modelId
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Dazai/Ok | [] | null | {
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} | 0 | null | ---
thumbnail: https://huggingface.co/front/thumbnails/dialogpt.png
tags:
- conversational
license: mit
---
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-medium) trained on a game character, Joshua from [The World Ends With You](https://en.wikipedia.org/wiki/The_World_Ends_with_You). The data comes from [a Kaggle game script dataset](https://www.kaggle.com/ruolinzheng/twewy-game-script).
I built a Discord AI chatbot based on this model. [Check out my GitHub repo.](https://github.com/RuolinZheng08/twewy-discord-chatbot)
Chat with the model:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("scottastrong/DialogGPT-medium-joshua")
model = AutoModelWithLMHead.from_pretrained("scottastrong/DialogGPT-medium-joshua")
# Let's chat for 4 lines
for step in range(4):
# encode the new user input, add the eos_token and return a tensor in Pytorch
new_user_input_ids = tokenizer.encode(input(">> User:") + tokenizer.eos_token, return_tensors='pt')
# print(new_user_input_ids)
# append the new user input tokens to the chat history
bot_input_ids = torch.cat([chat_history_ids, new_user_input_ids], dim=-1) if step > 0 else new_user_input_ids
# generated a response while limiting the total chat history to 1000 tokens,
chat_history_ids = model.generate(
bot_input_ids, max_length=200,
pad_token_id=tokenizer.eos_token_id,
no_repeat_ngram_size=3,
do_sample=True,
top_k=100,
top_p=0.7,
temperature=0.8
)
# pretty print last ouput tokens from bot
print("JoshuaBot: {}".format(tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)))
``` |
Dean/summarsiation | [] | null | {
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} | 0 | null | ---
tags:
- conversational
---
# Sedged DialoGPT Model |
Declan/Breitbart_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
language: code
license: mit
tags:
- Code
- GPyT
- code generator
---
GPyT is a GPT2 model trained from scratch (not fine tuned) on Python code from Github. Overall, it was ~80GB of pure Python code, the current GPyT model is a mere 2 epochs through this data, so it may benefit greatly from continued training and/or fine-tuning.
Newlines are replaced by `<N>`
Input to the model is code, up to the context length of 1024, with newlines replaced by `<N>`
Here's a quick example of using this model:
```py
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("Sentdex/GPyT")
model = AutoModelWithLMHead.from_pretrained("Sentdex/GPyT")
# copy and paste some code in here
inp = """import"""
newlinechar = "<N>"
converted = inp.replace("\n", newlinechar)
tokenized = tokenizer.encode(converted, return_tensors='pt')
resp = model.generate(tokenized)
decoded = tokenizer.decode(resp[0])
reformatted = decoded.replace("<N>","\n")
print(reformatted)
```
Should produce:
```
import numpy as np
import pytest
import pandas as pd<N
```
This model does a ton more than just imports, however. For a bunch of examples and a better understanding of the model's capabilities:
https://pythonprogramming.net/GPT-python-code-transformer-model-GPyT/
Considerations:
1. This model is intended for educational and research use only. Do not trust model outputs.
2. Model is highly likely to regurgitate code almost exactly as it saw it. It's up to you to determine licensing if you intend to actually use the generated code.
3. All Python code was blindly pulled from github. This means included code is both Python 2 and 3, among other more subtle differences, such as tabs being 2 spaces in some cases and 4 in others...and more non-homologous things.
4. Along with the above, this means the code generated could wind up doing or suggesting just about anything. Run the generated code at own risk...it could be *anything*
|
Declan/CNN_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-base__sst2__all-train
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. -->
# deberta-v3-base__sst2__all-train
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6964
- Accuracy: 0.49
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 7 | 0.6964 | 0.49 |
| No log | 2.0 | 14 | 0.7010 | 0.49 |
| No log | 3.0 | 21 | 0.7031 | 0.49 |
| No log | 4.0 | 28 | 0.7054 | 0.49 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/CNN_model_v7 | [] | null | {
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} | 0 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-0
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. -->
# deberta-v3-large__sst2__train-16-0
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9917
- Accuracy: 0.7705
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7001 | 1.0 | 7 | 0.7327 | 0.2857 |
| 0.6326 | 2.0 | 14 | 0.6479 | 0.5714 |
| 0.5232 | 3.0 | 21 | 0.5714 | 0.5714 |
| 0.3313 | 4.0 | 28 | 0.6340 | 0.7143 |
| 0.3161 | 5.0 | 35 | 0.6304 | 0.7143 |
| 0.0943 | 6.0 | 42 | 0.4719 | 0.8571 |
| 0.0593 | 7.0 | 49 | 0.5000 | 0.7143 |
| 0.0402 | 8.0 | 56 | 0.3530 | 0.8571 |
| 0.0307 | 9.0 | 63 | 0.3499 | 0.8571 |
| 0.0033 | 10.0 | 70 | 0.3258 | 0.8571 |
| 0.0021 | 11.0 | 77 | 0.3362 | 0.8571 |
| 0.0012 | 12.0 | 84 | 0.4591 | 0.8571 |
| 0.0036 | 13.0 | 91 | 0.4661 | 0.8571 |
| 0.001 | 14.0 | 98 | 0.5084 | 0.8571 |
| 0.0017 | 15.0 | 105 | 0.5844 | 0.8571 |
| 0.0005 | 16.0 | 112 | 0.6645 | 0.8571 |
| 0.002 | 17.0 | 119 | 0.7422 | 0.8571 |
| 0.0006 | 18.0 | 126 | 0.7354 | 0.8571 |
| 0.0005 | 19.0 | 133 | 0.7265 | 0.8571 |
| 0.0005 | 20.0 | 140 | 0.7207 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/CNN_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-1
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. -->
# deberta-v3-large__sst2__train-16-1
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6804
- Accuracy: 0.5497
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7086 | 1.0 | 7 | 0.7176 | 0.2857 |
| 0.6897 | 2.0 | 14 | 0.7057 | 0.2857 |
| 0.6491 | 3.0 | 21 | 0.6582 | 0.8571 |
| 0.567 | 4.0 | 28 | 0.4480 | 0.8571 |
| 0.4304 | 5.0 | 35 | 0.5465 | 0.7143 |
| 0.0684 | 6.0 | 42 | 0.5408 | 0.8571 |
| 0.0339 | 7.0 | 49 | 0.6501 | 0.8571 |
| 0.0082 | 8.0 | 56 | 0.9152 | 0.8571 |
| 0.0067 | 9.0 | 63 | 2.5162 | 0.5714 |
| 0.0045 | 10.0 | 70 | 1.1136 | 0.8571 |
| 0.0012 | 11.0 | 77 | 1.1668 | 0.8571 |
| 0.0007 | 12.0 | 84 | 1.2071 | 0.8571 |
| 0.0005 | 13.0 | 91 | 1.2310 | 0.8571 |
| 0.0006 | 14.0 | 98 | 1.2476 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-16-2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6959
- Accuracy: 0.5008
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7079 | 1.0 | 7 | 0.7361 | 0.2857 |
| 0.6815 | 2.0 | 14 | 0.7659 | 0.2857 |
| 0.6938 | 3.0 | 21 | 0.7944 | 0.2857 |
| 0.4584 | 4.0 | 28 | 1.2441 | 0.2857 |
| 0.4949 | 5.0 | 35 | 1.2285 | 0.5714 |
| 0.0574 | 6.0 | 42 | 1.7796 | 0.5714 |
| 0.0156 | 7.0 | 49 | 2.6027 | 0.5714 |
| 0.0051 | 8.0 | 56 | 2.8717 | 0.5714 |
| 0.0017 | 9.0 | 63 | 2.8491 | 0.5714 |
| 0.0023 | 10.0 | 70 | 1.7149 | 0.7143 |
| 0.001 | 11.0 | 77 | 1.1101 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-3
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. -->
# deberta-v3-large__sst2__train-16-3
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6286
