 0.1682 | 4.24 | 2650 | 0.4602 | 0.8933 |
| 0.1813 | 4.32 | 2700 | 0.4680 | 0.8933 |
| 0.2033 | 4.4 | 2750 | 0.4735 | 0.8933 |
| 0.1662 | 4.48 | 2800 | 0.4750 | 0.88 |
| 0.1686 | 4.56 | 2850 | 0.4830 | 0.8933 |
| 0.1603 | 4.64 | 2900 | 0.4909 | 0.8933 |
| 0.148 | 4.72 | 2950 | 0.4784 | 0.8933 |
| 0.162 | 4.8 | 3000 | 0.4750 | 0.8867 |
| 0.153 | 4.88 | 3050 | 0.4759 | 0.8867 |
| 0.1657 | 4.96 | 3100 | 0.4796 | 0.8933 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.3.2
- Tokenizers 0.13.1
|
pig4431/amazonPolarity_DistilBERT_5E
|
pig4431
| 2022-11-06T20:58:38Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:amazon_polarity",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T20:54:32Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- amazon_polarity
metrics:
- accuracy
model-index:
- name: amazonPolarity_DistilBERT_5EE
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: amazon_polarity
type: amazon_polarity
config: amazon_polarity
split: train
args: amazon_polarity
metrics:
- name: Accuracy
type: accuracy
value: 0.94
---
<!-- 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. -->
# amazonPolarity_DistilBERT_5EE
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the amazon_polarity dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2899
- Accuracy: 0.94
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6581 | 0.03 | 50 | 0.5315 | 0.84 |
| 0.4321 | 0.05 | 100 | 0.2897 | 0.8933 |
| 0.298 | 0.08 | 150 | 0.3165 | 0.8667 |
| 0.2902 | 0.11 | 200 | 0.2552 | 0.9067 |
| 0.2824 | 0.13 | 250 | 0.2277 | 0.9133 |
| 0.2522 | 0.16 | 300 | 0.1998 | 0.94 |
| 0.2781 | 0.19 | 350 | 0.1933 | 0.94 |
| 0.2668 | 0.21 | 400 | 0.2316 | 0.92 |
| 0.2619 | 0.24 | 450 | 0.1968 | 0.9333 |
| 0.2446 | 0.27 | 500 | 0.1846 | 0.9467 |
| 0.2677 | 0.29 | 550 | 0.1818 | 0.94 |
| 0.2026 | 0.32 | 600 | 0.2348 | 0.9133 |
| 0.2351 | 0.35 | 650 | 0.2127 | 0.92 |
| 0.2685 | 0.37 | 700 | 0.1792 | 0.94 |
| 0.2141 | 0.4 | 750 | 0.2252 | 0.9133 |
| 0.2193 | 0.43 | 800 | 0.2131 | 0.9267 |
| 0.2456 | 0.45 | 850 | 0.2205 | 0.9133 |
| 0.2548 | 0.48 | 900 | 0.1788 | 0.94 |
| 0.2353 | 0.51 | 950 | 0.1954 | 0.9267 |
| 0.2546 | 0.53 | 1000 | 0.1815 | 0.9333 |
| 0.2583 | 0.56 | 1050 | 0.1654 | 0.9333 |
| 0.219 | 0.59 | 1100 | 0.1760 | 0.9467 |
| 0.2241 | 0.61 | 1150 | 0.2107 | 0.92 |
| 0.2201 | 0.64 | 1200 | 0.2381 | 0.8933 |
| 0.1745 | 0.67 | 1250 | 0.1944 | 0.92 |
| 0.2698 | 0.69 | 1300 | 0.1971 | 0.9267 |
| 0.214 | 0.72 | 1350 | 0.1944 | 0.9333 |
| 0.2436 | 0.75 | 1400 | 0.2079 | 0.92 |
| 0.2318 | 0.77 | 1450 | 0.2088 | 0.9333 |
| 0.2206 | 0.8 | 1500 | 0.1875 | 0.94 |
| 0.2593 | 0.83 | 1550 | 0.1797 | 0.9267 |
| 0.1908 | 0.85 | 1600 | 0.1924 | 0.9333 |
| 0.2378 | 0.88 | 1650 | 0.1649 | 0.9267 |
| 0.2332 | 0.91 | 1700 | 0.1768 | 0.94 |
| 0.2125 | 0.93 | 1750 | 0.2276 | 0.92 |
| 0.2174 | 0.96 | 1800 | 0.2035 | 0.9333 |
| 0.19 | 0.99 | 1850 | 0.1805 | 0.94 |
| 0.1515 | 1.01 | 1900 | 0.1832 | 0.94 |
| 0.1671 | 1.04 | 1950 | 0.1902 | 0.94 |
| 0.171 | 1.07 | 2000 | 0.2468 | 0.9267 |
| 0.1495 | 1.09 | 2050 | 0.2276 | 0.9267 |
| 0.1535 | 1.12 | 2100 | 0.1926 | 0.94 |
| 0.2085 | 1.15 | 2150 | 0.1878 | 0.94 |
| 0.1395 | 1.17 | 2200 | 0.1795 | 0.9467 |
| 0.1556 | 1.2 | 2250 | 0.1554 | 0.9467 |
| 0.1273 | 1.23 | 2300 | 0.1707 | 0.94 |
| 0.1873 | 1.25 | 2350 | 0.1867 | 0.9467 |
| 0.1589 | 1.28 | 2400 | 0.2089 | 0.9333 |
| 0.1426 | 1.31 | 2450 | 0.1797 | 0.9467 |
| 0.149 | 1.33 | 2500 | 0.1991 | 0.9333 |
| 0.1535 | 1.36 | 2550 | 0.2116 | 0.94 |
| 0.1671 | 1.39 | 2600 | 0.1704 | 0.9467 |
| 0.1582 | 1.41 | 2650 | 0.1843 | 0.94 |
| 0.1393 | 1.44 | 2700 | 0.1831 | 0.94 |
| 0.1474 | 1.47 | 2750 | 0.1895 | 0.94 |
| 0.203 | 1.49 | 2800 | 0.1843 | 0.9467 |
| 0.1562 | 1.52 | 2850 | 0.2060 | 0.9467 |
| 0.1886 | 1.55 | 2900 | 0.1837 | 0.94 |
| 0.1332 | 1.57 | 2950 | 0.1920 | 0.9467 |
| 0.1519 | 1.6 | 3000 | 0.1789 | 0.9533 |
| 0.1354 | 1.63 | 3050 | 0.1974 | 0.9467 |
| 0.125 | 1.65 | 3100 | 0.1890 | 0.9533 |
| 0.2044 | 1.68 | 3150 | 0.1755 | 0.9533 |
| 0.1746 | 1.71 | 3200 | 0.1607 | 0.9467 |
| 0.1981 | 1.73 | 3250 | 0.1613 | 0.9533 |
| 0.1276 | 1.76 | 3300 | 0.1825 | 0.96 |
| 0.1935 | 1.79 | 3350 | 0.1707 | 0.9533 |
| 0.1848 | 1.81 | 3400 | 0.1697 | 0.96 |
| 0.1596 | 1.84 | 3450 | 0.1581 | 0.9667 |
| 0.1797 | 1.87 | 3500 | 0.1634 | 0.96 |
| 0.1493 | 1.89 | 3550 | 0.1614 | 0.9533 |
| 0.1703 | 1.92 | 3600 | 0.1673 | 0.9467 |
| 0.1951 | 1.95 | 3650 | 0.1589 | 0.9533 |
| 0.1582 | 1.97 | 3700 | 0.1761 | 0.9467 |
| 0.1974 | 2.0 | 3750 | 0.1918 | 0.94 |
| 0.1056 | 2.03 | 3800 | 0.2063 | 0.94 |
| 0.1109 | 2.05 | 3850 | 0.2031 | 0.9467 |
| 0.113 | 2.08 | 3900 | 0.2118 | 0.9467 |
| 0.0834 | 2.11 | 3950 | 0.1974 | 0.9533 |
| 0.1434 | 2.13 | 4000 | 0.2075 | 0.9533 |
| 0.0691 | 2.16 | 4050 | 0.2178 | 0.9533 |
| 0.1144 | 2.19 | 4100 | 0.2383 | 0.9467 |
| 0.1446 | 2.21 | 4150 | 0.2207 | 0.9533 |
| 0.172 | 2.24 | 4200 | 0.2034 | 0.9467 |
| 0.1026 | 2.27 | 4250 | 0.2048 | 0.9467 |
| 0.1131 | 2.29 | 4300 | 0.2334 | 0.9467 |
| 0.121 | 2.32 | 4350 | 0.2367 | 0.9333 |
| 0.1144 | 2.35 | 4400 | 0.2313 | 0.9467 |
| 0.1089 | 2.37 | 4450 | 0.2352 | 0.9533 |
| 0.1193 | 2.4 | 4500 | 0.2440 | 0.94 |
| 0.0689 | 2.43 | 4550 | 0.2379 | 0.9333 |
| 0.1799 | 2.45 | 4600 | 0.2354 | 0.9467 |
| 0.1068 | 2.48 | 4650 | 0.2158 | 0.9533 |
| 0.0974 | 2.51 | 4700 | 0.2456 | 0.94 |
| 0.0637 | 2.53 | 4750 | 0.2191 | 0.9333 |
| 0.1125 | 2.56 | 4800 | 0.2390 | 0.9467 |
| 0.1706 | 2.59 | 4850 | 0.2407 | 0.94 |
| 0.1533 | 2.61 | 4900 | 0.2242 | 0.9533 |
| 0.1357 | 2.64 | 4950 | 0.2119 | 0.9533 |
| 0.1342 | 2.67 | 5000 | 0.2268 | 0.9467 |
| 0.0796 | 2.69 | 5050 | 0.2450 | 0.9467 |
| 0.1351 | 2.72 | 5100 | 0.2499 | 0.94 |
| 0.1285 | 2.75 | 5150 | 0.2252 | 0.94 |
| 0.1563 | 2.77 | 5200 | 0.2191 | 0.94 |
| 0.1022 | 2.8 | 5250 | 0.2256 | 0.9533 |
| 0.11 | 2.83 | 5300 | 0.2365 | 0.9467 |
| 0.0926 | 2.85 | 5350 | 0.2206 | 0.9467 |
| 0.1043 | 2.88 | 5400 | 0.2018 | 0.9533 |
| 0.1041 | 2.91 | 5450 | 0.2268 | 0.9467 |
| 0.1232 | 2.93 | 5500 | 0.2164 | 0.9467 |
| 0.1537 | 2.96 | 5550 | 0.1956 | 0.9533 |
| 0.1188 | 2.99 | 5600 | 0.2126 | 0.9467 |
| 0.0749 | 3.01 | 5650 | 0.2249 | 0.9467 |
| 0.062 | 3.04 | 5700 | 0.2254 | 0.9467 |
| 0.0755 | 3.07 | 5750 | 0.2472 | 0.94 |
| 0.0866 | 3.09 | 5800 | 0.2569 | 0.94 |
| 0.0502 | 3.12 | 5850 | 0.2481 | 0.9467 |
| 0.1158 | 3.15 | 5900 | 0.2457 | 0.94 |
| 0.0413 | 3.17 | 5950 | 0.2500 | 0.94 |
| 0.0966 | 3.2 | 6000 | 0.2851 | 0.9333 |
| 0.0613 | 3.23 | 6050 | 0.2717 | 0.9467 |
| 0.1029 | 3.25 | 6100 | 0.2714 | 0.94 |
| 0.0833 | 3.28 | 6150 | 0.2683 | 0.94 |
| 0.0928 | 3.31 | 6200 | 0.2490 | 0.9467 |
| 0.0571 | 3.33 | 6250 | 0.2575 | 0.9533 |
| 0.1252 | 3.36 | 6300 | 0.2599 | 0.9467 |
| 0.0788 | 3.39 | 6350 | 0.2522 | 0.9467 |
| 0.0862 | 3.41 | 6400 | 0.2489 | 0.9533 |
| 0.112 | 3.44 | 6450 | 0.2452 | 0.9533 |
| 0.0868 | 3.47 | 6500 | 0.2438 | 0.9533 |
| 0.0979 | 3.49 | 6550 | 0.2474 | 0.94 |
| 0.0739 | 3.52 | 6600 | 0.2508 | 0.94 |
| 0.0786 | 3.55 | 6650 | 0.2621 | 0.94 |
| 0.0872 | 3.57 | 6700 | 0.2543 | 0.9333 |
| 0.0962 | 3.6 | 6750 | 0.2347 | 0.9467 |
| 0.124 | 3.63 | 6800 | 0.2319 | 0.9533 |
| 0.0747 | 3.65 | 6850 | 0.2448 | 0.9533 |
| 0.0591 | 3.68 | 6900 | 0.2379 | 0.94 |
| 0.1049 | 3.71 | 6950 | 0.2493 | 0.9333 |
| 0.0772 | 3.73 | 7000 | 0.2429 | 0.94 |
| 0.071 | 3.76 | 7050 | 0.2558 | 0.94 |
| 0.1116 | 3.79 | 7100 | 0.2600 | 0.94 |
| 0.1199 | 3.81 | 7150 | 0.2480 | 0.94 |
| 0.0819 | 3.84 | 7200 | 0.2506 | 0.94 |
| 0.1054 | 3.87 | 7250 | 0.2431 | 0.94 |
| 0.09 | 3.89 | 7300 | 0.2582 | 0.9333 |
| 0.0936 | 3.92 | 7350 | 0.2460 | 0.94 |
| 0.0469 | 3.95 | 7400 | 0.2509 | 0.94 |
| 0.1101 | 3.97 | 7450 | 0.2545 | 0.9467 |
| 0.1077 | 4.0 | 7500 | 0.2640 | 0.9467 |
| 0.0777 | 4.03 | 7550 | 0.2709 | 0.94 |
| 0.0777 | 4.05 | 7600 | 0.2842 | 0.94 |
| 0.0847 | 4.08 | 7650 | 0.2649 | 0.94 |
| 0.0462 | 4.11 | 7700 | 0.2702 | 0.9467 |
| 0.0572 | 4.13 | 7750 | 0.2628 | 0.94 |
| 0.0435 | 4.16 | 7800 | 0.2689 | 0.9467 |
| 0.0566 | 4.19 | 7850 | 0.2727 | 0.9467 |
| 0.1149 | 4.21 | 7900 | 0.2635 | 0.9467 |
| 0.0557 | 4.24 | 7950 | 0.2665 | 0.9467 |
| 0.061 | 4.27 | 8000 | 0.2680 | 0.9467 |
| 0.0664 | 4.29 | 8050 | 0.2767 | 0.9467 |
| 0.0481 | 4.32 | 8100 | 0.2662 | 0.9467 |