- Accuracy: 0.7068
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6955 | 1.0 | 7 | 0.7370 | 0.2857 |
| 0.6919 | 2.0 | 14 | 0.6855 | 0.4286 |
| 0.6347 | 3.0 | 21 | 0.5872 | 0.7143 |
| 0.4016 | 4.0 | 28 | 0.6644 | 0.7143 |
| 0.3097 | 5.0 | 35 | 0.5120 | 0.7143 |
| 0.0785 | 6.0 | 42 | 0.5845 | 0.7143 |
| 0.024 | 7.0 | 49 | 0.6951 | 0.7143 |
| 0.0132 | 8.0 | 56 | 0.8972 | 0.7143 |
| 0.0037 | 9.0 | 63 | 1.5798 | 0.7143 |
| 0.0034 | 10.0 | 70 | 1.5178 | 0.7143 |
| 0.003 | 11.0 | 77 | 1.3511 | 0.7143 |
| 0.0012 | 12.0 | 84 | 1.1346 | 0.7143 |
| 0.0007 | 13.0 | 91 | 0.9752 | 0.7143 |
| 0.0008 | 14.0 | 98 | 0.8531 | 0.7143 |
| 0.0007 | 15.0 | 105 | 0.8149 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-16-4
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6329
- Accuracy: 0.6392
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6945 | 1.0 | 7 | 0.7381 | 0.2857 |
| 0.7072 | 2.0 | 14 | 0.7465 | 0.2857 |
| 0.6548 | 3.0 | 21 | 0.7277 | 0.4286 |
| 0.5695 | 4.0 | 28 | 0.6738 | 0.5714 |
| 0.4615 | 5.0 | 35 | 0.8559 | 0.5714 |
| 0.0823 | 6.0 | 42 | 1.0983 | 0.5714 |
| 0.0274 | 7.0 | 49 | 1.9937 | 0.5714 |
| 0.0106 | 8.0 | 56 | 2.2209 | 0.5714 |
| 0.0039 | 9.0 | 63 | 2.2114 | 0.5714 |
| 0.0031 | 10.0 | 70 | 2.2808 | 0.5714 |
| 0.0013 | 11.0 | 77 | 2.3707 | 0.5714 |
| 0.0008 | 12.0 | 84 | 2.4902 | 0.5714 |
| 0.0005 | 13.0 | 91 | 2.5208 | 0.5714 |
| 0.0007 | 14.0 | 98 | 2.5683 | 0.5714 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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}
} | 5 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-7
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. -->
# deberta-v3-large__sst2__train-16-7
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6953
- Accuracy: 0.5063
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6911 | 1.0 | 7 | 0.7455 | 0.2857 |
| 0.6844 | 2.0 | 14 | 0.7242 | 0.2857 |
| 0.6137 | 3.0 | 21 | 0.7341 | 0.4286 |
| 0.3805 | 4.0 | 28 | 1.0217 | 0.4286 |
| 0.2201 | 5.0 | 35 | 1.1437 | 0.2857 |
| 0.0296 | 6.0 | 42 | 1.5997 | 0.4286 |
| 0.0103 | 7.0 | 49 | 2.6835 | 0.4286 |
| 0.0046 | 8.0 | 56 | 3.3521 | 0.4286 |
| 0.002 | 9.0 | 63 | 3.7846 | 0.4286 |
| 0.0017 | 10.0 | 70 | 4.0088 | 0.4286 |
| 0.0018 | 11.0 | 77 | 4.1483 | 0.4286 |
| 0.0006 | 12.0 | 84 | 4.2235 | 0.4286 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/ChicagoTribune_model_v7 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"translation_en_to_fr": {
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-16-8
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. -->
# deberta-v3-large__sst2__train-16-8
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6915
- Accuracy: 0.6579
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7129 | 1.0 | 7 | 0.7309 | 0.2857 |
| 0.6549 | 2.0 | 14 | 0.7316 | 0.4286 |
| 0.621 | 3.0 | 21 | 0.7131 | 0.5714 |
| 0.3472 | 4.0 | 28 | 0.5703 | 0.4286 |
| 0.2041 | 5.0 | 35 | 0.6675 | 0.5714 |
| 0.031 | 6.0 | 42 | 1.6750 | 0.5714 |
| 0.0141 | 7.0 | 49 | 1.8743 | 0.5714 |
| 0.0055 | 8.0 | 56 | 1.1778 | 0.5714 |
| 0.0024 | 9.0 | 63 | 1.0699 | 0.5714 |
| 0.0019 | 10.0 | 70 | 1.0933 | 0.5714 |
| 0.0012 | 11.0 | 77 | 1.1218 | 0.7143 |
| 0.0007 | 12.0 | 84 | 1.1468 | 0.7143 |
| 0.0006 | 13.0 | 91 | 1.1584 | 0.7143 |
| 0.0006 | 14.0 | 98 | 1.3092 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/FoxNews_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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},
"translation_en_to_de": {
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-32-1
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. -->
# deberta-v3-large__sst2__train-32-1
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4201
- Accuracy: 0.8759
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7162 | 1.0 | 13 | 0.6832 | 0.5385 |
| 0.6561 | 2.0 | 26 | 0.7270 | 0.4615 |
| 0.4685 | 3.0 | 39 | 1.0674 | 0.5385 |
| 0.2837 | 4.0 | 52 | 1.0841 | 0.5385 |
| 0.1129 | 5.0 | 65 | 0.3502 | 0.9231 |
| 0.0118 | 6.0 | 78 | 0.4829 | 0.9231 |
| 0.0022 | 7.0 | 91 | 0.7430 | 0.8462 |
| 0.0007 | 8.0 | 104 | 0.8219 | 0.8462 |
| 0.0005 | 9.0 | 117 | 0.8787 | 0.8462 |
| 0.0003 | 10.0 | 130 | 0.8713 | 0.8462 |
| 0.0003 | 11.0 | 143 | 0.8473 | 0.8462 |
| 0.0002 | 12.0 | 156 | 0.8482 | 0.8462 |
| 0.0002 | 13.0 | 169 | 0.8494 | 0.8462 |
| 0.0002 | 14.0 | 182 | 0.8638 | 0.8462 |
| 0.0002 | 15.0 | 195 | 0.8492 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/FoxNews_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
"conversational": {
"max_length": null
},
"summarization": {
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},
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-0
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. -->
# deberta-v3-large__sst2__train-8-0
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7088
- Accuracy: 0.5008
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6705 | 1.0 | 3 | 0.7961 | 0.25 |
| 0.6571 | 2.0 | 6 | 0.8092 | 0.25 |
| 0.7043 | 3.0 | 9 | 0.7977 | 0.25 |
| 0.6207 | 4.0 | 12 | 0.8478 | 0.25 |
| 0.5181 | 5.0 | 15 | 0.9782 | 0.25 |
| 0.4136 | 6.0 | 18 | 1.3151 | 0.25 |
| 0.3702 | 7.0 | 21 | 1.8633 | 0.25 |
| 0.338 | 8.0 | 24 | 2.2119 | 0.25 |
| 0.2812 | 9.0 | 27 | 2.3058 | 0.25 |
| 0.2563 | 10.0 | 30 | 2.3353 | 0.25 |
| 0.2132 | 11.0 | 33 | 2.5921 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/FoxNews_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 7 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-1
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. -->
# deberta-v3-large__sst2__train-8-1
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7020
- Accuracy: 0.5008
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6773 | 1.0 | 3 | 0.7822 | 0.25 |
| 0.6587 | 2.0 | 6 | 0.8033 | 0.25 |
| 0.693 | 3.0 | 9 | 0.8101 | 0.25 |
| 0.5979 | 4.0 | 12 | 1.1235 | 0.25 |
| 0.4095 | 5.0 | 15 | 1.3563 | 0.25 |
| 0.2836 | 6.0 | 18 | 1.5325 | 0.5 |
| 0.1627 | 7.0 | 21 | 1.7786 | 0.25 |
| 0.0956 | 8.0 | 24 | 2.0067 | 0.5 |
| 0.0535 | 9.0 | 27 | 2.3351 | 0.5 |
| 0.0315 | 10.0 | 30 | 2.6204 | 0.5 |
| 0.0182 | 11.0 | 33 | 2.8483 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/FoxNews_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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"translation_en_to_fr": {
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-3
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. -->
# deberta-v3-large__sst2__train-8-3
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6421
- Accuracy: 0.6310
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6696 | 1.0 | 3 | 0.7917 | 0.25 |
| 0.6436 | 2.0 | 6 | 0.8107 | 0.25 |
| 0.6923 | 3.0 | 9 | 0.8302 | 0.25 |
| 0.5051 | 4.0 | 12 | 0.9828 | 0.25 |
| 0.3688 | 5.0 | 15 | 0.7402 | 0.25 |
| 0.2671 | 6.0 | 18 | 0.5820 | 0.75 |
| 0.1935 | 7.0 | 21 | 0.8356 | 0.5 |
| 0.0815 | 8.0 | 24 | 1.0431 | 0.25 |
| 0.0591 | 9.0 | 27 | 0.9679 | 0.75 |
| 0.0276 | 10.0 | 30 | 1.0659 | 0.75 |
| 0.0175 | 11.0 | 33 | 0.9689 | 0.75 |
| 0.0152 | 12.0 | 36 | 0.8820 | 0.75 |
| 0.006 | 13.0 | 39 | 0.8337 | 0.75 |
| 0.0041 | 14.0 | 42 | 0.7650 | 0.75 |
| 0.0036 | 15.0 | 45 | 0.6960 | 0.75 |