| 0.0893 | 4.35 | 8150 | 0.2677 | 0.9467 |
| 0.0855 | 4.37 | 8200 | 0.2733 | 0.9467 |
| 0.0552 | 4.4 | 8250 | 0.2589 | 0.94 |
| 0.0469 | 4.43 | 8300 | 0.2733 | 0.94 |
| 0.0633 | 4.45 | 8350 | 0.2799 | 0.94 |
| 0.0629 | 4.48 | 8400 | 0.2838 | 0.94 |
| 0.0854 | 4.51 | 8450 | 0.2837 | 0.94 |
| 0.0596 | 4.53 | 8500 | 0.2808 | 0.94 |
| 0.0579 | 4.56 | 8550 | 0.2839 | 0.94 |
| 0.0508 | 4.59 | 8600 | 0.2844 | 0.94 |
| 0.0557 | 4.61 | 8650 | 0.2833 | 0.94 |
| 0.0383 | 4.64 | 8700 | 0.2878 | 0.94 |
| 0.0554 | 4.67 | 8750 | 0.2924 | 0.94 |
| 0.0681 | 4.69 | 8800 | 0.2868 | 0.94 |
| 0.065 | 4.72 | 8850 | 0.2888 | 0.94 |
| 0.0731 | 4.75 | 8900 | 0.2946 | 0.94 |
| 0.0638 | 4.77 | 8950 | 0.2886 | 0.94 |
| 0.043 | 4.8 | 9000 | 0.2867 | 0.94 |
| 0.0658 | 4.83 | 9050 | 0.2872 | 0.94 |
| 0.0249 | 4.85 | 9100 | 0.2882 | 0.94 |
| 0.0612 | 4.88 | 9150 | 0.2902 | 0.94 |
| 0.0271 | 4.91 | 9200 | 0.2890 | 0.94 |
| 0.0308 | 4.93 | 9250 | 0.2897 | 0.94 |
| 0.0896 | 4.96 | 9300 | 0.2898 | 0.94 |
| 0.1172 | 4.99 | 9350 | 0.2899 | 0.94 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pig4431/IMDB_DistilBERT_5E
|
pig4431
| 2022-11-06T20:41:02Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T20:36:38Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: IMDB_DistilBERT_5EE
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.94
---
<!-- 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. -->
# IMDB_DistilBERT_5EE
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2023
- Accuracy: 0.94
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6748 | 0.03 | 50 | 0.5955 | 0.88 |
| 0.4404 | 0.06 | 100 | 0.2853 | 0.9 |
| 0.3065 | 0.1 | 150 | 0.2208 | 0.9 |
| 0.3083 | 0.13 | 200 | 0.2023 | 0.9333 |
| 0.2922 | 0.16 | 250 | 0.1530 | 0.94 |
| 0.2761 | 0.19 | 300 | 0.2035 | 0.9267 |
| 0.2145 | 0.22 | 350 | 0.2450 | 0.9 |
| 0.258 | 0.26 | 400 | 0.1680 | 0.9267 |
| 0.2702 | 0.29 | 450 | 0.1607 | 0.9333 |
| 0.2587 | 0.32 | 500 | 0.1496 | 0.9467 |
| 0.2822 | 0.35 | 550 | 0.1405 | 0.9333 |
| 0.2538 | 0.38 | 600 | 0.1396 | 0.9467 |
| 0.2707 | 0.42 | 650 | 0.1626 | 0.9333 |
| 0.2408 | 0.45 | 700 | 0.1623 | 0.9067 |
| 0.2531 | 0.48 | 750 | 0.1300 | 0.9467 |
| 0.2014 | 0.51 | 800 | 0.1529 | 0.9333 |
| 0.2454 | 0.54 | 850 | 0.1365 | 0.94 |
| 0.2282 | 0.58 | 900 | 0.1447 | 0.9533 |
| 0.2554 | 0.61 | 950 | 0.1321 | 0.9467 |
| 0.24 | 0.64 | 1000 | 0.1256 | 0.9467 |
| 0.2239 | 0.67 | 1050 | 0.1290 | 0.9467 |
| 0.2865 | 0.7 | 1100 | 0.1288 | 0.9667 |
| 0.2456 | 0.74 | 1150 | 0.1299 | 0.9533 |
| 0.2407 | 0.77 | 1200 | 0.1565 | 0.9267 |
| 0.2256 | 0.8 | 1250 | 0.1262 | 0.96 |
| 0.238 | 0.83 | 1300 | 0.1599 | 0.9333 |
| 0.2151 | 0.86 | 1350 | 0.1252 | 0.9333 |
| 0.187 | 0.9 | 1400 | 0.1132 | 0.9467 |
| 0.2218 | 0.93 | 1450 | 0.1030 | 0.9533 |
| 0.2371 | 0.96 | 1500 | 0.1036 | 0.9467 |
| 0.2264 | 0.99 | 1550 | 0.1041 | 0.9467 |
| 0.2159 | 1.02 | 1600 | 0.1338 | 0.9267 |
| 0.1773 | 1.06 | 1650 | 0.1218 | 0.94 |
| 0.1381 | 1.09 | 1700 | 0.1593 | 0.94 |
| 0.1582 | 1.12 | 1750 | 0.1445 | 0.9533 |
| 0.1921 | 1.15 | 1800 | 0.1355 | 0.94 |
| 0.206 | 1.18 | 1850 | 0.1511 | 0.9467 |
| 0.1679 | 1.22 | 1900 | 0.1394 | 0.94 |
| 0.1691 | 1.25 | 1950 | 0.1403 | 0.9333 |
| 0.2301 | 1.28 | 2000 | 0.1169 | 0.9467 |
| 0.1764 | 1.31 | 2050 | 0.1507 | 0.9333 |
| 0.1772 | 1.34 | 2100 | 0.1148 | 0.96 |
| 0.1749 | 1.38 | 2150 | 0.1203 | 0.94 |
| 0.1912 | 1.41 | 2200 | 0.1037 | 0.94 |
| 0.1614 | 1.44 | 2250 | 0.1006 | 0.9533 |
| 0.1975 | 1.47 | 2300 | 0.0985 | 0.9533 |
| 0.1843 | 1.5 | 2350 | 0.0922 | 0.9533 |
| 0.1764 | 1.54 | 2400 | 0.1259 | 0.9467 |
| 0.1855 | 1.57 | 2450 | 0.1243 | 0.96 |
| 0.1272 | 1.6 | 2500 | 0.2107 | 0.9267 |
| 0.241 | 1.63 | 2550 | 0.1142 | 0.9533 |
| 0.1584 | 1.66 | 2600 | 0.1194 | 0.9467 |
| 0.1568 | 1.7 | 2650 | 0.1196 | 0.9533 |
| 0.1896 | 1.73 | 2700 | 0.1311 | 0.9533 |
| 0.143 | 1.76 | 2750 | 0.1140 | 0.9533 |
| 0.227 | 1.79 | 2800 | 0.1482 | 0.9333 |
| 0.1404 | 1.82 | 2850 | 0.1366 | 0.94 |
| 0.1865 | 1.86 | 2900 | 0.1174 | 0.94 |
| 0.1659 | 1.89 | 2950 | 0.1189 | 0.94 |
| 0.1882 | 1.92 | 3000 | 0.1144 | 0.9467 |
| 0.1403 | 1.95 | 3050 | 0.1358 | 0.94 |
| 0.2193 | 1.98 | 3100 | 0.1092 | 0.9533 |
| 0.1392 | 2.02 | 3150 | 0.1278 | 0.9267 |
| 0.1292 | 2.05 | 3200 | 0.1186 | 0.96 |
| 0.0939 | 2.08 | 3250 | 0.1183 | 0.94 |
| 0.1356 | 2.11 | 3300 | 0.1939 | 0.94 |
| 0.1175 | 2.14 | 3350 | 0.1499 | 0.94 |
| 0.1285 | 2.18 | 3400 | 0.1538 | 0.94 |
| 0.1018 | 2.21 | 3450 | 0.1796 | 0.9333 |
| 0.1342 | 2.24 | 3500 | 0.1540 | 0.94 |
| 0.17 | 2.27 | 3550 | 0.1261 | 0.94 |
| 0.1548 | 2.3 | 3600 | 0.1375 | 0.9267 |
| 0.1415 | 2.34 | 3650 | 0.1264 | 0.9333 |
| 0.1096 | 2.37 | 3700 | 0.1252 | 0.9333 |
| 0.1001 | 2.4 | 3750 | 0.1546 | 0.94 |
| 0.0934 | 2.43 | 3800 | 0.1534 | 0.94 |
| 0.1287 | 2.46 | 3850 | 0.1735 | 0.9333 |
| 0.0872 | 2.5 | 3900 | 0.1475 | 0.9467 |
| 0.0994 | 2.53 | 3950 | 0.1735 | 0.9467 |
| 0.1558 | 2.56 | 4000 | 0.1585 | 0.9467 |
| 0.1517 | 2.59 | 4050 | 0.2021 | 0.9333 |
| 0.1246 | 2.62 | 4100 | 0.1594 | 0.9267 |
| 0.1228 | 2.66 | 4150 | 0.1338 | 0.9533 |
| 0.1064 | 2.69 | 4200 | 0.1421 | 0.9467 |
| 0.1466 | 2.72 | 4250 | 0.1383 | 0.9467 |
| 0.1243 | 2.75 | 4300 | 0.1604 | 0.9533 |
| 0.1434 | 2.78 | 4350 | 0.1736 | 0.9333 |
| 0.1127 | 2.82 | 4400 | 0.1909 | 0.9267 |
| 0.0908 | 2.85 | 4450 | 0.1958 | 0.9333 |
| 0.1134 | 2.88 | 4500 | 0.1596 | 0.94 |
| 0.1345 | 2.91 | 4550 | 0.1604 | 0.9533 |
| 0.1913 | 2.94 | 4600 | 0.1852 | 0.9267 |
| 0.1382 | 2.98 | 4650 | 0.1852 | 0.9333 |
| 0.1109 | 3.01 | 4700 | 0.1905 | 0.9333 |
| 0.1144 | 3.04 | 4750 | 0.1655 | 0.94 |
| 0.074 | 3.07 | 4800 | 0.2034 | 0.9333 |
| 0.0926 | 3.1 | 4850 | 0.1929 | 0.94 |
| 0.0911 | 3.13 | 4900 | 0.1703 | 0.9333 |
| 0.0933 | 3.17 | 4950 | 0.1826 | 0.9333 |
| 0.1003 | 3.2 | 5000 | 0.1716 | 0.94 |
| 0.0889 | 3.23 | 5050 | 0.1843 | 0.9267 |
| 0.0841 | 3.26 | 5100 | 0.1670 | 0.94 |
| 0.0918 | 3.29 | 5150 | 0.1595 | 0.9467 |
| 0.0795 | 3.33 | 5200 | 0.1504 | 0.96 |
| 0.0978 | 3.36 | 5250 | 0.1317 | 0.96 |
| 0.1202 | 3.39 | 5300 | 0.1641 | 0.9533 |
| 0.0935 | 3.42 | 5350 | 0.1473 | 0.96 |
| 0.0673 | 3.45 | 5400 | 0.1684 | 0.9533 |
| 0.0729 | 3.49 | 5450 | 0.1414 | 0.9533 |
| 0.077 | 3.52 | 5500 | 0.1669 | 0.9533 |
| 0.1264 | 3.55 | 5550 | 0.1364 | 0.96 |
| 0.1282 | 3.58 | 5600 | 0.1575 | 0.9467 |
| 0.0553 | 3.61 | 5650 | 0.1440 | 0.9467 |
| 0.0953 | 3.65 | 5700 | 0.1526 | 0.9533 |
| 0.0886 | 3.68 | 5750 | 0.1633 | 0.94 |
| 0.0901 | 3.71 | 5800 | 0.1704 | 0.9467 |
| 0.0986 | 3.74 | 5850 | 0.1674 | 0.94 |
| 0.0849 | 3.77 | 5900 | 0.1989 | 0.9333 |
| 0.0815 | 3.81 | 5950 | 0.1942 | 0.94 |
| 0.0973 | 3.84 | 6000 | 0.1611 | 0.94 |
| 0.0599 | 3.87 | 6050 | 0.1807 | 0.9267 |
| 0.1068 | 3.9 | 6100 | 0.1966 | 0.94 |
| 0.0889 | 3.93 | 6150 | 0.1979 | 0.9333 |
| 0.0854 | 3.97 | 6200 | 0.2012 | 0.9333 |
| 0.1207 | 4.0 | 6250 | 0.1983 | 0.9333 |
| 0.0735 | 4.03 | 6300 | 0.1795 | 0.94 |
| 0.1148 | 4.06 | 6350 | 0.1966 | 0.94 |
| 0.0725 | 4.09 | 6400 | 0.2290 | 0.94 |
| 0.0576 | 4.13 | 6450 | 0.1936 | 0.9333 |
| 0.0477 | 4.16 | 6500 | 0.2090 | 0.9333 |
| 0.0722 | 4.19 | 6550 | 0.1878 | 0.9333 |
| 0.0936 | 4.22 | 6600 | 0.2087 | 0.94 |
| 0.0715 | 4.25 | 6650 | 0.2040 | 0.94 |
| 0.0586 | 4.29 | 6700 | 0.1862 | 0.9333 |
| 0.0548 | 4.32 | 6750 | 0.1801 | 0.9267 |
| 0.0527 | 4.35 | 6800 | 0.1912 | 0.9333 |
| 0.0813 | 4.38 | 6850 | 0.1941 | 0.9333 |
| 0.0531 | 4.41 | 6900 | 0.1932 | 0.9267 |
| 0.0606 | 4.45 | 6950 | 0.2195 | 0.94 |
| 0.1213 | 4.48 | 7000 | 0.1975 | 0.9333 |
| 0.0807 | 4.51 | 7050 | 0.1915 | 0.9333 |
| 0.076 | 4.54 | 7100 | 0.1987 | 0.9333 |
| 0.0595 | 4.57 | 7150 | 0.2052 | 0.9333 |
| 0.0832 | 4.61 | 7200 | 0.2039 | 0.9333 |
| 0.0657 | 4.64 | 7250 | 0.2186 | 0.94 |
| 0.0684 | 4.67 | 7300 | 0.2063 | 0.94 |
| 0.0429 | 4.7 | 7350 | 0.2056 | 0.94 |
| 0.0531 | 4.73 | 7400 | 0.2139 | 0.94 |
| 0.0556 | 4.77 | 7450 | 0.2153 | 0.94 |
| 0.0824 | 4.8 | 7500 | 0.2010 | 0.9333 |
| 0.039 | 4.83 | 7550 | 0.2079 | 0.94 |
| 0.068 | 4.86 | 7600 | 0.2140 | 0.94 |
| 0.065 | 4.89 | 7650 | 0.2108 | 0.94 |
| 0.0359 | 4.93 | 7700 | 0.2058 | 0.94 |
| 0.0592 | 4.96 | 7750 | 0.2029 | 0.94 |
| 0.0793 | 4.99 | 7800 | 0.2023 | 0.94 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
yunan/ddpm-butterflies-128
|
yunan
| 2022-11-06T20:20:17Z | 1 | 0 |
diffusers
|
[
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2022-11-06T19:06:04Z |
---
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/yunan/ddpm-butterflies-128/tensorboard?#scalars)
|
andrewkroening/GalaxyFarAway-DialoGPT-LeiaOrgana
|
andrewkroening
| 2022-11-06T20:13:52Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T20:12:57Z |
---
language: en
tags:
- conversational
license: cc
---
# GPT-2
This model is based on a GPT-2 model which was fine-tuned on a Hugging Face dataset. It is intended largely as an illustrative example and is not intended to be used for any serious purpose. It's trained on a movie script for goodness' sake.
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.
## Acknowledgements
There are several sources of inspiration and insight for the project that spawned this model. I'd like to recognize them up front:
* The [Microsoft DialoGPT-Medium](https://huggingface.co/microsoft/DialoGPT-medium?text=Hi.) model page was very insightful for getting stated.
* Lynn Zheng [r3dhummingbird](https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua?text=Hey+my+name+is+Thomas%21+How+are+you%3F) put together one heck of an awesome tutorial on how to fine-tune GPT-2 for conversational purposes. I used her tutorial as a starting point for this project. Check out the [Github repo here.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
* [This article](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30) was also very insightful. Written by Rostyslav Neskorozhenyi.
* From a lineage standpoint, it looks like Nathan Cooper kicked this whole thing off with this [notebook.](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
* Noah Gift figured out a few of the big pieces in [this repository.](https://github.com/nogibjj/hugging-face-tutorial-practice)
* I'd be remiss if I also didn't mention Hugging Face's own support [documentation](https://huggingface.co/transformers/v2.0.0/examples.html#gpt-2-gpt-and-causal-language-modeling) and team. All around great.
## Model description
This model uses GPT-2 Medium as a base model and was fine-tuned using scripts from the original (and best) Star Wars Trilogy. In this particular case, it was fine-tuned on Leia Organa's 220-some lines. This is not a lot, and thus the model should not be assumed to have serious integrity. It's just a fun little project.
## Intended uses & limitations
This model is intended to be used for fun and entertainment. Don't take it too seriously.
### Ways to use
You can always chat with the model directly on the Hugging Face website. Just click the "Chat" button on the right side of the model page.
If you want to use the model in your own project, I recommend you train it better using much more data.
To access the GitHub repository I used to train this model, click [here](https://github.com/nogibjj/hugging-face-gpt-trainer/tree/gpt-fine-tune)
## Fine-tuning data
The script to generate this model takes a Hugging Face data set in this approximate format:
| Speaker | Text |
| --- | --- |
| Luke | Hello there. |
| Han | General Kenobi. |
| Luke | You are a bold one. |
The script then asks the user to define parameters for making the dataset and proceeding to fine-tuning. The actual dataset for this model can be found [here.](andrewkroening/Star-wars-scripts-dialogue-IV-VI)
|
andrewkroening/GalaxyFarAway-DialoGPT-Threepio
|
andrewkroening
| 2022-11-06T20:02:42Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T19:56:30Z |
---
language: en
tags:
- conversational
license: cc
---
# GPT-2
This model is based on a GPT-2 model which was fine-tuned on a Hugging Face dataset. It is intended largely as an illustrative example and is not intended to be used for any serious purpose. It's trained on a movie script for goodness' sake.
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.
## Acknowledgements
There are several sources of inspiration and insight for the project that spawned this model. I'd like to recognize them up front:
* The [Microsoft DialoGPT-Medium](https://huggingface.co/microsoft/DialoGPT-medium?text=Hi.) model page was very insightful for getting stated.
* Lynn Zheng [r3dhummingbird](https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua?text=Hey+my+name+is+Thomas%21+How+are+you%3F) put together one heck of an awesome tutorial on how to fine-tune GPT-2 for conversational purposes. I used her tutorial as a starting point for this project. Check out the [Github repo here.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
* [This article](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30) was also very insightful. Written by Rostyslav Neskorozhenyi.
* From a lineage standpoint, it looks like Nathan Cooper kicked this whole thing off with this [notebook.](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
* Noah Gift figured out a few of the big pieces in [this repository.](https://github.com/nogibjj/hugging-face-tutorial-practice)
* I'd be remiss if I also didn't mention Hugging Face's own support [documentation](https://huggingface.co/transformers/v2.0.0/examples.html#gpt-2-gpt-and-causal-language-modeling) and team. All around great.
## Model description
This model uses GPT-2 Medium as a base model and was fine-tuned using scripts from the original (and best) Star Wars Trilogy. In this particular case, it was fine-tuned on C3PO's 300-some lines. This is not a lot, and thus the model should not be assumed to have serious integrity. It's just a fun little project.
## Intended uses & limitations
This model is intended to be used for fun and entertainment. Don't take it too seriously.
### Ways to use
You can always chat with the model directly on the Hugging Face website. Just click the "Chat" button on the right side of the model page.
If you want to use the model in your own project, I recommend you train it better using much more data.
To access the GitHub repository I used to train this model, click [here](https://github.com/nogibjj/hugging-face-gpt-trainer/tree/gpt-fine-tune)
## Fine-tuning data
The script to generate this model takes a Hugging Face data set in this approximate format:
| Speaker | Text |
| --- | --- |
| Luke | Hello there. |
| Han | General Kenobi. |
| Luke | You are a bold one. |
The script then asks the user to define parameters for making the dataset and proceeding to fine-tuning. The actual dataset for this model can be found [here.](andrewkroening/Star-wars-scripts-dialogue-IV-VI)
|
ZenzoHaigoshima/ZenzoHaigoshima
|
ZenzoHaigoshima
| 2022-11-06T20:01:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-06T19:56:12Z |
|
andrewkroening/GalaxyFarAway-DialoGPT-LukeSkywalker
|
andrewkroening
| 2022-11-06T19:50:52Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"en",
"license:cc",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T19:48:55Z |
---
language: en
tags:
- conversational
license: cc
---
# GPT-2
This model is based on a GPT-2 model which was fine-tuned on a Hugging Face dataset. It is intended largely as an illustrative example and is not intended to be used for any serious purpose. It's trained on a movie script for goodness' sake.
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.
## Acknowledgements
There are several sources of inspiration and insight for the project that spawned this model. I'd like to recognize them up front:
* The [Microsoft DialoGPT-Medium](https://huggingface.co/microsoft/DialoGPT-medium?text=Hi.) model page was very insightful for getting stated.
* Lynn Zheng [r3dhummingbird](https://huggingface.co/r3dhummingbird/DialoGPT-medium-joshua?text=Hey+my+name+is+Thomas%21+How+are+you%3F) put together one heck of an awesome tutorial on how to fine-tune GPT-2 for conversational purposes. I used her tutorial as a starting point for this project. Check out the [Github repo here.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
* [This article](https://towardsdatascience.com/make-your-own-rick-sanchez-bot-with-transformers-and-dialogpt-fine-tuning-f85e6d1f4e30) was also very insightful. Written by Rostyslav Neskorozhenyi.