| 0.0034 | 16.0 | 48 | 0.6548 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/HuffPost_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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},
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-v3-large__sst2__train-8-5
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3078
- Accuracy: 0.6930
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6813 | 1.0 | 3 | 0.7842 | 0.25 |
| 0.6617 | 2.0 | 6 | 0.7968 | 0.25 |
| 0.6945 | 3.0 | 9 | 0.7746 | 0.25 |
| 0.5967 | 4.0 | 12 | 0.7557 | 0.25 |
| 0.4824 | 5.0 | 15 | 0.6920 | 0.25 |
| 0.3037 | 6.0 | 18 | 0.6958 | 0.5 |
| 0.2329 | 7.0 | 21 | 0.6736 | 0.5 |
| 0.1441 | 8.0 | 24 | 0.3749 | 1.0 |
| 0.0875 | 9.0 | 27 | 0.3263 | 0.75 |
| 0.0655 | 10.0 | 30 | 0.3525 | 0.75 |
| 0.0373 | 11.0 | 33 | 0.1993 | 1.0 |
| 0.0173 | 12.0 | 36 | 0.1396 | 1.0 |
| 0.0147 | 13.0 | 39 | 0.0655 | 1.0 |
| 0.0084 | 14.0 | 42 | 0.0343 | 1.0 |
| 0.0049 | 15.0 | 45 | 0.0225 | 1.0 |
| 0.004 | 16.0 | 48 | 0.0167 | 1.0 |
| 0.003 | 17.0 | 51 | 0.0134 | 1.0 |
| 0.0027 | 18.0 | 54 | 0.0114 | 1.0 |
| 0.002 | 19.0 | 57 | 0.0104 | 1.0 |
| 0.0015 | 20.0 | 60 | 0.0099 | 1.0 |
| 0.0014 | 21.0 | 63 | 0.0095 | 1.0 |
| 0.0013 | 22.0 | 66 | 0.0095 | 1.0 |
| 0.0012 | 23.0 | 69 | 0.0091 | 1.0 |
| 0.0011 | 24.0 | 72 | 0.0085 | 1.0 |
| 0.0009 | 25.0 | 75 | 0.0081 | 1.0 |
| 0.001 | 26.0 | 78 | 0.0077 | 1.0 |
| 0.0008 | 27.0 | 81 | 0.0074 | 1.0 |
| 0.0009 | 28.0 | 84 | 0.0071 | 1.0 |
| 0.0007 | 29.0 | 87 | 0.0068 | 1.0 |
| 0.0008 | 30.0 | 90 | 0.0064 | 1.0 |
| 0.0007 | 31.0 | 93 | 0.0062 | 1.0 |
| 0.0007 | 32.0 | 96 | 0.0059 | 1.0 |
| 0.0007 | 33.0 | 99 | 0.0056 | 1.0 |
| 0.0005 | 34.0 | 102 | 0.0054 | 1.0 |
| 0.0006 | 35.0 | 105 | 0.0053 | 1.0 |
| 0.0008 | 36.0 | 108 | 0.0051 | 1.0 |
| 0.0007 | 37.0 | 111 | 0.0050 | 1.0 |
| 0.0007 | 38.0 | 114 | 0.0049 | 1.0 |
| 0.0006 | 39.0 | 117 | 0.0048 | 1.0 |
| 0.0005 | 40.0 | 120 | 0.0048 | 1.0 |
| 0.0005 | 41.0 | 123 | 0.0048 | 1.0 |
| 0.0005 | 42.0 | 126 | 0.0047 | 1.0 |
| 0.0005 | 43.0 | 129 | 0.0047 | 1.0 |
| 0.0005 | 44.0 | 132 | 0.0047 | 1.0 |
| 0.0006 | 45.0 | 135 | 0.0047 | 1.0 |
| 0.0005 | 46.0 | 138 | 0.0047 | 1.0 |
| 0.0005 | 47.0 | 141 | 0.0047 | 1.0 |
| 0.0006 | 48.0 | 144 | 0.0047 | 1.0 |
| 0.0005 | 49.0 | 147 | 0.0047 | 1.0 |
| 0.0005 | 50.0 | 150 | 0.0047 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/HuffPost_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-6
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. -->
# deberta-v3-large__sst2__train-8-6
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4331
- Accuracy: 0.7106
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6486 | 1.0 | 3 | 0.7901 | 0.25 |
| 0.6418 | 2.0 | 6 | 0.9259 | 0.25 |
| 0.6169 | 3.0 | 9 | 1.0574 | 0.25 |
| 0.5639 | 4.0 | 12 | 1.1372 | 0.25 |
| 0.4562 | 5.0 | 15 | 0.6090 | 0.5 |
| 0.3105 | 6.0 | 18 | 0.4435 | 1.0 |
| 0.2303 | 7.0 | 21 | 0.2804 | 1.0 |
| 0.1388 | 8.0 | 24 | 0.2205 | 1.0 |
| 0.0918 | 9.0 | 27 | 0.1282 | 1.0 |
| 0.0447 | 10.0 | 30 | 0.0643 | 1.0 |
| 0.0297 | 11.0 | 33 | 0.0361 | 1.0 |
| 0.0159 | 12.0 | 36 | 0.0211 | 1.0 |
| 0.0102 | 13.0 | 39 | 0.0155 | 1.0 |
| 0.0061 | 14.0 | 42 | 0.0158 | 1.0 |
| 0.0049 | 15.0 | 45 | 0.0189 | 1.0 |
| 0.0035 | 16.0 | 48 | 0.0254 | 1.0 |
| 0.0027 | 17.0 | 51 | 0.0305 | 1.0 |
| 0.0021 | 18.0 | 54 | 0.0287 | 1.0 |
| 0.0016 | 19.0 | 57 | 0.0215 | 1.0 |
| 0.0016 | 20.0 | 60 | 0.0163 | 1.0 |
| 0.0014 | 21.0 | 63 | 0.0138 | 1.0 |
| 0.0015 | 22.0 | 66 | 0.0131 | 1.0 |
| 0.001 | 23.0 | 69 | 0.0132 | 1.0 |
| 0.0014 | 24.0 | 72 | 0.0126 | 1.0 |
| 0.0011 | 25.0 | 75 | 0.0125 | 1.0 |
| 0.001 | 26.0 | 78 | 0.0119 | 1.0 |
| 0.0008 | 27.0 | 81 | 0.0110 | 1.0 |
| 0.0007 | 28.0 | 84 | 0.0106 | 1.0 |
| 0.0008 | 29.0 | 87 | 0.0095 | 1.0 |
| 0.0009 | 30.0 | 90 | 0.0089 | 1.0 |
| 0.0008 | 31.0 | 93 | 0.0083 | 1.0 |
| 0.0007 | 32.0 | 96 | 0.0075 | 1.0 |
| 0.0008 | 33.0 | 99 | 0.0066 | 1.0 |
| 0.0006 | 34.0 | 102 | 0.0059 | 1.0 |
| 0.0007 | 35.0 | 105 | 0.0054 | 1.0 |
| 0.0008 | 36.0 | 108 | 0.0051 | 1.0 |
| 0.0007 | 37.0 | 111 | 0.0049 | 1.0 |
| 0.0007 | 38.0 | 114 | 0.0047 | 1.0 |
| 0.0006 | 39.0 | 117 | 0.0045 | 1.0 |
| 0.0006 | 40.0 | 120 | 0.0046 | 1.0 |
| 0.0005 | 41.0 | 123 | 0.0045 | 1.0 |
| 0.0006 | 42.0 | 126 | 0.0044 | 1.0 |
| 0.0006 | 43.0 | 129 | 0.0043 | 1.0 |
| 0.0006 | 44.0 | 132 | 0.0044 | 1.0 |
| 0.0005 | 45.0 | 135 | 0.0045 | 1.0 |
| 0.0006 | 46.0 | 138 | 0.0043 | 1.0 |
| 0.0006 | 47.0 | 141 | 0.0043 | 1.0 |
| 0.0006 | 48.0 | 144 | 0.0041 | 1.0 |
| 0.0007 | 49.0 | 147 | 0.0042 | 1.0 |
| 0.0005 | 50.0 | 150 | 0.0042 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/HuffPost_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-7
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. -->
# deberta-v3-large__sst2__train-8-7
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7037
- Accuracy: 0.5008
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6864 | 1.0 | 3 | 0.7800 | 0.25 |
| 0.6483 | 2.0 | 6 | 0.8067 | 0.25 |
| 0.6028 | 3.0 | 9 | 0.8500 | 0.25 |
| 0.4086 | 4.0 | 12 | 1.0661 | 0.25 |
| 0.2923 | 5.0 | 15 | 1.2302 | 0.25 |
| 0.2059 | 6.0 | 18 | 1.0312 | 0.5 |
| 0.1238 | 7.0 | 21 | 1.1271 | 0.5 |
| 0.0711 | 8.0 | 24 | 1.3100 | 0.5 |
| 0.0453 | 9.0 | 27 | 1.4208 | 0.5 |
| 0.0198 | 10.0 | 30 | 1.5988 | 0.5 |
| 0.0135 | 11.0 | 33 | 1.9174 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/HuffPost_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
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} | 3 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-8
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. -->
# deberta-v3-large__sst2__train-8-8
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7414
- Accuracy: 0.5623
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6597 | 1.0 | 3 | 0.7716 | 0.25 |
| 0.6376 | 2.0 | 6 | 0.7802 | 0.25 |
| 0.5857 | 3.0 | 9 | 0.6625 | 0.75 |
| 0.4024 | 4.0 | 12 | 0.5195 | 0.75 |
| 0.2635 | 5.0 | 15 | 0.4222 | 1.0 |
| 0.1714 | 6.0 | 18 | 0.4410 | 0.5 |
| 0.1267 | 7.0 | 21 | 0.7773 | 0.75 |
| 0.0582 | 8.0 | 24 | 0.9070 | 0.75 |
| 0.0374 | 9.0 | 27 | 0.9539 | 0.75 |
| 0.0204 | 10.0 | 30 | 1.0507 | 0.75 |
| 0.012 | 11.0 | 33 | 1.2802 | 0.5 |
| 0.0086 | 12.0 | 36 | 1.4272 | 0.5 |
| 0.0049 | 13.0 | 39 | 1.4803 | 0.5 |
| 0.0039 | 14.0 | 42 | 1.4912 | 0.5 |
| 0.0031 | 15.0 | 45 | 1.5231 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/HuffPost_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 9 | null | ---
license: mit
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: deberta-v3-large__sst2__train-8-9
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. -->
# deberta-v3-large__sst2__train-8-9
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6013
- Accuracy: 0.7210
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6757 | 1.0 | 3 | 0.7810 | 0.25 |
| 0.6506 | 2.0 | 6 | 0.8102 | 0.25 |
| 0.6463 | 3.0 | 9 | 0.8313 | 0.25 |
| 0.5813 | 4.0 | 12 | 0.8858 | 0.25 |
| 0.4635 | 5.0 | 15 | 0.8220 | 0.25 |
| 0.3992 | 6.0 | 18 | 0.7226 | 0.5 |
| 0.3281 | 7.0 | 21 | 0.6707 | 0.75 |
| 0.2276 | 8.0 | 24 | 0.7515 | 0.75 |
| 0.1674 | 9.0 | 27 | 0.6971 | 0.75 |
| 0.0873 | 10.0 | 30 | 0.5419 | 0.75 |
| 0.0525 | 11.0 | 33 | 0.5025 | 0.75 |
| 0.0286 | 12.0 | 36 | 0.5229 | 0.75 |