* From a lineage standpoint, it looks like Nathan Cooper kicked this whole thing off with this [notebook.](https://github.com/ncoop57/i-am-a-nerd/blob/master/_notebooks/2020-05-12-chatbot-part-1.ipynb)
* Noah Gift figured out a few of the big pieces in [this repository.](https://github.com/nogibjj/hugging-face-tutorial-practice)
* I'd be remiss if I also didn't mention Hugging Face's own support [documentation](https://huggingface.co/transformers/v2.0.0/examples.html#gpt-2-gpt-and-causal-language-modeling) and team. All around great.
## Model description
This model uses GPT-2 Medium as a base model and was fine-tuned using scripts from the original (and best) Star Wars Trilogy. In this particular case, it was fine-tuned on Luke Skywalker's 490-some lines. This is not a lot, and thus the model should not be assumed to have serious integrity. It's just a fun little project.
## Intended uses & limitations
This model is intended to be used for fun and entertainment. Don't take it too seriously.
### Ways to use
You can always chat with the model directly on the Hugging Face website. Just click the "Chat" button on the right side of the model page.
If you want to use the model in your own project, I recommend you train it better using much more data.
To access the GitHub repository I used to train this model, click [here](https://github.com/nogibjj/hugging-face-gpt-trainer/tree/gpt-fine-tune)
## Fine-tuning data
The script to generate this model takes a Hugging Face data set in this approximate format:
| Speaker | Text |
| --- | --- |
| Luke | Hello there. |
| Han | General Kenobi. |
| Luke | You are a bold one. |
The script then asks the user to define parameters for making the dataset and proceeding to fine-tuning. The actual dataset for this model can be found [here.](andrewkroening/Star-wars-scripts-dialogue-IV-VI)
|
halflings/house_price_prediction_ser
|
halflings
| 2022-11-06T19:40:06Z | 0 | 2 |
mlconsole
|
[
"mlconsole",
"tabular-regression",
"dataset:house_price_prediction",
"license:unknown",
"model-index",
"region:us"
] |
tabular-regression
| 2022-11-06T19:40:02Z |
---
license: unknown
inference: false
tags:
- mlconsole
- tabular-regression
library_name: mlconsole
metrics:
- mae
- loss
datasets:
- house_price_prediction
model-index:
- name: house_price_prediction_ser
results:
- task:
type: tabular-regression
name: tabular-regression
dataset:
type: house_price_prediction
name: house_price_prediction
metrics:
- type: mae
name: Mean absolute error
value: 5.011783599853516
- type: loss
name: Model loss
value: 43.01755905151367
---
# regression model trained on "house_price_prediction"
🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/house_price_prediction_ser) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
|
pig4431/IMDB_XLNET_5E
|
pig4431
| 2022-11-06T19:29:12Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlnet",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T19:24:11Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
model-index:
- name: IMDB_XLNET_5E
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.94
---
<!-- 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. -->
# IMDB_XLNET_5E
This model is a fine-tuned version of [xlnet-base-cased](https://huggingface.co/xlnet-base-cased) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3195
- Accuracy: 0.94
## 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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3192 | 0.63 | 50 | 0.2033 | 0.94 |
| 0.196 | 1.27 | 100 | 0.2036 | 0.9467 |
| 0.1651 | 1.9 | 150 | 0.2106 | 0.9267 |
| 0.0628 | 2.53 | 200 | 0.3531 | 0.92 |
| 0.0865 | 3.16 | 250 | 0.2186 | 0.9533 |
| 0.0436 | 3.8 | 300 | 0.2718 | 0.9533 |
| 0.0254 | 4.43 | 350 | 0.3195 | 0.94 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.13.0
- Datasets 2.6.1
- Tokenizers 0.13.1
|
syuCream/A
|
syuCream
| 2022-11-06T18:59:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-06T18:58:27Z |
import torch
from diffusers import StableDiffusionPipeline
from torch import autocast
MODEL_ID = "CompVis/stable-diffusion-v1-4"
DEVICE = "cuda"
YOUR_TOKEN = "コピーしたアクセストークン"
pipe = StableDiffusionPipeline.from_pretrained(MODEL_ID, revision="fp16", torch_dtype=torch.float16, use_auth_token=YOUR_TOKEN)
pipe.to(DEVICE)
prompt = "a dog painted by Katsuhika Hokusai"
with autocast(DEVICE):
image = pipe(prompt, guidance_scale=7.5)["sample"][0]
image.save("test.png")
|
sd-concepts-library/terraria-style
|
sd-concepts-library
| 2022-11-06T18:59:29Z | 0 | 12 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-06T18:59:25Z |
---
license: mit
---
### terraria style on Stable Diffusion
This is the `<terr-sty>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
cyburn/mollie_monger
|
cyburn
| 2022-11-06T18:27:48Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-06T15:29:38Z |
# mollie monget style
This model will produce images styled like the great artist Hollie Mengert. By respect for the artist I have changed the name to Mollie Monger.
To call on the style add ", by mollie monger" at the end of your prompt.
Samples are included in the repo.
|
ianlaauu/fine-tuning-NLP
|
ianlaauu
| 2022-11-06T18:15:42Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:gpl-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-04T20:19:05Z |
---
license: gpl-2.0
---
- Pre-trained model:
Roberta
- tags:
GLU
- datasets:
MRPC
|
sd-concepts-library/coraline
|
sd-concepts-library
| 2022-11-06T17:06:08Z | 0 | 2 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-06T09:24:14Z |
---
license: mit
---
### coraline on Stable Diffusion
This is the `coraline` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as an `object`:
This is the sample created at the end of training: "a graffiti on the wall with coraline"
|
dchaplinsky/uk_ner_web_trf_large
|
dchaplinsky
| 2022-11-06T16:35:38Z | 5 | 6 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"dataset:ner-uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-10-31T18:26:48Z |
---
tags:
- spacy
- token-classification
language: uk
datasets:
- ner-uk
license: mit
model-index:
- name: uk_ner_web_trf_large
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9183514774
- name: NER Recall
type: recall
value: 0.915503876
- name: NER F Score
type: f_score
value: 0.9169254658
widget:
- text: "Президент Володимир Зеленський пояснив, що наразі діалог із режимом Володимира путіна неможливий, адже агресор обрав курс на знищення українського народу. За словами Зеленського цей режим РФ виявляє неповагу до суверенітету і територіальної цілісності України."
---
# uk_ner_web_trf_large
## Model description
**uk_ner_web_trf_large** is a fine-tuned [XLM-Roberta model](https://huggingface.co/xlm-roberta-large) that is ready to use for **Named Entity Recognition** and achieves a **SoA** performance for the NER task for Ukrainian language. It outperforms another SpaCy model, [uk_core_news_trf](https://huggingface.co/ukr-models/uk_core_news_trf) on a NER task.
It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
The model was fine-tuned on the [NER-UK dataset](https://github.com/lang-uk/ner-uk), released by the [lang-uk](https://lang.org.ua).
Smaller transformer based model for the SpaCy is available [here](https://huggingface.co/dchaplinsky/uk_ner_web_trf_base).
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk project](https://lang.org.ua), 2022
|
dchaplinsky/uk_ner_web_trf_base
|
dchaplinsky
| 2022-11-06T16:34:16Z | 8 | 3 |
spacy
|
[
"spacy",
"token-classification",
"uk",
"dataset:ner-uk",
"license:mit",
"model-index",
"region:us"
] |
token-classification
| 2022-10-24T22:39:42Z |
---
tags:
- spacy
- token-classification
language: uk
datasets:
- ner-uk
license: mit
model-index:
- name: uk_ner_web_trf_base
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.8987742191
- name: NER Recall
type: recall
value: 0.8810077519
- name: NER F Score
type: f_score
value: 0.8898023096
widget:
- text: "Президент Володимир Зеленський пояснив, що наразі діалог із режимом Володимира путіна неможливий, адже агресор обрав курс на знищення українського народу. За словами Зеленського цей режим РФ виявляє неповагу до суверенітету і територіальної цілісності України."
---
# uk_ner_web_trf_base
## Model description
**uk_ner_web_trf_base** is a fine-tuned [XLM-Roberta model](https://huggingface.co/xlm-roberta-base) that is ready to use for **Named Entity Recognition** and achieves a performance close to **SoA** for the NER task for Ukrainian language. It has been trained to recognize four types of entities: location (LOC), organizations (ORG), person (PERS) and Miscellaneous (MISC).
The model was fine-tuned on the [NER-UK dataset](https://github.com/lang-uk/ner-uk), released by the [lang-uk](https://lang.org.ua).
A bigger model, trained on xlm-roberta-large with the **State-of-the-Art** performance is available [here](https://huggingface.co/dchaplinsky/uk_ner_web_trf_large).
Copyright: [Dmytro Chaplynskyi](https://twitter.com/dchaplinsky), [lang-uk project](https://lang.org.ua), 2022
|
damikkuch/dmkch
|
damikkuch
| 2022-11-06T15:48:22Z | 0 | 0 | null |
[
"license:openrail",
"region:us"
] | null | 2022-11-06T15:46:36Z |
---
license: openrail
---
git lfs install
git clone https://huggingface.co/damikkuch/dmkch
|
Yaxin/bert-base-multilingual-cased-42-QAData
|
Yaxin
| 2022-11-06T15:37:24Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-06T15:36:30Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-base-multilingual-cased-42-QAData
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-multilingual-cased-42-QAData
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0873
- Precision: 0.4420
- Recall: 0.2887
- F1: 0.3493
- Accuracy: 0.9755
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 4e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.1064 | 1.0 | 3118 | 0.0873 | 0.4420 | 0.2887 | 0.3493 | 0.9755 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pere/t5-sami-oversetter
|
pere
| 2022-11-06T14:22:18Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2022-10-19T07:08:44Z |
---
license: apache-2.0
---
# T5 Sami - Norwegian - Sami
Placeholder for future model. Description is coming soon.
|
fgaim/tibert-base
|
fgaim
| 2022-11-06T14:12:22Z | 13 | 1 |
transformers
|
[
"transformers",
"pytorch",
"jax",
"bert",
"fill-mask",
"ti",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-03-02T23:29:05Z |
---
language: ti
widget:
- text: "ዓቕሚ ደቂኣንስትዮ [MASK] ብግብሪ ተራእዩ"
---
# BERT Base for Tigrinya Language
We pre-train a BERT base-uncased model for Tigrinya on a dataset of 40 million tokens trained for 40 epochs.
This repo contains the original pre-trained Flax model that was trained on a TPU v3.8 and its corresponding PyTorch version.
## Hyperparameters
The hyperparameters corresponding to the model sizes mentioned above are as follows:
| Model Size | L | AH | HS | FFN | P | Seq |
|------------|----|----|-----|------|------|------|
| BASE | 12 | 12 | 768 | 3072 | 110M | 512 |
(L = number of layers; AH = number of attention heads; HS = hidden size; FFN = feedforward network dimension; P = number of parameters; Seq = maximum sequence length.)
## Citation
If you use this model in your product or research, please cite as follows:
```
@article{Fitsum2021TiPLMs,
author={Fitsum Gaim and Wonsuk Yang and Jong C. Park},
title={Monolingual Pre-trained Language Models for Tigrinya},
year=2021,
publisher={WiNLP 2021 at EMNLP 2021}
}
```
|
radioactive11/flower-classification
|
radioactive11
| 2022-11-06T14:09:30Z | 0 | 0 | null |
[
"doi:10.57967/hf/0096",
"region:us"
] | null | 2022-11-06T14:06:45Z |
# flower-classifier
This Project is an application of Machine Learning with python programming.
- It focuses on Flower classification using deep learning concepts and machine learning algorithms.
- The goal was to apply Deep learning techniques to train a flower classifier to recognize different species of flowers. Flower recognition uses the edge and colour characteristics of flower images to classify flowers.