| 0.0149 | 13.0 | 39 | 0.5660 | 0.75 |
| 0.0082 | 14.0 | 42 | 0.6954 | 0.75 |
| 0.006 | 15.0 | 45 | 0.8649 | 0.75 |
| 0.0043 | 16.0 | 48 | 1.0011 | 0.75 |
| 0.0035 | 17.0 | 51 | 1.0909 | 0.75 |
| 0.0021 | 18.0 | 54 | 1.1615 | 0.75 |
| 0.0017 | 19.0 | 57 | 1.2147 | 0.75 |
| 0.0013 | 20.0 | 60 | 1.2585 | 0.75 |
| 0.0016 | 21.0 | 63 | 1.2917 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/NPR_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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},
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-1
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. -->
# distilbert-base-uncased__hate_speech_offensive__train-16-1
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: 1.0424
- Accuracy: 0.5355
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0989 | 1.0 | 10 | 1.1049 | 0.1 |
| 1.0641 | 2.0 | 20 | 1.0768 | 0.3 |
| 0.9742 | 3.0 | 30 | 1.0430 | 0.4 |
| 0.8765 | 4.0 | 40 | 1.0058 | 0.4 |
| 0.6979 | 5.0 | 50 | 0.8488 | 0.7 |
| 0.563 | 6.0 | 60 | 0.7221 | 0.7 |
| 0.4135 | 7.0 | 70 | 0.6587 | 0.8 |
| 0.2509 | 8.0 | 80 | 0.5577 | 0.7 |
| 0.0943 | 9.0 | 90 | 0.5840 | 0.7 |
| 0.0541 | 10.0 | 100 | 0.6959 | 0.7 |
| 0.0362 | 11.0 | 110 | 0.6884 | 0.6 |
| 0.0254 | 12.0 | 120 | 0.9263 | 0.6 |
| 0.0184 | 13.0 | 130 | 0.7992 | 0.6 |
| 0.0172 | 14.0 | 140 | 0.7351 | 0.6 |
| 0.0131 | 15.0 | 150 | 0.7664 | 0.6 |
| 0.0117 | 16.0 | 160 | 0.8262 | 0.6 |
| 0.0101 | 17.0 | 170 | 0.8839 | 0.6 |
| 0.0089 | 18.0 | 180 | 0.9018 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-16-5
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: 0.9907
- Accuracy: 0.49
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0941 | 1.0 | 10 | 1.1287 | 0.2 |
| 1.0481 | 2.0 | 20 | 1.1136 | 0.2 |
| 0.9498 | 3.0 | 30 | 1.1200 | 0.2 |
| 0.8157 | 4.0 | 40 | 1.0771 | 0.2 |
| 0.65 | 5.0 | 50 | 0.9733 | 0.4 |
| 0.5021 | 6.0 | 60 | 1.0626 | 0.4 |
| 0.3358 | 7.0 | 70 | 1.0787 | 0.4 |
| 0.2017 | 8.0 | 80 | 1.3183 | 0.4 |
| 0.088 | 9.0 | 90 | 1.2204 | 0.5 |
| 0.0527 | 10.0 | 100 | 1.6892 | 0.4 |
| 0.0337 | 11.0 | 110 | 1.6967 | 0.5 |
| 0.0238 | 12.0 | 120 | 1.5436 | 0.5 |
| 0.0183 | 13.0 | 130 | 1.7447 | 0.4 |
| 0.0159 | 14.0 | 140 | 1.8999 | 0.4 |
| 0.014 | 15.0 | 150 | 1.9004 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-6
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__hate_speech_offensive__train-16-6
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: 0.8331
- Accuracy: 0.625
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0881 | 1.0 | 10 | 1.1248 | 0.1 |
| 1.0586 | 2.0 | 20 | 1.1162 | 0.2 |
| 0.9834 | 3.0 | 30 | 1.1199 | 0.3 |
| 0.9271 | 4.0 | 40 | 1.0740 | 0.3 |
| 0.7663 | 5.0 | 50 | 1.0183 | 0.5 |
| 0.6042 | 6.0 | 60 | 1.0259 | 0.5 |
| 0.4482 | 7.0 | 70 | 0.8699 | 0.7 |
| 0.3072 | 8.0 | 80 | 1.0615 | 0.5 |
| 0.1458 | 9.0 | 90 | 1.0164 | 0.5 |
| 0.0838 | 10.0 | 100 | 1.0620 | 0.5 |
| 0.055 | 11.0 | 110 | 1.1829 | 0.5 |
| 0.0347 | 12.0 | 120 | 1.2815 | 0.4 |
| 0.0244 | 13.0 | 130 | 1.2607 | 0.6 |
| 0.0213 | 14.0 | 140 | 1.3695 | 0.5 |
| 0.0169 | 15.0 | 150 | 1.4397 | 0.5 |
| 0.0141 | 16.0 | 160 | 1.4388 | 0.6 |
| 0.0122 | 17.0 | 170 | 1.4242 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/NewYorkTimes_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-7
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__hate_speech_offensive__train-16-7
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: 0.9011
- Accuracy: 0.578
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0968 | 1.0 | 10 | 1.1309 | 0.0 |
| 1.0709 | 2.0 | 20 | 1.1237 | 0.1 |
| 0.9929 | 3.0 | 30 | 1.1254 | 0.1 |
| 0.878 | 4.0 | 40 | 1.1206 | 0.5 |
| 0.7409 | 5.0 | 50 | 1.0831 | 0.1 |
| 0.5663 | 6.0 | 60 | 0.9830 | 0.6 |
| 0.4105 | 7.0 | 70 | 0.9919 | 0.5 |
| 0.2912 | 8.0 | 80 | 1.0472 | 0.6 |
| 0.1013 | 9.0 | 90 | 1.1617 | 0.4 |
| 0.0611 | 10.0 | 100 | 1.2789 | 0.6 |
| 0.039 | 11.0 | 110 | 1.4091 | 0.4 |
| 0.0272 | 12.0 | 120 | 1.4974 | 0.4 |
| 0.0189 | 13.0 | 130 | 1.4845 | 0.5 |
| 0.018 | 14.0 | 140 | 1.4924 | 0.5 |
| 0.0131 | 15.0 | 150 | 1.5206 | 0.6 |
| 0.0116 | 16.0 | 160 | 1.5858 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-8
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__hate_speech_offensive__train-16-8
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: 1.0704
- Accuracy: 0.394
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1031 | 1.0 | 10 | 1.1286 | 0.1 |
| 1.0648 | 2.0 | 20 | 1.1157 | 0.3 |
| 0.9982 | 3.0 | 30 | 1.1412 | 0.2 |
| 0.9283 | 4.0 | 40 | 1.2053 | 0.2 |
| 0.7958 | 5.0 | 50 | 1.1466 | 0.2 |
| 0.6668 | 6.0 | 60 | 1.1783 | 0.3 |
| 0.5068 | 7.0 | 70 | 1.2992 | 0.3 |
| 0.3741 | 8.0 | 80 | 1.3483 | 0.3 |
| 0.1653 | 9.0 | 90 | 1.4533 | 0.2 |
| 0.0946 | 10.0 | 100 | 1.6292 | 0.2 |
| 0.0569 | 11.0 | 110 | 1.8381 | 0.2 |
| 0.0346 | 12.0 | 120 | 2.0781 | 0.2 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-16-9
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__hate_speech_offensive__train-16-9
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: 1.1121
- Accuracy: 0.16
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1038 | 1.0 | 10 | 1.1243 | 0.1 |
| 1.0859 | 2.0 | 20 | 1.1182 | 0.2 |
| 1.0234 | 3.0 | 30 | 1.1442 | 0.3 |
| 0.9493 | 4.0 | 40 | 1.2239 | 0.1 |
| 0.8114 | 5.0 | 50 | 1.2023 | 0.4 |
| 0.6464 | 6.0 | 60 | 1.2329 | 0.4 |
| 0.4731 | 7.0 | 70 | 1.2971 | 0.5 |
| 0.3355 | 8.0 | 80 | 1.3913 | 0.4 |
| 0.1268 | 9.0 | 90 | 1.4670 | 0.5 |
| 0.0747 | 10.0 | 100 | 1.7961 | 0.4 |
| 0.0449 | 11.0 | 110 | 1.8168 | 0.5 |
| 0.0307 | 12.0 | 120 | 1.9307 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/NewYorkTimes_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-0
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__hate_speech_offensive__train-32-0
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: 0.7714
- Accuracy: 0.705
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0871 | 1.0 | 19 | 1.0704 | 0.45 |
| 1.0019 | 2.0 | 38 | 1.0167 | 0.55 |
| 0.8412 | 3.0 | 57 | 0.9134 | 0.55 |
| 0.6047 | 4.0 | 76 | 0.8430 | 0.6 |
| 0.3746 | 5.0 | 95 | 0.8315 | 0.6 |
| 0.1885 | 6.0 | 114 | 0.8585 | 0.6 |
| 0.0772 | 7.0 | 133 | 0.9443 | 0.65 |
| 0.0312 | 8.0 | 152 | 1.1019 | 0.65 |
| 0.0161 | 9.0 | 171 | 1.1420 | 0.65 |
| 0.0102 | 10.0 | 190 | 1.2773 | 0.65 |
| 0.0077 | 11.0 | 209 | 1.2454 | 0.65 |
| 0.0064 | 12.0 | 228 | 1.2785 | 0.65 |
| 0.006 | 13.0 | 247 | 1.3834 | 0.65 |
| 0.0045 | 14.0 | 266 | 1.4139 | 0.65 |
| 0.0043 | 15.0 | 285 | 1.4056 | 0.65 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/NewYorkTimes_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-1
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. -->
# distilbert-base-uncased__hate_speech_offensive__train-32-1
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: 1.0606
- Accuracy: 0.4745
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0941 | 1.0 | 19 | 1.1045 | 0.2 |
| 0.9967 | 2.0 | 38 | 1.1164 | 0.35 |
| 0.8164 | 3.0 | 57 | 1.1570 | 0.4 |
| 0.5884 | 4.0 | 76 | 1.2403 | 0.35 |
| 0.3322 | 5.0 | 95 | 1.3815 | 0.35 |
| 0.156 | 6.0 | 114 | 1.8102 | 0.3 |
| 0.0576 | 7.0 | 133 | 2.1439 | 0.4 |
| 0.0227 | 8.0 | 152 | 2.4368 | 0.3 |
| 0.0133 | 9.0 | 171 | 2.5994 | 0.4 |
| 0.009 | 10.0 | 190 | 2.7388 | 0.35 |
| 0.0072 | 11.0 | 209 | 2.8287 | 0.35 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Politico_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-32-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7136
- Accuracy: 0.679
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1052 | 1.0 | 19 | 1.0726 | 0.45 |
| 1.0421 | 2.0 | 38 | 1.0225 | 0.5 |
| 0.9173 | 3.0 | 57 | 0.9164 | 0.6 |
| 0.6822 | 4.0 | 76 | 0.8251 | 0.7 |
| 0.4407 | 5.0 | 95 | 0.8908 | 0.5 |
| 0.2367 | 6.0 | 114 | 0.6772 | 0.75 |
| 0.1145 | 7.0 | 133 | 0.7792 | 0.65 |
| 0.0479 | 8.0 | 152 | 1.0657 | 0.6 |
| 0.0186 | 9.0 | 171 | 1.2228 | 0.65 |
| 0.0111 | 10.0 | 190 | 1.1100 | 0.6 |
| 0.0083 | 11.0 | 209 | 1.1991 | 0.65 |
| 0.0067 | 12.0 | 228 | 1.2654 | 0.65 |
| 0.0061 | 13.0 | 247 | 1.2837 | 0.65 |
| 0.0046 | 14.0 | 266 | 1.2860 | 0.6 |
| 0.0043 | 15.0 | 285 | 1.3160 | 0.65 |
| 0.0037 | 16.0 | 304 | 1.3323 | 0.65 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Politico_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-3
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__hate_speech_offensive__train-32-3
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: 0.8286
- Accuracy: 0.661
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1041 | 1.0 | 19 | 1.0658 | 0.5 |
| 1.009 | 2.0 | 38 | 0.9892 | 0.7 |
| 0.7925 | 3.0 | 57 | 0.8516 | 0.7 |
| 0.5279 | 4.0 | 76 | 0.7877 | 0.65 |
| 0.2932 | 5.0 | 95 | 0.7592 | 0.65 |
| 0.1166 | 6.0 | 114 | 0.9437 | 0.65 |
| 0.044 | 7.0 | 133 | 1.0315 | 0.75 |
| 0.0197 | 8.0 | 152 | 1.3513 | 0.55 |
| 0.0126 | 9.0 | 171 | 1.1702 | 0.7 |
| 0.0083 | 10.0 | 190 | 1.2272 | 0.7 |
| 0.0068 | 11.0 | 209 | 1.2889 | 0.7 |
| 0.0059 | 12.0 | 228 | 1.3073 | 0.7 |
| 0.0052 | 13.0 | 247 | 1.3595 | 0.7 |
| 0.0041 | 14.0 | 266 | 1.4443 | 0.7 |
| 0.0038 | 15.0 | 285 | 1.4709 | 0.7 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Politico_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-32-4
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: 0.7384
- Accuracy: 0.724
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1013 | 1.0 | 19 | 1.0733 | 0.55 |
| 1.0226 | 2.0 | 38 | 1.0064 | 0.65 |
| 0.8539 | 3.0 | 57 | 0.8758 | 0.75 |
| 0.584 | 4.0 | 76 | 0.6941 | 0.7 |
| 0.2813 | 5.0 | 95 | 0.5151 | 0.7 |
| 0.1122 | 6.0 | 114 | 0.4351 | 0.8 |
| 0.0432 | 7.0 | 133 | 0.4896 | 0.85 |
| 0.0199 | 8.0 | 152 | 0.5391 | 0.85 |
| 0.0126 | 9.0 | 171 | 0.5200 | 0.85 |
| 0.0085 | 10.0 | 190 | 0.5622 | 0.85 |
| 0.0069 | 11.0 | 209 | 0.5950 | 0.85 |
| 0.0058 | 12.0 | 228 | 0.6015 | 0.85 |
| 0.0053 | 13.0 | 247 | 0.6120 | 0.85 |
| 0.0042 | 14.0 | 266 | 0.6347 | 0.85 |
| 0.0039 | 15.0 | 285 | 0.6453 | 0.85 |
| 0.0034 | 16.0 | 304 | 0.6660 | 0.85 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Politico_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-32-5
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: 1.1327
- Accuracy: 0.57
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0972 | 1.0 | 19 | 1.0470 | 0.45 |
| 0.9738 | 2.0 | 38 | 0.9244 | 0.65 |
| 0.7722 | 3.0 | 57 | 0.8612 | 0.65 |
| 0.4929 | 4.0 | 76 | 0.6759 | 0.75 |
| 0.2435 | 5.0 | 95 | 0.7273 | 0.7 |
| 0.0929 | 6.0 | 114 | 0.6444 | 0.85 |
| 0.0357 | 7.0 | 133 | 0.7671 | 0.8 |
| 0.0173 | 8.0 | 152 | 0.7599 | 0.75 |
| 0.0121 | 9.0 | 171 | 0.8140 | 0.8 |
| 0.0081 | 10.0 | 190 | 0.7861 | 0.8 |
| 0.0066 | 11.0 | 209 | 0.8318 | 0.8 |
| 0.0057 | 12.0 | 228 | 0.8777 | 0.8 |
| 0.0053 | 13.0 | 247 | 0.8501 | 0.8 |
| 0.004 | 14.0 | 266 | 0.8603 | 0.8 |
| 0.004 | 15.0 | 285 | 0.8787 | 0.8 |
| 0.0034 | 16.0 | 304 | 0.8969 | 0.8 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Politico_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-6
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__hate_speech_offensive__train-32-6
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: 1.0523
- Accuracy: 0.663
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0957 | 1.0 | 19 | 1.0696 | 0.6 |
| 1.0107 | 2.0 | 38 | 1.0047 | 0.55 |
| 0.8257 | 3.0 | 57 | 0.8358 | 0.8 |
| 0.6006 | 4.0 | 76 | 0.7641 | 0.6 |
| 0.4172 | 5.0 | 95 | 0.5931 | 0.8 |
| 0.2639 | 6.0 | 114 | 0.5570 | 0.7 |
| 0.1314 | 7.0 | 133 | 0.5017 | 0.65 |
| 0.0503 | 8.0 | 152 | 0.3115 | 0.75 |
| 0.023 | 9.0 | 171 | 0.4353 | 0.85 |
| 0.0128 | 10.0 | 190 | 0.5461 | 0.75 |
| 0.0092 | 11.0 | 209 | 0.5045 | 0.8 |
| 0.007 | 12.0 | 228 | 0.5014 | 0.8 |
| 0.0064 | 13.0 | 247 | 0.5070 | 0.8 |
| 0.0049 | 14.0 | 266 | 0.4681 | 0.8 |
| 0.0044 | 15.0 | 285 | 0.4701 | 0.8 |
| 0.0039 | 16.0 | 304 | 0.4862 | 0.8 |
| 0.0036 | 17.0 | 323 | 0.4742 | 0.8 |
| 0.0035 | 18.0 | 342 | 0.4652 | 0.8 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-32-9
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__hate_speech_offensive__train-32-9
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: 0.7075
- Accuracy: 0.692
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1054 | 1.0 | 19 | 1.0938 | 0.35 |
| 1.0338 | 2.0 | 38 | 1.0563 | 0.65 |
| 0.8622 | 3.0 | 57 | 0.9372 | 0.6 |
| 0.5919 | 4.0 | 76 | 0.8461 | 0.6 |
| 0.3357 | 5.0 | 95 | 1.0206 | 0.45 |
| 0.1621 | 6.0 | 114 | 0.9802 | 0.7 |
| 0.0637 | 7.0 | 133 | 1.2434 | 0.65 |
| 0.0261 | 8.0 | 152 | 1.3865 | 0.65 |
| 0.0156 | 9.0 | 171 | 1.4414 | 0.7 |
| 0.01 | 10.0 | 190 | 1.5502 | 0.7 |
| 0.0079 | 11.0 | 209 | 1.6102 | 0.7 |
| 0.0062 | 12.0 | 228 | 1.6525 | 0.7 |
| 0.0058 | 13.0 | 247 | 1.6884 | 0.7 |
| 0.0046 | 14.0 | 266 | 1.7479 | 0.7 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
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} | 5 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-0
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__hate_speech_offensive__train-8-0
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: 1.1097
- Accuracy: 0.132
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1065 | 1.0 | 5 | 1.1287 | 0.0 |
| 1.0592 | 2.0 | 10 | 1.1729 | 0.0 |
| 1.0059 | 3.0 | 15 | 1.1959 | 0.0 |
| 0.9129 | 4.0 | 20 | 1.2410 | 0.0 |
| 0.8231 | 5.0 | 25 | 1.2820 | 0.0 |
| 0.7192 | 6.0 | 30 | 1.3361 | 0.0 |
| 0.6121 | 7.0 | 35 | 1.4176 | 0.0 |
| 0.5055 | 8.0 | 40 | 1.5111 | 0.0 |
| 0.4002 | 9.0 | 45 | 1.5572 | 0.0 |
| 0.3788 | 10.0 | 50 | 1.6733 | 0.0 |
| 0.2755 | 11.0 | 55 | 1.7381 | 0.2 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v3 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-1
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. -->
# distilbert-base-uncased__hate_speech_offensive__train-8-1
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: 1.1013
- Accuracy: 0.0915
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0866 | 1.0 | 5 | 1.1363 | 0.0 |
| 1.0439 | 2.0 | 10 | 1.1803 | 0.0 |
| 1.0227 | 3.0 | 15 | 1.2162 | 0.2 |
| 0.9111 | 4.0 | 20 | 1.2619 | 0.0 |
| 0.8243 | 5.0 | 25 | 1.2929 | 0.2 |
| 0.7488 | 6.0 | 30 | 1.3010 | 0.2 |
| 0.62 | 7.0 | 35 | 1.3011 | 0.2 |
| 0.5054 | 8.0 | 40 | 1.2931 | 0.4 |
| 0.4191 | 9.0 | 45 | 1.3274 | 0.4 |
| 0.4107 | 10.0 | 50 | 1.3259 | 0.4 |
| 0.3376 | 11.0 | 55 | 1.2800 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v4 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1019
- Accuracy: 0.139
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1082 | 1.0 | 5 | 1.1432 | 0.0 |
| 1.0524 | 2.0 | 10 | 1.1613 | 0.0 |