The project is broken down into multiple steps:
1. Load and preprocess the image dataset
2. Train the image classifier on your dataset
3. Use the trained classifier to predict image content
> PS: Please do not forget to give this repo a star.
|
tatakof/q-FrozenLake-v1-4x4-noSlippery
|
tatakof
| 2022-11-06T12:36:48Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-06T12:36:42Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **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="franfram/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"])
```
|
halflings/diabetes_detection_v2
|
halflings
| 2022-11-06T11:21:56Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"tabular-classification",
"dataset:diabetes_detection",
"license:unknown",
"model-index",
"region:us"
] |
tabular-classification
| 2022-11-06T11:21:52Z |
---
license: unknown
inference: false
tags:
- mlconsole
- tabular-classification
library_name: mlconsole
metrics:
- accuracy
- loss
datasets:
- diabetes_detection
model-index:
- name: diabetes_detection_v2
results:
- task:
type: tabular-classification
name: tabular-classification
dataset:
type: diabetes_detection
name: diabetes_detection
metrics:
- type: accuracy
name: Accuracy
value: 0.7395833730697632
- type: loss
name: Model loss
value: 0.5416829586029053
---
# classification model trained on "diabetes_detection"
🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/diabetes_detection_v2) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
|
irfanns/autotrain-english-to-interlingua-translator-2002766502
|
irfanns
| 2022-11-06T10:56:33Z | 100 | 0 |
transformers
|
[
"transformers",
"pytorch",
"autotrain",
"translation",
"en",
"it",
"dataset:irfanns/autotrain-data-english-to-interlingua-translator",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] |
translation
| 2022-11-06T10:44:14Z |
---
tags:
- autotrain
- translation
language:
- en
- it
datasets:
- irfanns/autotrain-data-english-to-interlingua-translator
co2_eq_emissions:
emissions: 19.067960229529483
---
# Model Trained Using AutoTrain
- Problem type: Translation
- Model ID: 2002766502
- CO2 Emissions (in grams): 19.0680
## Validation Metrics
- Loss: 1.241
- SacreBLEU: 42.137
- Gen len: 32.318
|
vanme/vmehlin_distilbert-finetuned-squad
|
vanme
| 2022-11-06T10:37:11Z | 19 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-10-24T13:12:36Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: vmehlin_distilbert-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. -->
# vmehlin_distilbert-finetuned-squad
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the squad dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
### co2_eq_emissions:
- emissions: 49.49 g
- source: eco2AI
- training_time: 00:31:54
- geographical_location: Bavaria, Germany
- hardware_used: Intel(R) Xeon(R) Gold 5215 CPUs (2devices) & NVIDIA A40 (1 device)
|
arielazzi/whisper-small-pt
|
arielazzi
| 2022-11-06T07:59:53Z | 104 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"pt",
"dataset:mozilla-foundation/common_voice_11_0",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-05T20:54:53Z |
---
language:
- pt
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small PT - Ariel Azzi
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: pt, split: test'
metrics:
- name: Wer
type: wer
value: 14.344671278521048
---
<!-- 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. -->
# Whisper Small PT - Ariel Azzi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2065
- Wer: 14.3447
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.198 | 0.59 | 1000 | 0.2338 | 16.2424 |
| 0.0933 | 1.19 | 2000 | 0.2138 | 14.9756 |
| 0.082 | 1.78 | 3000 | 0.2024 | 14.2111 |
| 0.0452 | 2.38 | 4000 | 0.2065 | 14.3447 |
### Framework versions
- Transformers 4.25.0.dev0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
okho0653/distilbert-base-uncased-finetuned-sst-2-english-finetuned-cad-20pc
|
okho0653
| 2022-11-06T06:51:03Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T06:40:47Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-cad-20pc
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-sst-2-english-finetuned-cad-20pc
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 7 | 0.0032 | 1.0 | 1.0 |
| No log | 2.0 | 14 | 0.0002 | 1.0 | 1.0 |
| No log | 3.0 | 21 | 0.0001 | 1.0 | 1.0 |
| No log | 4.0 | 28 | 0.0001 | 1.0 | 1.0 |
| No log | 5.0 | 35 | 0.0001 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
okho0653/distilbert-base-uncased-finetuned-sst-2-english-finetuned-20pc
|
okho0653
| 2022-11-06T06:40:16Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T06:27:55Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-sst-2-english-finetuned-20pc
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-sst-2-english-finetuned-20pc
This model is a fine-tuned version of [distilbert-base-uncased-finetuned-sst-2-english](https://huggingface.co/distilbert-base-uncased-finetuned-sst-2-english) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5078
- Accuracy: 0.8333
- F1: 0.3721
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 41 | 0.3986 | 0.8272 | 0.0667 |
| No log | 2.0 | 82 | 0.3829 | 0.8519 | 0.4 |
| No log | 3.0 | 123 | 0.4916 | 0.8333 | 0.2286 |
| No log | 4.0 | 164 | 0.4894 | 0.8333 | 0.4490 |
| No log | 5.0 | 205 | 0.5078 | 0.8333 | 0.3721 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
munjulurik/autoShots
|
munjulurik
| 2022-11-06T06:34:39Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"t5",
"text2text-generation",
"news",
"summarizer",
"inshorts",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-02T04:24:04Z |
---
language:
- en
tags:
- t5
- news
- summarizer
- inshorts
---
## Model description
AutoShots is a news summariser model, built by mimicking InShorts application, which manually summarises news into ~60 words. It is a T5-Small model, that has been fine tuned with data scraped from Inshorts website.
Disclaimer: This model and the use of Inshorts data was solely for research and learning perspective, and is not intended to be used as any commercial application. Specials thanks to Inshorts website for allowing me to access their data :)
### How to use
Here is how to use this model with the [pipeline API](https://huggingface.co/transformers/main_classes/pipelines.html):
```python
from transformers import pipeline
print(summarizer("""Washington [US], October 31 (ANI): US President Joe Biden lost his temper with Volodymyr Zelenskyy in June during a phone conversation when he asked for more military aid, NBC News reported on Monday, citing sources familiar with the call. The report said Biden routinely calls Zelenskyy when the US announces new aid packages for Ukraine. But the June call was different. Biden had barely finished informing Zelenskyy that he had approved another USD 1 billion in military assistance for Ukraine when his counterpart started asking for extra help Kyiv needs but isn’t getting, the report said. Biden raised his voice, and as per the NBC report said Zelenskyy could “show a little more gratitude.” Prior to the June 15 phone call, Biden’s dissatisfaction with Zelenskyy had been building for weeks, the sources said. According to them, the US president and a number of his aides believed that Washington was doing everything possible and as quickly as possible, but Zelenskyy continued to publicly pay attention only to what was not being done. After Zelenskyy was rebuffed during the June call, Zelenskyy publicly delivered a video message thanking Biden for the assistance and defusing the tensions. “I had an important conversation with US President Biden today,” NBC quoted Ukraine’s president in videotaped remarks. “I am grateful for this support. It is especially important for our defence in Donbas.” The United States has been a leading provider of security assistance to Ukraine, particularly since the start of the Russia-Ukraine conflict on February 24. This report on the Biden-Zelenskyy phone call comes two days after Washington announced USD 275 million in additional military assistance for Ukraine. “This drawdown will bring the total US military assistance for Ukraine to an unprecedented level of more than USD 18.5 billion since the beginning of the Administration,” the US State Department said in a statement. The United States, in 2022, provided more advanced defence equipment to Ukraine, as well as greater amounts of previously provided equipment, according to a Congressional Research Service report. According to Pentagon, US security assistance committed to Ukraine, includes High Mobility Artillery Rocket Systems, Stinger anti-aircraft systems, Javelin anti-armour systems and Mi-17 helicopters. Ukrainian officials have sought to acquire other advanced systems, including fighter aircraft, anti-ship, and additional air defence and anti-missile capabilities. (ANI) This report is auto-generated from ANI news service. ThePrint holds no responsibility for its content."""))
>>> [{'summary_text': "US President Joe Biden lost his temper with US President Volodymyr Zelenskyy during a phone conversation in June when he asked for more military aid, NBC News reported on Monday. Biden had barely finished informing him that he had approved another USD 1 billion in military assistance for Ukraine when his counterpart started asking for extra help Kyiv needs but isn't getting, the report said."}]
```
|
okho0653/distilbert-base-uncased-finetuned-cad-20pc
|
okho0653
| 2022-11-06T06:26:56Z | 107 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T06:17:09Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-cad-20pc
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-cad-20pc
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: 0.0221
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 7 | 0.2262 | 1.0 | 1.0 |
| No log | 2.0 | 14 | 0.0736 | 1.0 | 1.0 |
| No log | 3.0 | 21 | 0.0358 | 1.0 | 1.0 |
| No log | 4.0 | 28 | 0.0249 | 1.0 | 1.0 |
| No log | 5.0 | 35 | 0.0221 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
okho0653/distilbert-base-uncased-finetuned-20pc
|
okho0653
| 2022-11-06T06:16:40Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T06:04:12Z |
---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-20pc
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-20pc
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: 0.3326
- Accuracy: 0.8642
- F1: 0.4762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 41 | 0.4428 | 0.8333 | 0.0 |
| No log | 2.0 | 82 | 0.4012 | 0.8333 | 0.0 |
| No log | 3.0 | 123 | 0.3619 | 0.8333 | 0.1818 |
| No log | 4.0 | 164 | 0.3488 | 0.8580 | 0.3784 |
| No log | 5.0 | 205 | 0.3326 | 0.8642 | 0.4762 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/jdfromny206
|
huggingtweets
| 2022-11-06T05:44:41Z | 108 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T05:31:26Z |
---
language: en
thumbnail: http://www.huggingtweets.com/jdfromny206/1667713430931/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/1521632298273288193/svg4l6b7_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">JDfromNY</div>
<div style="text-align: center; font-size: 14px;">@jdfromny206</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 JDfromNY.
| Data | JDfromNY |
| --- | --- |
| Tweets downloaded | 3228 |
| Retweets | 107 |
| Short tweets | 128 |
| Tweets kept | 2993 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1kxuv9gk/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 @jdfromny206's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3e7l89e5) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3e7l89e5/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/jdfromny206')
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)
|
huggingtweets/alexabliss_wwe
|
huggingtweets
| 2022-11-06T05:06:07Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T04:18:55Z |
---
language: en
thumbnail: http://www.huggingtweets.com/alexabliss_wwe/1667711162135/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/1271821102134833153/krgeswcX_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Lexi (Kaufman) Cabrera</div>
<div style="text-align: center; font-size: 14px;">@alexabliss_wwe</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 Lexi (Kaufman) Cabrera.
| Data | Lexi (Kaufman) Cabrera |
| --- | --- |
| Tweets downloaded | 3184 |
| Retweets | 1160 |
| Short tweets | 399 |
| Tweets kept | 1625 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2hgwztvb/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 @alexabliss_wwe's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/2vlezdiv) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/2vlezdiv/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/alexabliss_wwe')
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)
|
okho0653/Bio_ClinicalBERT-finetuned-cad-20pc
|
okho0653
| 2022-11-06T02:59:42Z | 109 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T02:45:20Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bio_ClinicalBERT-finetuned-cad-20pc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-finetuned-cad-20pc
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0088
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 7 | 0.1109 | 1.0 | 1.0 |
| No log | 2.0 | 14 | 0.0284 | 1.0 | 1.0 |
| No log | 3.0 | 21 | 0.0142 | 1.0 | 1.0 |
| No log | 4.0 | 28 | 0.0097 | 1.0 | 1.0 |
| No log | 5.0 | 35 | 0.0088 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
uripper/GIANNIS
|
uripper
| 2022-11-06T02:34:15Z | 5 | 0 |
diffusers
|
[
"diffusers",
"unconditional-image-generation",
"diffusers:DDPMPipeline",
"region:us"
] |
unconditional-image-generation
| 2022-11-01T10:20:02Z |
---
tags:
- unconditional-image-generation
---
|
okho0653/Bio_ClinicalBERT-finetuned-20pc
|
okho0653
| 2022-11-06T02:33:46Z | 5 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-06T02:19:10Z |
---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: Bio_ClinicalBERT-finetuned-20pc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Bio_ClinicalBERT-finetuned-20pc
This model is a fine-tuned version of [emilyalsentzer/Bio_ClinicalBERT](https://huggingface.co/emilyalsentzer/Bio_ClinicalBERT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3213
- Accuracy: 0.8580
- F1: 0.4390
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 41 | 1.0399 | 0.8642 | 0.45 |
| No log | 2.0 | 82 | 1.1412 | 0.8519 | 0.4 |
| No log | 3.0 | 123 | 1.2759 | 0.8642 | 0.45 |
| No log | 4.0 | 164 | 1.2953 | 0.8519 | 0.5385 |
| No log | 5.0 | 205 | 1.3213 | 0.8580 | 0.4390 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
Robertuus/Crypto_Sentiment_Analysis_Bert
|
Robertuus
| 2022-11-06T02:00:41Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"text-classification",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-05T14:41:48Z |
---
license: unknown
---
# Bert model fine-tuned to analyze the sentiment of messages, LABEL_0 is positive and LABEL_1 is negative.
|
sd-concepts-library/smurf-style
|
sd-concepts-library
| 2022-11-06T01:34:45Z | 0 | 4 | null |
[
"license:mit",
"region:us"
] | null | 2022-11-06T01:34:41Z |
---
license: mit
---
### Smurf Style on Stable Diffusion
This is the `<smurfy>` concept taught to Stable Diffusion via Textual Inversion. You can load this concept into the [Stable Conceptualizer](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb) notebook. You can also train your own concepts and load them into the concept libraries using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb).