| 1.0641 | 3.0 | 15 | 1.1547 | 0.0 |
| 0.9592 | 4.0 | 20 | 1.1680 | 0.0 |
| 0.9085 | 5.0 | 25 | 1.1762 | 0.0 |
| 0.8508 | 6.0 | 30 | 1.1809 | 0.2 |
| 0.7263 | 7.0 | 35 | 1.1912 | 0.2 |
| 0.6448 | 8.0 | 40 | 1.2100 | 0.2 |
| 0.5378 | 9.0 | 45 | 1.2037 | 0.2 |
| 0.5031 | 10.0 | 50 | 1.2096 | 0.2 |
| 0.4041 | 11.0 | 55 | 1.2203 | 0.2 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v5 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-3
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__hate_speech_offensive__train-8-3
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: 0.9681
- Accuracy: 0.549
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1073 | 1.0 | 5 | 1.1393 | 0.0 |
| 1.0392 | 2.0 | 10 | 1.1729 | 0.0 |
| 1.0302 | 3.0 | 15 | 1.1694 | 0.2 |
| 0.9176 | 4.0 | 20 | 1.1846 | 0.2 |
| 0.8339 | 5.0 | 25 | 1.1663 | 0.2 |
| 0.7533 | 6.0 | 30 | 1.1513 | 0.4 |
| 0.6327 | 7.0 | 35 | 1.1474 | 0.4 |
| 0.4402 | 8.0 | 40 | 1.1385 | 0.4 |
| 0.3752 | 9.0 | 45 | 1.0965 | 0.2 |
| 0.3448 | 10.0 | 50 | 1.0357 | 0.2 |
| 0.2582 | 11.0 | 55 | 1.0438 | 0.2 |
| 0.1903 | 12.0 | 60 | 1.0561 | 0.2 |
| 0.1479 | 13.0 | 65 | 1.0569 | 0.2 |
| 0.1129 | 14.0 | 70 | 1.0455 | 0.2 |
| 0.1071 | 15.0 | 75 | 1.0416 | 0.4 |
| 0.0672 | 16.0 | 80 | 1.1164 | 0.4 |
| 0.0561 | 17.0 | 85 | 1.1846 | 0.6 |
| 0.0463 | 18.0 | 90 | 1.2040 | 0.6 |
| 0.0431 | 19.0 | 95 | 1.2078 | 0.6 |
| 0.0314 | 20.0 | 100 | 1.2368 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v6 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
"model_type": "bert",
"task_specific_params": {
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-8-4
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: 1.1045
- Accuracy: 0.128
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1115 | 1.0 | 5 | 1.1174 | 0.0 |
| 1.0518 | 2.0 | 10 | 1.1379 | 0.0 |
| 1.0445 | 3.0 | 15 | 1.1287 | 0.0 |
| 0.9306 | 4.0 | 20 | 1.1324 | 0.2 |
| 0.8242 | 5.0 | 25 | 1.1219 | 0.2 |
| 0.7986 | 6.0 | 30 | 1.1369 | 0.4 |
| 0.7369 | 7.0 | 35 | 1.1732 | 0.2 |
| 0.534 | 8.0 | 40 | 1.1828 | 0.6 |
| 0.4285 | 9.0 | 45 | 1.1482 | 0.6 |
| 0.3691 | 10.0 | 50 | 1.1401 | 0.6 |
| 0.3215 | 11.0 | 55 | 1.1286 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/Reuters_model_v8 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"BertForMaskedLM"
],
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__hate_speech_offensive__train-8-5
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: 1.7214
- Accuracy: 0.37
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0995 | 1.0 | 5 | 1.1301 | 0.0 |
| 1.0227 | 2.0 | 10 | 1.1727 | 0.0 |
| 1.0337 | 3.0 | 15 | 1.1734 | 0.2 |
| 0.9137 | 4.0 | 20 | 1.1829 | 0.2 |
| 0.8065 | 5.0 | 25 | 1.1496 | 0.4 |
| 0.7038 | 6.0 | 30 | 1.1101 | 0.4 |
| 0.6246 | 7.0 | 35 | 1.0982 | 0.2 |
| 0.4481 | 8.0 | 40 | 1.0913 | 0.2 |
| 0.3696 | 9.0 | 45 | 1.0585 | 0.4 |
| 0.3137 | 10.0 | 50 | 1.0418 | 0.4 |
| 0.2482 | 11.0 | 55 | 1.0078 | 0.4 |
| 0.196 | 12.0 | 60 | 0.9887 | 0.6 |
| 0.1344 | 13.0 | 65 | 0.9719 | 0.6 |
| 0.1014 | 14.0 | 70 | 1.0053 | 0.6 |
| 0.111 | 15.0 | 75 | 0.9653 | 0.6 |
| 0.0643 | 16.0 | 80 | 0.9018 | 0.6 |
| 0.0559 | 17.0 | 85 | 0.9393 | 0.6 |
| 0.0412 | 18.0 | 90 | 1.0210 | 0.6 |
| 0.0465 | 19.0 | 95 | 0.9965 | 0.6 |
| 0.0328 | 20.0 | 100 | 0.9739 | 0.6 |
| 0.0289 | 21.0 | 105 | 0.9796 | 0.6 |
| 0.0271 | 22.0 | 110 | 0.9968 | 0.6 |
| 0.0239 | 23.0 | 115 | 1.0143 | 0.6 |
| 0.0201 | 24.0 | 120 | 1.0459 | 0.6 |
| 0.0185 | 25.0 | 125 | 1.0698 | 0.6 |
| 0.0183 | 26.0 | 130 | 1.0970 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/WallStreetJournal_model_v1 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-6
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__hate_speech_offensive__train-8-6
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: 1.1275
- Accuracy: 0.3795
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.11 | 1.0 | 5 | 1.1184 | 0.0 |
| 1.0608 | 2.0 | 10 | 1.1227 | 0.0 |
| 1.0484 | 3.0 | 15 | 1.1009 | 0.2 |
| 0.9614 | 4.0 | 20 | 1.1009 | 0.2 |
| 0.8545 | 5.0 | 25 | 1.0772 | 0.2 |
| 0.8241 | 6.0 | 30 | 1.0457 | 0.2 |
| 0.708 | 7.0 | 35 | 1.0301 | 0.4 |
| 0.5045 | 8.0 | 40 | 1.0325 | 0.4 |
| 0.4175 | 9.0 | 45 | 1.0051 | 0.4 |
| 0.3446 | 10.0 | 50 | 0.9610 | 0.4 |
| 0.2851 | 11.0 | 55 | 0.9954 | 0.4 |
| 0.1808 | 12.0 | 60 | 1.0561 | 0.4 |
| 0.1435 | 13.0 | 65 | 1.0218 | 0.4 |
| 0.1019 | 14.0 | 70 | 1.0254 | 0.4 |
| 0.0908 | 15.0 | 75 | 0.9935 | 0.4 |
| 0.0591 | 16.0 | 80 | 1.0090 | 0.4 |
| 0.0512 | 17.0 | 85 | 1.0884 | 0.4 |
| 0.0397 | 18.0 | 90 | 1.2732 | 0.4 |
| 0.039 | 19.0 | 95 | 1.2979 | 0.6 |
| 0.0325 | 20.0 | 100 | 1.2705 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/WallStreetJournal_model_v2 | [
"pytorch",
"bert",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
"architectures": [
"BertForMaskedLM"
],
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} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-7
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__hate_speech_offensive__train-8-7
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: 1.1206
- Accuracy: 0.0555
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1186 | 1.0 | 5 | 1.1631 | 0.0 |
| 1.058 | 2.0 | 10 | 1.1986 | 0.0 |
| 1.081 | 3.0 | 15 | 1.2111 | 0.0 |
| 1.0118 | 4.0 | 20 | 1.2373 | 0.0 |
| 0.9404 | 5.0 | 25 | 1.2645 | 0.0 |
| 0.9146 | 6.0 | 30 | 1.3258 | 0.0 |
| 0.8285 | 7.0 | 35 | 1.3789 | 0.0 |
| 0.6422 | 8.0 | 40 | 1.3783 | 0.0 |
| 0.6156 | 9.0 | 45 | 1.3691 | 0.0 |
| 0.5321 | 10.0 | 50 | 1.3693 | 0.0 |
| 0.4504 | 11.0 | 55 | 1.4000 | 0.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-8
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__hate_speech_offensive__train-8-8
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: 1.0005
- Accuracy: 0.518
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1029 | 1.0 | 5 | 1.1295 | 0.0 |
| 1.0472 | 2.0 | 10 | 1.1531 | 0.0 |
| 1.054 | 3.0 | 15 | 1.1475 | 0.0 |
| 0.9366 | 4.0 | 20 | 1.1515 | 0.0 |
| 0.8698 | 5.0 | 25 | 1.1236 | 0.4 |
| 0.8148 | 6.0 | 30 | 1.0716 | 0.6 |
| 0.6884 | 7.0 | 35 | 1.0662 | 0.6 |
| 0.5641 | 8.0 | 40 | 1.0671 | 0.6 |
| 0.5 | 9.0 | 45 | 1.0282 | 0.6 |
| 0.3882 | 10.0 | 50 | 1.0500 | 0.6 |
| 0.3522 | 11.0 | 55 | 1.1381 | 0.6 |
| 0.2492 | 12.0 | 60 | 1.1278 | 0.6 |
| 0.2063 | 13.0 | 65 | 1.0731 | 0.6 |
| 0.1608 | 14.0 | 70 | 1.1339 | 0.6 |
| 0.1448 | 15.0 | 75 | 1.1892 | 0.6 |
| 0.0925 | 16.0 | 80 | 1.1840 | 0.6 |
| 0.0768 | 17.0 | 85 | 1.0608 | 0.6 |
| 0.0585 | 18.0 | 90 | 1.1073 | 0.6 |
| 0.0592 | 19.0 | 95 | 1.3134 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Declan/WallStreetJournal_model_v4 | [
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} | 7 | 2022-02-09T17:18:08Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__hate_speech_offensive__train-8-9
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__hate_speech_offensive__train-8-9
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: 1.0959
- Accuracy: 0.093
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1068 | 1.0 | 5 | 1.1545 | 0.0 |
| 1.0494 | 2.0 | 10 | 1.1971 | 0.0 |
| 1.0612 | 3.0 | 15 | 1.2164 | 0.0 |
| 0.9517 | 4.0 | 20 | 1.2545 | 0.0 |
| 0.8874 | 5.0 | 25 | 1.2699 | 0.0 |
| 0.8598 | 6.0 | 30 | 1.2835 | 0.0 |