Here is the new concept you will be able to use as a `style`:
|
huggingtweets/ibdwssbm-kodorinssb-tsm_leffen
|
huggingtweets
| 2022-11-06T01:12:43Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-06T01:10:36Z |
---
language: en
thumbnail: http://www.huggingtweets.com/ibdwssbm-kodorinssb-tsm_leffen/1667697159635/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/1560338805445611521/SwRxF60m_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/1499195152639926276/t4_WbYMx_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/1513270656196292608/t2voAbPh_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">TSM FTX Leffen & Panda | iBDW (Cody Schwab) & FLY | KoDoRiN</div>
<div style="text-align: center; font-size: 14px;">@ibdwssbm-kodorinssb-tsm_leffen</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 TSM FTX Leffen & Panda | iBDW (Cody Schwab) & FLY | KoDoRiN.
| Data | TSM FTX Leffen | Panda | iBDW (Cody Schwab) | FLY | KoDoRiN |
| --- | --- | --- | --- |
| Tweets downloaded | 3244 | 3249 | 3048 |
| Retweets | 301 | 493 | 479 |
| Short tweets | 335 | 235 | 275 |
| Tweets kept | 2608 | 2521 | 2294 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/7pksc1xu/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 @ibdwssbm-kodorinssb-tsm_leffen's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/19lbljqq) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/19lbljqq/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/ibdwssbm-kodorinssb-tsm_leffen')
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)
|
ryo-hsgw/xlm-roberta-base-finetuned-panx-en
|
ryo-hsgw
| 2022-11-05T23:46:25Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-05T23:43:18Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-en
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.en
metrics:
- name: F1
type: f1
value: 0.6863181312569522
---
<!-- 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-en
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: 0.3927
- F1: 0.6863
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.1465 | 1.0 | 50 | 0.5838 | 0.4777 |
| 0.505 | 2.0 | 100 | 0.4627 | 0.6393 |
| 0.3783 | 3.0 | 150 | 0.3927 | 0.6863 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ryo-hsgw/xlm-roberta-base-finetuned-panx-it
|
ryo-hsgw
| 2022-11-05T23:43:08Z | 6 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-05T23:39:48Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-it
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.it
metrics:
- name: F1
type: f1
value: 0.8224755700325732
---
<!-- 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-it
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: 0.2521
- F1: 0.8225
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.8088 | 1.0 | 70 | 0.3423 | 0.7009 |
| 0.2844 | 2.0 | 140 | 0.2551 | 0.8027 |
| 0.1905 | 3.0 | 210 | 0.2521 | 0.8225 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
ryo-hsgw/xlm-roberta-base-finetuned-panx-fr
|
ryo-hsgw
| 2022-11-05T23:39:34Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2022-11-05T23:34:50Z |
---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-fr
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.fr
metrics:
- name: F1
type: f1
value: 0.8325761399966348
---
<!-- 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-fr
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: 0.2978
- F1: 0.8326
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.574 | 1.0 | 191 | 0.3495 | 0.7889 |
| 0.2649 | 2.0 | 382 | 0.2994 | 0.8242 |
| 0.1716 | 3.0 | 573 | 0.2978 | 0.8326 |
### Framework versions
- Transformers 4.11.3
- Pytorch 1.11.0
- Datasets 1.16.1
- Tokenizers 0.10.3
|
huggingtweets/sama-willmanidis
|
huggingtweets
| 2022-11-05T23:12:05Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T23:11:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/sama-willmanidis/1667689920861/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/804990434455887872/BG0Xh7Oa_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/1580635866334101504/K2OCKgAJ_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI CYBORG 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Sam Altman & Will Manidis</div>
<div style="text-align: center; font-size: 14px;">@sama-willmanidis</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 Sam Altman & Will Manidis.
| Data | Sam Altman | Will Manidis |
| --- | --- | --- |
| Tweets downloaded | 3247 | 3244 |
| Retweets | 389 | 62 |
| Short tweets | 144 | 442 |
| Tweets kept | 2714 | 2740 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2smlli7t/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 @sama-willmanidis's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/285i3b4q) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/285i3b4q/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/sama-willmanidis')
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)
|
huggingtweets/aeronautblue
|
huggingtweets
| 2022-11-05T21:43:10Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T21:39:42Z |
---
language: en
thumbnail: http://www.huggingtweets.com/aeronautblue/1667684473479/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/1515688111526891521/o_3LoG40_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">blue</div>
<div style="text-align: center; font-size: 14px;">@aeronautblue</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 blue.
| Data | blue |
| --- | --- |
| Tweets downloaded | 2373 |
| Retweets | 460 |
| Short tweets | 379 |
| Tweets kept | 1534 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/e1wsp7qa/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 @aeronautblue's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/61928z1e) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/61928z1e/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/aeronautblue')
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)
|
tatakof/ppo-LunarLander-v2
|
tatakof
| 2022-11-05T21:38:58Z | 1 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-05T17:16:16Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 278.23 +/- 24.06
name: mean_reward
verified: false
---
# **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
...
```
|
CTAE4OK/Niki
|
CTAE4OK
| 2022-11-05T21:14:22Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-05T21:09:35Z |
from diffusers import DiffusionPipeline
pipeline = DiffusionPipeline.from_pretrained("DGSpitzer/Cyberpunk-Anime-Diffusion")
|
halflings/diabetes_detection_fixed3
|
halflings
| 2022-11-05T20:43:11Z | 0 | 0 |
mlconsole
|
[
"mlconsole",
"tabular-classification",
"dataset:diabetes_detection",
"license:unknown",
"model-index",
"region:us"
] |
tabular-classification
| 2022-11-05T20:43:08Z |
---
license: unknown
inference: false
tags:
- mlconsole
- tabular-classification
library_name: mlconsole
metrics:
- accuracy
- loss
datasets:
- diabetes_detection
model-index:
- name: diabetes_detection_fixed3
results:
- task:
type: tabular-classification
name: tabular-classification
dataset:
type: diabetes_detection
name: diabetes_detection
metrics:
- type: accuracy
name: Accuracy
value: 0.78125
- type: loss
name: Model loss
value: 0.523585319519043
---
# classification model trained on "diabetes_detection"
🤖 [Load and use this model](https://mlconsole.com/model/hf/halflings/diabetes_detection_fixed3) in one click.
🧑💻 [Train your own model](https://mlconsole.com) on ML Console.
|
flamesbob/BrokenM_style
|
flamesbob
| 2022-11-05T20:35:41Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-03T16:45:01Z |
---
license: creativeml-openrail-m
---
`Broken mirror, shattered mirror, brokenM_style` this style gives a shattered mirror / reflection to prompts.
License This embedding is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage. The CreativeML OpenRAIL License specifies:
You can't use the embedding to deliberately produce nor share illegal or harmful outputs or content The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license You may re-distribute the weights and use the embedding commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here
|
radeveljic99/ppo-LunarLander-v2
|
radeveljic99
| 2022-11-05T20:24:44Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2022-11-05T20:02:50Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 174.96 +/- 12.10
name: mean_reward
verified: false
---
# **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
...
```
|
Laxman/my-awesome-setfit-model
|
Laxman
| 2022-11-05T20:05:30Z | 4 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-05T20:05:14Z |
---
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
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 100 with parameters:
```
{'batch_size': 16, '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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 100,
"warmup_steps": 10,
"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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
OpenMatch/co-condenser-large-msmarco
|
OpenMatch
| 2022-11-05T20:02:56Z | 9 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2022-10-28T00:11:04Z |
---
license: mit
---
This model has been pretrained on MS MARCO passages first, then fine-tuned on the MS MARCO training set following the approach described in the paper **Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**. The model can be used to reproduce the experimental results associated GitHub repository is available here https://github.com/OpenMatch/COCO-DR.
This model is trained with BERT-large as the backbone with 335M hyperparameters.
|
huggingtweets/_akhaliq-cyalm-iluminatibot
|
huggingtweets
| 2022-11-05T19:25:03Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T19:24:56Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1570915453534453763/sFncOvJP_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/423106148279922688/anTfhXtr_400x400.jpeg')">
</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/1451191636810092553/kpM5Fe12_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">illuminatibot & cyril almeida & AK</div>
<div style="text-align: center; font-size: 14px;">@_akhaliq-cyalm-iluminatibot</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 illuminatibot & cyril almeida & AK.
| Data | illuminatibot | cyril almeida | AK |
| --- | --- | --- | --- |
| Tweets downloaded | 3250 | 454 | 3246 |
| Retweets | 0 | 9 | 1390 |
| Short tweets | 1602 | 29 | 168 |
| Tweets kept | 1648 | 416 | 1688 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/1zfr5cxv/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 @_akhaliq-cyalm-iluminatibot's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/38z7gf3g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/38z7gf3g/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/_akhaliq-cyalm-iluminatibot')
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)
|
Ballesteyoni/Woman
|
Ballesteyoni
| 2022-11-05T18:11:28Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-05T18:09:52Z |
Women dancing in a circle in menstrual blood in moon shadow with chamans
|
barbarabax/unicorns
|
barbarabax
| 2022-11-05T18:02:06Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-05T15:44:14Z |
Use unicornstyle in prompt ------
language:
- "List of ISO 639-1 code for your language"
- English
tags:
- ckpt
- unicorn
license: "openrail"
|
ocm/distilbert-base-uncased-finetuned-emotion
|
ocm
| 2022-11-05T17:45:19Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-29T11:15:47Z |
---
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: 0.935
- name: F1
type: f1
value: 0.9351083637430424
---
<!-- 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: 0.1582
- Accuracy: 0.935
- F1: 0.9351
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7703 | 1.0 | 250 | 0.2588 | 0.918 | 0.9165 |
| 0.2031 | 2.0 | 500 | 0.1773 | 0.928 | 0.9282 |
| 0.1385 | 3.0 | 750 | 0.1593 | 0.934 | 0.9342 |
| 0.1101 | 4.0 | 1000 | 0.1582 | 0.935 | 0.9351 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
erose/wav2vec2-malayalam_english-3h
|
erose
| 2022-11-05T16:11:28Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"malayalam",
"ml_en",
"code-switching",
"ml",
"en",
"dataset:erose/code_switching-ml-en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2022-11-03T13:25:37Z |
---
license: apache-2.0
description: wav2vec2 based model for malayalam-english code-switched speech
language:
- ml
- en
tags:
- automatic-speech-recognition
- malayalam
- ml_en
- code-switching
datasets:
- erose/code_switching-ml-en
model-index:
- name: wav2vec2 ml_en
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: erose/code_switching-ml-en (test set)
type: code_switching-ml-en
args: ml_en
metrics:
- name: Test WER
type: wer
value: 58.93
- name: Test CER
type: cer
value: 19.45
---
|
s-nlp/gpt2-base-gedi-detoxification
|
s-nlp
| 2022-11-05T16:05:17Z | 30 | 2 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conditional-text-generation",
"en",
"arxiv:2109.08914",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-03-02T23:29:05Z |
---
language:
- en
tags:
- text-generation
- conditional-text-generation
---
# Model Details
This is a conditional language model based on [gpt2-medium](https://huggingface.co/gpt2-medium/) but with a vocabulary from [t5-base](https://huggingface.co/t5-base), for compatibility with T5-based paraphrasers such as [t5-paranmt-detox](https://huggingface.co/SkolkovoInstitute/t5-paranmt-detox). The model is conditional on two styles, `toxic` and `normal`, and was fine-tuned on the dataset from the Jigsaw [toxic comment classification challenge](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge).
The model was trained for the paper [Text Detoxification using Large Pre-trained Neural Models](https://arxiv.org/abs/2109.08914) (Dale et al, 2021) that describes its possible usage in more detail.