| 0.7006 | 7.0 | 35 | 1.3139 | 0.0 |
| 0.5969 | 8.0 | 40 | 1.3116 | 0.2 |
| 0.4769 | 9.0 | 45 | 1.3124 | 0.4 |
| 0.4352 | 10.0 | 50 | 1.3541 | 0.4 |
| 0.3231 | 11.0 | 55 | 1.3919 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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"transformers",
"autotrain_compatible"
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__all-train
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__sst2__all-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2496
- Accuracy: 0.8962
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3643 | 1.0 | 433 | 0.2496 | 0.8962 |
| 0.196 | 2.0 | 866 | 0.2548 | 0.9110 |
| 0.0915 | 3.0 | 1299 | 0.4483 | 0.8957 |
| 0.0505 | 4.0 | 1732 | 0.4968 | 0.9044 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-0
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__sst2__train-16-0
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: 0.6903
- Accuracy: 0.5091
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6934 | 1.0 | 7 | 0.7142 | 0.2857 |
| 0.6703 | 2.0 | 14 | 0.7379 | 0.2857 |
| 0.6282 | 3.0 | 21 | 0.7769 | 0.2857 |
| 0.5193 | 4.0 | 28 | 0.8799 | 0.2857 |
| 0.5104 | 5.0 | 35 | 0.8380 | 0.4286 |
| 0.2504 | 6.0 | 42 | 0.8622 | 0.4286 |
| 0.1794 | 7.0 | 49 | 0.9227 | 0.4286 |
| 0.1156 | 8.0 | 56 | 0.8479 | 0.4286 |
| 0.0709 | 9.0 | 63 | 1.0929 | 0.2857 |
| 0.0471 | 10.0 | 70 | 1.2189 | 0.2857 |
| 0.0288 | 11.0 | 77 | 1.2026 | 0.4286 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-1
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. -->
# distilbert-base-uncased__sst2__train-16-1
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: 0.6012
- Accuracy: 0.6766
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6983 | 1.0 | 7 | 0.7036 | 0.2857 |
| 0.6836 | 2.0 | 14 | 0.7181 | 0.2857 |
| 0.645 | 3.0 | 21 | 0.7381 | 0.2857 |
| 0.5902 | 4.0 | 28 | 0.7746 | 0.2857 |
| 0.5799 | 5.0 | 35 | 0.7242 | 0.5714 |
| 0.3584 | 6.0 | 42 | 0.6935 | 0.5714 |
| 0.2596 | 7.0 | 49 | 0.7041 | 0.5714 |
| 0.1815 | 8.0 | 56 | 0.5930 | 0.7143 |
| 0.0827 | 9.0 | 63 | 0.6976 | 0.7143 |
| 0.0613 | 10.0 | 70 | 0.7346 | 0.7143 |
| 0.0356 | 11.0 | 77 | 0.6992 | 0.5714 |
| 0.0158 | 12.0 | 84 | 0.7328 | 0.5714 |
| 0.013 | 13.0 | 91 | 0.7819 | 0.5714 |
| 0.0103 | 14.0 | 98 | 0.8589 | 0.5714 |
| 0.0087 | 15.0 | 105 | 0.9177 | 0.5714 |
| 0.0076 | 16.0 | 112 | 0.9519 | 0.5714 |
| 0.0078 | 17.0 | 119 | 0.9556 | 0.5714 |
| 0.006 | 18.0 | 126 | 0.9542 | 0.5714 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-16-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6748
- Accuracy: 0.6315
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7043 | 1.0 | 7 | 0.7054 | 0.2857 |
| 0.6711 | 2.0 | 14 | 0.7208 | 0.2857 |
| 0.6311 | 3.0 | 21 | 0.7365 | 0.2857 |
| 0.551 | 4.0 | 28 | 0.7657 | 0.5714 |
| 0.5599 | 5.0 | 35 | 0.6915 | 0.5714 |
| 0.3167 | 6.0 | 42 | 0.7134 | 0.5714 |
| 0.2489 | 7.0 | 49 | 0.7892 | 0.5714 |
| 0.1985 | 8.0 | 56 | 0.6756 | 0.7143 |
| 0.0864 | 9.0 | 63 | 0.8059 | 0.5714 |
| 0.0903 | 10.0 | 70 | 0.8165 | 0.7143 |
| 0.0429 | 11.0 | 77 | 0.7947 | 0.7143 |
| 0.0186 | 12.0 | 84 | 0.8570 | 0.7143 |
| 0.0146 | 13.0 | 91 | 0.9346 | 0.7143 |
| 0.011 | 14.0 | 98 | 0.9804 | 0.7143 |
| 0.0098 | 15.0 | 105 | 1.0136 | 0.7143 |
| 0.0086 | 16.0 | 112 | 1.0424 | 0.7143 |
| 0.0089 | 17.0 | 119 | 1.0736 | 0.7143 |
| 0.0068 | 18.0 | 126 | 1.0808 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-3
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__sst2__train-16-3
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: 0.7887
- Accuracy: 0.6458
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6928 | 1.0 | 7 | 0.6973 | 0.4286 |
| 0.675 | 2.0 | 14 | 0.7001 | 0.4286 |
| 0.6513 | 3.0 | 21 | 0.6959 | 0.4286 |
| 0.5702 | 4.0 | 28 | 0.6993 | 0.4286 |
| 0.5389 | 5.0 | 35 | 0.6020 | 0.7143 |
| 0.3386 | 6.0 | 42 | 0.5326 | 0.5714 |
| 0.2596 | 7.0 | 49 | 0.4943 | 0.7143 |
| 0.1633 | 8.0 | 56 | 0.3589 | 0.8571 |
| 0.1086 | 9.0 | 63 | 0.2924 | 0.8571 |
| 0.0641 | 10.0 | 70 | 0.2687 | 0.8571 |
| 0.0409 | 11.0 | 77 | 0.2202 | 0.8571 |
| 0.0181 | 12.0 | 84 | 0.2445 | 0.8571 |
| 0.0141 | 13.0 | 91 | 0.2885 | 0.8571 |
| 0.0108 | 14.0 | 98 | 0.3069 | 0.8571 |
| 0.009 | 15.0 | 105 | 0.3006 | 0.8571 |
| 0.0084 | 16.0 | 112 | 0.2834 | 0.8571 |
| 0.0088 | 17.0 | 119 | 0.2736 | 0.8571 |
| 0.0062 | 18.0 | 126 | 0.2579 | 0.8571 |
| 0.0058 | 19.0 | 133 | 0.2609 | 0.8571 |
| 0.0057 | 20.0 | 140 | 0.2563 | 0.8571 |
| 0.0049 | 21.0 | 147 | 0.2582 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-16-4
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: 1.1501
- Accuracy: 0.6387
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7043 | 1.0 | 7 | 0.7139 | 0.2857 |
| 0.68 | 2.0 | 14 | 0.7398 | 0.2857 |
| 0.641 | 3.0 | 21 | 0.7723 | 0.2857 |
| 0.5424 | 4.0 | 28 | 0.8391 | 0.2857 |
| 0.5988 | 5.0 | 35 | 0.7761 | 0.2857 |
| 0.3698 | 6.0 | 42 | 0.7707 | 0.4286 |
| 0.3204 | 7.0 | 49 | 0.8290 | 0.4286 |
| 0.2882 | 8.0 | 56 | 0.6551 | 0.5714 |
| 0.1512 | 9.0 | 63 | 0.5652 | 0.5714 |
| 0.1302 | 10.0 | 70 | 0.5278 | 0.5714 |
| 0.1043 | 11.0 | 77 | 0.4987 | 0.7143 |
| 0.0272 | 12.0 | 84 | 0.5278 | 0.5714 |
| 0.0201 | 13.0 | 91 | 0.5307 | 0.5714 |
| 0.0129 | 14.0 | 98 | 0.5382 | 0.5714 |
| 0.0117 | 15.0 | 105 | 0.5227 | 0.5714 |
| 0.0094 | 16.0 | 112 | 0.5066 | 0.7143 |
| 0.0104 | 17.0 | 119 | 0.4869 | 0.7143 |
| 0.0069 | 18.0 | 126 | 0.4786 | 0.7143 |
| 0.0062 | 19.0 | 133 | 0.4707 | 0.7143 |
| 0.0065 | 20.0 | 140 | 0.4669 | 0.7143 |
| 0.0051 | 21.0 | 147 | 0.4686 | 0.7143 |
| 0.0049 | 22.0 | 154 | 0.4784 | 0.7143 |
| 0.0046 | 23.0 | 161 | 0.4839 | 0.7143 |
| 0.0039 | 24.0 | 168 | 0.4823 | 0.7143 |
| 0.0044 | 25.0 | 175 | 0.4791 | 0.7143 |
| 0.0037 | 26.0 | 182 | 0.4778 | 0.7143 |
| 0.0038 | 27.0 | 189 | 0.4770 | 0.7143 |
| 0.0036 | 28.0 | 196 | 0.4750 | 0.7143 |
| 0.0031 | 29.0 | 203 | 0.4766 | 0.7143 |
| 0.0031 | 30.0 | 210 | 0.4754 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/ChemBERTa-10M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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} | 90 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-16-5
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: 0.6537
- Accuracy: 0.6332
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6925 | 1.0 | 7 | 0.6966 | 0.2857 |
| 0.6703 | 2.0 | 14 | 0.7045 | 0.2857 |
| 0.6404 | 3.0 | 21 | 0.7205 | 0.2857 |
| 0.555 | 4.0 | 28 | 0.7548 | 0.2857 |
| 0.5179 | 5.0 | 35 | 0.6745 | 0.5714 |
| 0.3038 | 6.0 | 42 | 0.7260 | 0.5714 |
| 0.2089 | 7.0 | 49 | 0.8016 | 0.5714 |
| 0.1303 | 8.0 | 56 | 0.8202 | 0.5714 |
| 0.0899 | 9.0 | 63 | 0.9966 | 0.5714 |
| 0.0552 | 10.0 | 70 | 1.1887 | 0.5714 |
| 0.0333 | 11.0 | 77 | 1.2163 | 0.5714 |
| 0.0169 | 12.0 | 84 | 1.2874 | 0.5714 |
| 0.0136 | 13.0 | 91 | 1.3598 | 0.5714 |
| 0.0103 | 14.0 | 98 | 1.4237 | 0.5714 |
| 0.0089 | 15.0 | 105 | 1.4758 | 0.5714 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/ChemBERTa-10M-MTR | [
"pytorch",
"roberta",
"arxiv:1910.09700",
"transformers"
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} | 708 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-6
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__sst2__train-16-6