An example of its use and the code for its training is given in https://github.com/skoltech-nlp/detox.
## Model Description
- **Developed by:** SkolkovoInstitute
- **Model type:** Conditional Text Generation
- **Language:** English
- **Related Models:**
- **Parent Model:** [gpt2-medium](https://huggingface.co/gpt2-medium/)
- **Source of vocabulary:** [t5-base](https://huggingface.co/t5-base)
- **Resources for more information:**
- The paper [Text Detoxification using Large Pre-trained Neural Models](https://arxiv.org/abs/2109.08914)
- Its repository https://github.com/skoltech-nlp/detox.
# Uses
The model is intended for usage as a discriminator in a text detoxification pipeline using the ParaGeDi approach (see [the paper](https://arxiv.org/abs/2109.08914) for more details). It can also be used for text generation conditional on toxic or non-toxic style, but we do not know how to condition it on the things other than toxicity, so we do not recommend this usage. Another possible use is as a toxicity classifier (using the Bayes rule), but the model is not expected to perform better than e.g. a BERT-based standard classifier.
# Bias, Risks, and Limitations
The model inherits all the risks of its parent model, [gpt2-medium](https://huggingface.co/gpt2-medium/). It also inherits all the biases of the [Jigsaw dataset](https://www.kaggle.com/c/jigsaw-toxic-comment-classification-challenge) on which it was fine-tuned. The model is intended to be conditional on style, but in fact it does not clearly separate the concepts of style and content, so it might regard some texts as toxic or safe based not on the style, but on their topics or keywords.
# Training Details
See the paper [Text Detoxification using Large Pre-trained Neural Models](https://arxiv.org/abs/2109.08914) and [the associated code](https://github.com/s-nlp/detox/tree/main/emnlp2021/style_transfer/paraGeDi).
# Evaluation
The model has not been evaluated on its own, only as a part as a ParaGeDi text detoxification pipeline (see [the paper](https://arxiv.org/abs/2109.08914)).
# Citation
**BibTeX:**
```
@inproceedings{dale-etal-2021-text,
title = "Text Detoxification using Large Pre-trained Neural Models",
author = "Dale, David and
Voronov, Anton and
Dementieva, Daryna and
Logacheva, Varvara and
Kozlova, Olga and
Semenov, Nikita and
Panchenko, Alexander",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.629",
pages = "7979--7996",
}
```
|
pepa/deberta-v3-base-fever
|
pepa
| 2022-11-05T15:03:56Z | 7 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-29T07:36:51Z |
---
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-base-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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. -->
# deberta-v3-base-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5146
- eval_p: 0.8912
- eval_r: 0.8904
- eval_f1: 0.8897
- eval_runtime: 49.9875
- eval_samples_per_second: 376.194
- eval_steps_per_second: 47.032
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
pepa/deberta-v3-large-fever
|
pepa
| 2022-11-05T15:03:41Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-01T20:22:41Z |
---
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-large-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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. -->
# deberta-v3-large-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.5286
- eval_p: 0.8827
- eval_r: 0.8826
- eval_f1: 0.8816
- eval_runtime: 231.4062
- eval_samples_per_second: 81.264
- eval_steps_per_second: 10.16
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
pepa/deberta-v3-small-fever
|
pepa
| 2022-11-05T15:03:10Z | 4 | 0 |
transformers
|
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-10-29T07:39:36Z |
---
tags:
- generated_from_trainer
model-index:
- name: deberta-v3-small-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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. -->
# deberta-v3-small-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4816
- eval_p: 0.8811
- eval_r: 0.8783
- eval_f1: 0.8780
- eval_runtime: 28.4486
- eval_samples_per_second: 661.017
- eval_steps_per_second: 82.64
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
pepa/bigbird-roberta-large-fever
|
pepa
| 2022-11-05T15:02:18Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"text-classification",
"generated_from_trainer",
"dataset:copenlu/fever_gold_evidence",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-02T07:55:05Z |
---
tags:
- generated_from_trainer
model-index:
- name: bigbird-roberta-large-fever
results: []
datasets:
- copenlu/fever_gold_evidence
---
<!-- 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. -->
# bigbird-roberta-large-fever
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.4721
- eval_p: 0.8933
- eval_r: 0.8930
- eval_f1: 0.8926
- eval_runtime: 153.523
- eval_samples_per_second: 122.49
- eval_steps_per_second: 15.314
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Framework versions
- Transformers 4.21.1
- Pytorch 1.12.1
- Datasets 2.6.1
- Tokenizers 0.12.1
|
Marve271/BartConditionalGeneration-finetuned-insult
|
Marve271
| 2022-11-05T14:08:07Z | 5 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-05T12:49:03Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: BartConditionalGeneration-finetuned-insult
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. -->
# BartConditionalGeneration-finetuned-insult
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.2955
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 338 | 4.9652 |
| 5.5666 | 2.0 | 676 | 4.2736 |
| 4.9076 | 3.0 | 1014 | 4.2014 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
AlanRobotics/bert_q_a_test
|
AlanRobotics
| 2022-11-05T13:51:56Z | 61 | 0 |
transformers
|
[
"transformers",
"tf",
"bert",
"question-answering",
"generated_from_keras_callback",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2022-11-05T12:18:36Z |
---
tags:
- generated_from_keras_callback
model-index:
- name: bert_q_a_test
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. -->
# bert_q_a_test
This model was trained from scratch 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 4.24.0
- TensorFlow 2.9.2
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/damienleevoice
|
huggingtweets
| 2022-11-05T13:39:35Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T13:34:14Z |
---
language: en
thumbnail: http://www.huggingtweets.com/damienleevoice/1667655559324/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/1525084483036164097/z_XHCdw1_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Damien Lee</div>
<div style="text-align: center; font-size: 14px;">@damienleevoice</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 Damien Lee.
| Data | Damien Lee |
| --- | --- |
| Tweets downloaded | 1774 |
| Retweets | 52 |
| Short tweets | 315 |
| Tweets kept | 1407 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/2x5e6fes/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 @damienleevoice's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3f9sjksd) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3f9sjksd/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/damienleevoice')
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)
|
komekami/distilbert-base-uncased-finetuned-emotion
|
komekami
| 2022-11-05T12:39:54Z | 106 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-05T11:11:31Z |
---
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: 0.934
- name: F1
type: f1
value: 0.9341415823944494
---
<!-- 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: 0.1599
- Accuracy: 0.934
- F1: 0.9341
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.1887 | 1.0 | 250 | 0.1806 | 0.9295 | 0.9293 |
| 0.1245 | 2.0 | 500 | 0.1599 | 0.934 | 0.9341 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
pallavi176/bert-fine-tuned-cola
|
pallavi176
| 2022-11-05T11:55:11Z | 10 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-05T11:33:40Z |
---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- matthews_correlation
model-index:
- name: bert-fine-tuned-cola
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: train
args: cola
metrics:
- name: Matthews Correlation
type: matthews_correlation
value: 0.5778590180299453
---
<!-- 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-fine-tuned-cola
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8136
- Matthews Correlation: 0.5779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Matthews Correlation |
|:-------------:|:-----:|:----:|:---------------:|:--------------------:|
| 0.4785 | 1.0 | 1069 | 0.5265 | 0.4996 |
| 0.3162 | 2.0 | 2138 | 0.6626 | 0.5701 |
| 0.1779 | 3.0 | 3207 | 0.8136 | 0.5779 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
fadhilarkn/setfit-model
|
fadhilarkn
| 2022-11-05T10:25:17Z | 1 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"mpnet",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-05T10:25:05Z |
---
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
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
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 40 with parameters:
```
{'batch_size': 16, '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": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 40,
"warmup_steps": 4,
"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': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
jonathang/dog_breed
|
jonathang
| 2022-11-05T10:16:42Z | 0 | 0 |
fastai
|
[
"fastai",
"region:us"
] | null | 2022-11-02T03:00:36Z |
---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
OpenBioML/LibreFold_AF2_reproduction
|
OpenBioML
| 2022-11-05T08:56:37Z | 0 | 0 | null |
[
"AlphaFold",
"protein model",
"license:cc-by-4.0",
"region:us"
] | null | 2022-10-20T17:22:18Z |
---
tags:
- AlphaFold
- protein model
license: cc-by-4.0
---
# LibreFold AF2 reproduction
Text
## Intro
Text
## Model description
Text
## Intended uses & limitations
Text
### How to use
Text
### Limitations and bias
Text
## Training data
Text
### Collection process
Text
## Training procedure
### Preprocessing
Text
### BibTeX entry and citation info
```bibtex
Text
```
|
Shunian/yelp_review_classification
|
Shunian
| 2022-11-05T07:21:17Z | 12 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:yelp_review_full",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2022-11-05T06:38:54Z |
---
tags:
- generated_from_trainer
datasets:
- yelp_review_full
metrics:
- accuracy
model-index:
- name: yelp_review_classification
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: yelp_review_full
type: yelp_review_full
config: yelp_review_full
split: train
args: yelp_review_full
metrics:
- name: Accuracy
type: accuracy
value: 0.6852
---
<!-- 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. -->
# yelp_review_classification
This model was trained from scratch on the yelp_review_full dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8517
- Accuracy: 0.6852
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Accuracy | Validation Loss |
|:-------------:|:-----:|:------:|:--------:|:---------------:|
| 0.7149 | 1.0 | 40625 | 0.6889 | 0.7167 |
| 0.6501 | 2.0 | 81250 | 0.6967 | 0.6979 |
| 0.5547 | 3.0 | 121875 | 0.6915 | 0.7377 |
| 0.5375 | 4.0 | 162500 | 0.6895 | 0.7611 |
| 0.4386 | 5.0 | 203125 | 0.8517 | 0.6852 |
### Framework versions
- Transformers 4.22.2
- Pytorch 1.12.1+cu102
- Datasets 2.5.2
- Tokenizers 0.12.1
|
MarkGG/Romance-baseline
|
MarkGG
| 2022-11-05T05:16:39Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T03:22:25Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Romance-baseline
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. -->
# Romance-baseline
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5909
## 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: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.94 | 15 | 10.7009 |
| No log | 1.94 | 30 | 10.0799 |
| No log | 2.94 | 45 | 9.6627 |
| No log | 3.94 | 60 | 9.4619 |
| No log | 4.94 | 75 | 9.2970 |
| No log | 5.94 | 90 | 9.0919 |
| No log | 6.94 | 105 | 8.9071 |
| No log | 7.94 | 120 | 8.7240 |
| No log | 8.94 | 135 | 8.5485 |
| No log | 9.94 | 150 | 8.3952 |
| No log | 10.94 | 165 | 8.2469 |
| No log | 11.94 | 180 | 8.1193 |
| No log | 12.94 | 195 | 7.9918 |
| No log | 13.94 | 210 | 7.8662 |
| No log | 14.94 | 225 | 7.7394 |
| No log | 15.94 | 240 | 7.6219 |
| No log | 16.94 | 255 | 7.5135 |
| No log | 17.94 | 270 | 7.4110 |
| No log | 18.94 | 285 | 7.3021 |
| No log | 19.94 | 300 | 7.2021 |
| No log | 20.94 | 315 | 7.1276 |
| No log | 21.94 | 330 | 7.0278 |
| No log | 22.94 | 345 | 6.9627 |
| No log | 23.94 | 360 | 6.8806 |
| No log | 24.94 | 375 | 6.8214 |
| No log | 25.94 | 390 | 6.7725 |
| No log | 26.94 | 405 | 6.7101 |
| No log | 27.94 | 420 | 6.6792 |
| No log | 28.94 | 435 | 6.6361 |
| No log | 29.94 | 450 | 6.5950 |
| No log | 30.94 | 465 | 6.5745 |
| No log | 31.94 | 480 | 6.5469 |
| No log | 32.94 | 495 | 6.5520 |
| No log | 33.94 | 510 | 6.5121 |
| No log | 34.94 | 525 | 6.5255 |
| No log | 35.94 | 540 | 6.5179 |
| No log | 36.94 | 555 | 6.5079 |
| No log | 37.94 | 570 | 6.5138 |
| No log | 38.94 | 585 | 6.5170 |
| No log | 39.94 | 600 | 6.4807 |
| No log | 40.94 | 615 | 6.5338 |
| No log | 41.94 | 630 | 6.4960 |
| No log | 42.94 | 645 | 6.5342 |
| No log | 43.94 | 660 | 6.5119 |
| No log | 44.94 | 675 | 6.5614 |
| No log | 45.94 | 690 | 6.5235 |
| No log | 46.94 | 705 | 6.5388 |
| No log | 47.94 | 720 | 6.5574 |
| No log | 48.94 | 735 | 6.5581 |
| No log | 49.94 | 750 | 6.5909 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
nubby/anime_multi-artist
|
nubby
| 2022-11-05T03:57:07Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2022-11-01T19:38:58Z |
---
license: creativeml-openrail-m
---
Waifu-Diffusion-v1-3 based StableDiffusion model with Dreambooth training on images from 3 different anime style artists. Trained to 17,000 steps using 155 total training images.
## Usage
Can be used in StableDiffusion, including the extremely popular Web UI by Automatic1111, like any other model by placing the .CKPT file in the correct directory. Please consult the documentation for your installation of StableDiffusion for more specific instructions.
Use ```"m_kgrartist"``` for kagura_tohru style, ```"m_ozdmartist"``` for ozadomi style, or ```"m_srartist"``` seero style in your prompt to invoke the style of the desired artist.
## Example images from ```"m_kgrartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/SIA7g2C.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/UbBsvZo.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/kMv5MH9.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/BiYihYs.png width=100% height=100%/></td>
</tr>
</table>
## Example images from ```"m_ozdmartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/t2UmHWa.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/LFrQsy6.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/DnHg1Kp.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/cXooD2r.png width=100% height=100%/></td>
</tr>
</table>
## Example images from ```"m_srartist"```
<table>
<tr>
<td><img src=https://i.imgur.com/0gsFN2H.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/aDJr8x6.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/AUafGCd.png width=100% height=100%/></td>
<td><img src=https://i.imgur.com/va246Yv.png width=100% height=100%/></td>
</tr>
</table>
## License
This model is open access and available to all, with a CreativeML OpenRAIL-M license further specifying rights and usage.
The CreativeML OpenRAIL License specifies:
1. You can't use the model to deliberately produce nor share illegal or harmful outputs or content
2. The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
3. You may re-distribute the weights and use the model commercially and/or as a service. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully)
[Please read the full license here](https://huggingface.co/spaces/CompVis/stable-diffusion-license)
|
MarkGG/Romance-cleaned-1
|
MarkGG
| 2022-11-05T03:10:38Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tf",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-10-26T03:35:43Z |
---
license: mit
tags:
- generated_from_trainer
model-index:
- name: Romance-cleaned-1
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. -->
# Romance-cleaned-1
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7175
## 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: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.97 | 29 | 9.9497 |
| No log | 1.97 | 58 | 9.1816 |
| No log | 2.97 | 87 | 8.5947 |
| No log | 3.97 | 116 | 8.2217 |
| No log | 4.97 | 145 | 7.8354 |
| No log | 5.97 | 174 | 7.5075 |
| No log | 6.97 | 203 | 7.2112 |
| No log | 7.97 | 232 | 6.9077 |
| No log | 8.97 | 261 | 6.5994 |
| No log | 9.97 | 290 | 6.3077 |
| No log | 10.97 | 319 | 6.0416 |
| No log | 11.97 | 348 | 5.8126 |
| No log | 12.97 | 377 | 5.6197 |
| No log | 13.97 | 406 | 5.4789 |
| No log | 14.97 | 435 | 5.3665 |
| No log | 15.97 | 464 | 5.2738 |
| No log | 16.97 | 493 | 5.1942 |
| No log | 17.97 | 522 | 5.1382 |
| No log | 18.97 | 551 | 5.0784 |
| No log | 19.97 | 580 | 5.0347 |
| No log | 20.97 | 609 | 4.9873 |
| No log | 21.97 | 638 | 4.9514 |
| No log | 22.97 | 667 | 4.9112 |
| No log | 23.97 | 696 | 4.8838 |
| No log | 24.97 | 725 | 4.8468 |
| No log | 25.97 | 754 | 4.8221 |
| No log | 26.97 | 783 | 4.7996 |
| No log | 27.97 | 812 | 4.7815 |
| No log | 28.97 | 841 | 4.7606 |
| No log | 29.97 | 870 | 4.7394 |
| No log | 30.97 | 899 | 4.7167 |
| No log | 31.97 | 928 | 4.7140 |
| No log | 32.97 | 957 | 4.6910 |
| No log | 33.97 | 986 | 4.6844 |
| No log | 34.97 | 1015 | 4.6765 |
| No log | 35.97 | 1044 | 4.6687 |
| No log | 36.97 | 1073 | 4.6721 |
| No log | 37.97 | 1102 | 4.6724 |
| No log | 38.97 | 1131 | 4.6629 |
| No log | 39.97 | 1160 | 4.6772 |
| No log | 40.97 | 1189 | 4.6795 |
| No log | 41.97 | 1218 | 4.6788 |
| No log | 42.97 | 1247 | 4.6832 |
| No log | 43.97 | 1276 | 4.6954 |
| No log | 44.97 | 1305 | 4.7009 |
| No log | 45.97 | 1334 | 4.7082 |
| No log | 46.97 | 1363 | 4.7140 |
| No log | 47.97 | 1392 | 4.7158 |
| No log | 48.97 | 1421 | 4.7181 |
| No log | 49.97 | 1450 | 4.7175 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
huggingtweets/transgirltoking
|
huggingtweets
| 2022-11-05T02:57:28Z | 103 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T02:56:05Z |
---
language: en
thumbnail: http://www.huggingtweets.com/transgirltoking/1667617044734/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/1587630117890949121/Uo9ukfaP_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">fallmoder</div>
<div style="text-align: center; font-size: 14px;">@transgirltoking</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 fallmoder.
| Data | fallmoder |
| --- | --- |
| Tweets downloaded | 950 |
| Retweets | 280 |
| Short tweets | 97 |
| Tweets kept | 573 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/279zhs1a/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 @transgirltoking's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/ipbrk4ae) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/ipbrk4ae/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/transgirltoking')
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)
|
huggingtweets/00daniponie
|
huggingtweets
| 2022-11-05T01:51:32Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T01:09:38Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1495719135858233345/0T3aMUoa_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">dani little ponie 🏳️⚧️🐀</div>
<div style="text-align: center; font-size: 14px;">@00daniponie</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 dani little ponie 🏳️⚧️🐀.
| Data | dani little ponie 🏳️⚧️🐀 |
| --- | --- |
| Tweets downloaded | 3227 |
| Retweets | 1904 |
| Short tweets | 56 |
| Tweets kept | 1267 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/3cbrld7j/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 @00daniponie's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/39w151kw) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/39w151kw/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/00daniponie')
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)
|
hazrulakmal/distilgpt2-ecb-finetuned
|
hazrulakmal
| 2022-11-05T01:25:33Z | 15 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-03T19:14:53Z |
---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-ecb-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. -->
# distilgpt2-ecb-finetuned
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8705
## 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: 0.001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.9655 | 1.0 | 17714 | 0.9472 |
| 0.9121 | 2.0 | 35428 | 0.8986 |
| 0.8682 | 3.0 | 53142 | 0.8705 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
Nanohana/efficietnet-lstm-image-captioning
|
Nanohana
| 2022-11-05T00:28:47Z | 0 | 0 | null |
[
"region:us"
] | null | 2022-11-04T22:51:32Z |
---
title: {{image-captioning}}
sdk: {{gradio}}
app_file: app.py
---
# image-captioning
This repository contains an image captioning system that is composed of:
- Pretrained EfficientNet-B0 in ImageNet
- Word Embedding with Flickr8k vocabulary
- 1 layer LSTM
It was trained for 100 epoches (CNN weights were frozen) and the vocabulary was built with words that appear at least 5 times in the Flickr8k dataset.
|
huggingtweets/hellgirl2004
|
huggingtweets
| 2022-11-05T00:11:47Z | 105 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2022-11-05T00:11:39Z |
---
language: en
thumbnail: https://github.com/borisdayma/huggingtweets/blob/master/img/logo.png?raw=true
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1581781821414686722/lvOpNTQf_400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">🎃 rei 💀</div>
<div style="text-align: center; font-size: 14px;">@hellgirl2004</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 🎃 rei 💀.
| Data | 🎃 rei 💀 |
| --- | --- |
| Tweets downloaded | 3168 |
| Retweets | 1517 |
| Short tweets | 584 |
| Tweets kept | 1067 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/m0ohu4nr/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 @hellgirl2004's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/3mcqxcff) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/3mcqxcff/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/hellgirl2004')
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)
|
mpjan/msmarco-distilbert-base-tas-b-mmarco-pt-300k
|
mpjan
| 2022-11-05T00:08:25Z | 8 | 4 |
sentence-transformers
|
[
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"pt",
"dataset:unicamp-dl/mmarco",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2022-11-05T00:03:16Z |
---
pipeline_tag: sentence-similarity
language:
- 'pt'
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
datasets:
- 'unicamp-dl/mmarco'
---
# mpjan/msmarco-distilbert-base-tas-b-mmarco-pt-300k
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.
It is a fine-tuning of [sentence-transformers/msmarco-distilbert-base-tas-b](https://huggingface.co/sentence-transformers/msmarco-distilbert-base-tas-b) on the first 300k triplets of the Portuguese subset in [unicamp-dl/mmarco](https://huggingface.co/datasets/unicamp-dl/mmarco).
<!--- 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('mpjan/msmarco-distilbert-base-tas-b-mmarco-pt-300k')
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('mpjan/msmarco-distilbert-base-tas-b-mmarco-pt-300k')
model = AutoModel.from_pretrained('mpjan/msmarco-distilbert-base-tas-b-mmarco-pt-300k')
# 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 18750 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 5,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 9375,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(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 -->
|
jinhybr/OCR-Donut-CORD
|
jinhybr
| 2022-11-05T00:07:44Z | 1,087 | 199 |
transformers
|
[
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-text-to-text",
"donut",
"image-to-text",
"vision",
"arxiv:2111.15664",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-to-text
| 2022-11-04T13:22:17Z |
---
license: mit
tags:
- donut
- image-to-text
- vision
---
# Donut (base-sized model, fine-tuned on CORD)
Donut model fine-tuned on CORD. It was introduced in the paper [OCR-free Document Understanding Transformer](https://arxiv.org/abs/2111.15664) by Geewok et al. and first released in [this repository](https://github.com/clovaai/donut).
Disclaimer: The team releasing Donut did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
Donut consists of a vision encoder (Swin Transformer) and a text decoder (BART). Given an image, the encoder first encodes the image into a tensor of embeddings (of shape batch_size, seq_len, hidden_size), after which the decoder autoregressively generates text, conditioned on the encoding of the encoder.
## Intended uses & limitations
This model is fine-tuned on CORD, a document parsing dataset.
We refer to the [documentation](https://huggingface.co/docs/transformers/main/en/model_doc/donut) which includes code examples.
## CORD Dataset
CORD: A Consolidated Receipt Dataset for Post-OCR Parsing.
|
username23231/_
|
username23231
| 2022-11-05T00:07:18Z | 0 | 2 | null |
[
"license:bigscience-bloom-rail-1.0",
"region:us"
] | null | 2022-11-05T00:07:18Z |
---
license: bigscience-bloom-rail-1.0
---
|
lilouuch/mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
|
lilouuch
| 2022-11-04T23:46:53Z | 16 | 1 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"encoder-decoder",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2022-11-04T19:53:43Z |
---
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
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. -->
# mbert2mbert-arabic-text-summarization-finetuned-xsum_arabic_abstractive_final_finaln
This model is a fine-tuned version of [malmarjeh/mbert2mbert-arabic-text-summarization](https://huggingface.co/malmarjeh/mbert2mbert-arabic-text-summarization) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2826
- Rouge1: 0.0119
- Rouge2: 0.0
- Rougel: 0.0119
- Rougelsum: 0.0119
- Gen Len: 41.8856
## 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: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 2.5104 | 1.0 | 7915 | 2.3684 | 0.0 | 0.0 | 0.0 | 0.0 | 41.8314 |
| 2.2222 | 2.0 | 15830 | 2.2826 | 0.0119 | 0.0 | 0.0119 | 0.0119 | 41.8856 |
### Framework versions
- Transformers 4.20.1
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
|
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