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: 0.8356
- Accuracy: 0.6480
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6978 | 1.0 | 7 | 0.6807 | 0.4286 |
| 0.6482 | 2.0 | 14 | 0.6775 | 0.4286 |
| 0.6051 | 3.0 | 21 | 0.6623 | 0.5714 |
| 0.486 | 4.0 | 28 | 0.6710 | 0.5714 |
| 0.4612 | 5.0 | 35 | 0.5325 | 0.7143 |
| 0.2233 | 6.0 | 42 | 0.4992 | 0.7143 |
| 0.1328 | 7.0 | 49 | 0.4753 | 0.7143 |
| 0.0905 | 8.0 | 56 | 0.2416 | 1.0 |
| 0.0413 | 9.0 | 63 | 0.2079 | 1.0 |
| 0.0356 | 10.0 | 70 | 0.2234 | 0.8571 |
| 0.0217 | 11.0 | 77 | 0.2639 | 0.8571 |
| 0.0121 | 12.0 | 84 | 0.2977 | 0.8571 |
| 0.0105 | 13.0 | 91 | 0.3468 | 0.8571 |
| 0.0085 | 14.0 | 98 | 0.3912 | 0.8571 |
| 0.0077 | 15.0 | 105 | 0.4000 | 0.8571 |
| 0.0071 | 16.0 | 112 | 0.4015 | 0.8571 |
| 0.0078 | 17.0 | 119 | 0.3865 | 0.8571 |
| 0.0059 | 18.0 | 126 | 0.3603 | 0.8571 |
| 0.0051 | 19.0 | 133 | 0.3231 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/ChemBERTa-5M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
] | fill-mask | {
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"RobertaForMaskedLM"
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} | 29 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-7
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__sst2__train-16-7
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: 0.6952
- Accuracy: 0.5025
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6949 | 1.0 | 7 | 0.7252 | 0.2857 |
| 0.6678 | 2.0 | 14 | 0.7550 | 0.2857 |
| 0.6299 | 3.0 | 21 | 0.8004 | 0.2857 |
| 0.5596 | 4.0 | 28 | 0.8508 | 0.2857 |
| 0.5667 | 5.0 | 35 | 0.8464 | 0.2857 |
| 0.367 | 6.0 | 42 | 0.8515 | 0.2857 |
| 0.2706 | 7.0 | 49 | 0.9574 | 0.2857 |
| 0.2163 | 8.0 | 56 | 0.9710 | 0.4286 |
| 0.1024 | 9.0 | 63 | 1.1607 | 0.1429 |
| 0.1046 | 10.0 | 70 | 1.3779 | 0.1429 |
| 0.0483 | 11.0 | 77 | 1.4876 | 0.1429 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/ChemBERTa-5M-MTR | [
"pytorch",
"roberta",
"transformers"
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} | 13 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-8
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__sst2__train-16-8
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: 0.6895
- Accuracy: 0.5222
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6899 | 1.0 | 7 | 0.7055 | 0.2857 |
| 0.6793 | 2.0 | 14 | 0.7205 | 0.2857 |
| 0.6291 | 3.0 | 21 | 0.7460 | 0.2857 |
| 0.5659 | 4.0 | 28 | 0.8041 | 0.2857 |
| 0.5607 | 5.0 | 35 | 0.7785 | 0.4286 |
| 0.3349 | 6.0 | 42 | 0.8163 | 0.4286 |
| 0.2436 | 7.0 | 49 | 0.9101 | 0.2857 |
| 0.1734 | 8.0 | 56 | 0.8632 | 0.5714 |
| 0.1122 | 9.0 | 63 | 0.9851 | 0.5714 |
| 0.0661 | 10.0 | 70 | 1.0835 | 0.5714 |
| 0.0407 | 11.0 | 77 | 1.1656 | 0.5714 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/ChemBERTa-77M-MLM | [
"pytorch",
"roberta",
"fill-mask",
"transformers",
"autotrain_compatible"
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"RobertaForMaskedLM"
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} | 2,416 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-16-9
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__sst2__train-16-9
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: 0.6915
- Accuracy: 0.5157
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6868 | 1.0 | 7 | 0.7121 | 0.1429 |
| 0.6755 | 2.0 | 14 | 0.7234 | 0.1429 |
| 0.6389 | 3.0 | 21 | 0.7384 | 0.2857 |
| 0.5575 | 4.0 | 28 | 0.7884 | 0.2857 |
| 0.4972 | 5.0 | 35 | 0.7767 | 0.4286 |
| 0.2821 | 6.0 | 42 | 0.8275 | 0.4286 |
| 0.1859 | 7.0 | 49 | 0.9283 | 0.2857 |
| 0.1388 | 8.0 | 56 | 0.9384 | 0.4286 |
| 0.078 | 9.0 | 63 | 1.1973 | 0.4286 |
| 0.0462 | 10.0 | 70 | 1.4016 | 0.4286 |
| 0.0319 | 11.0 | 77 | 1.4087 | 0.4286 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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"transformers"
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} | 7,169 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-0
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__sst2__train-32-0
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: 0.8558
- Accuracy: 0.7183
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7088 | 1.0 | 13 | 0.6819 | 0.6154 |
| 0.635 | 2.0 | 26 | 0.6318 | 0.7692 |
| 0.547 | 3.0 | 39 | 0.5356 | 0.7692 |
| 0.3497 | 4.0 | 52 | 0.4456 | 0.6923 |
| 0.1979 | 5.0 | 65 | 0.3993 | 0.7692 |
| 0.098 | 6.0 | 78 | 0.3613 | 0.7692 |
| 0.0268 | 7.0 | 91 | 0.3561 | 0.9231 |
| 0.0137 | 8.0 | 104 | 0.3755 | 0.9231 |
| 0.0083 | 9.0 | 117 | 0.4194 | 0.7692 |
| 0.0065 | 10.0 | 130 | 0.4446 | 0.7692 |
| 0.005 | 11.0 | 143 | 0.4527 | 0.7692 |
| 0.0038 | 12.0 | 156 | 0.4645 | 0.7692 |
| 0.0033 | 13.0 | 169 | 0.4735 | 0.7692 |
| 0.0033 | 14.0 | 182 | 0.4874 | 0.7692 |
| 0.0029 | 15.0 | 195 | 0.5041 | 0.7692 |
| 0.0025 | 16.0 | 208 | 0.5148 | 0.7692 |
| 0.0024 | 17.0 | 221 | 0.5228 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepChem/SmilesTokenizer_PubChem_1M | [
"pytorch",
"roberta",
"feature-extraction",
"transformers"
] | feature-extraction | {
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"RobertaModel"
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} | 227 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-1
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. -->
# distilbert-base-uncased__sst2__train-32-1
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: 0.6492
- Accuracy: 0.6551
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7106 | 1.0 | 13 | 0.6850 | 0.6154 |
| 0.631 | 2.0 | 26 | 0.6632 | 0.6923 |
| 0.5643 | 3.0 | 39 | 0.6247 | 0.7692 |
| 0.3992 | 4.0 | 52 | 0.5948 | 0.7692 |
| 0.1928 | 5.0 | 65 | 0.5803 | 0.7692 |
| 0.0821 | 6.0 | 78 | 0.6404 | 0.6923 |
| 0.0294 | 7.0 | 91 | 0.7387 | 0.6923 |
| 0.0141 | 8.0 | 104 | 0.8270 | 0.6923 |
| 0.0082 | 9.0 | 117 | 0.8496 | 0.6923 |
| 0.0064 | 10.0 | 130 | 0.8679 | 0.6923 |
| 0.005 | 11.0 | 143 | 0.8914 | 0.6923 |
| 0.0036 | 12.0 | 156 | 0.9278 | 0.6923 |
| 0.0031 | 13.0 | 169 | 0.9552 | 0.6923 |
| 0.0029 | 14.0 | 182 | 0.9745 | 0.6923 |
| 0.0028 | 15.0 | 195 | 0.9785 | 0.6923 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepESP/gpt2-spanish-medium | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
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} | 340 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-32-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4805
- Accuracy: 0.7699
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7124 | 1.0 | 13 | 0.6882 | 0.5385 |
| 0.6502 | 2.0 | 26 | 0.6715 | 0.5385 |
| 0.6001 | 3.0 | 39 | 0.6342 | 0.6154 |
| 0.455 | 4.0 | 52 | 0.5713 | 0.7692 |
| 0.2605 | 5.0 | 65 | 0.5562 | 0.7692 |
| 0.1258 | 6.0 | 78 | 0.6799 | 0.7692 |
| 0.0444 | 7.0 | 91 | 0.8096 | 0.7692 |
| 0.0175 | 8.0 | 104 | 0.9281 | 0.6923 |
| 0.0106 | 9.0 | 117 | 0.9826 | 0.6923 |
| 0.0077 | 10.0 | 130 | 1.0254 | 0.7692 |
| 0.0056 | 11.0 | 143 | 1.0667 | 0.7692 |
| 0.0042 | 12.0 | 156 | 1.1003 | 0.7692 |
| 0.0036 | 13.0 | 169 | 1.1299 | 0.7692 |
| 0.0034 | 14.0 | 182 | 1.1623 | 0.6923 |
| 0.003 | 15.0 | 195 | 1.1938 | 0.6923 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepESP/gpt2-spanish | [
"pytorch",
"tf",
"jax",
"gpt2",
"text-generation",
"es",
"dataset:ebooks",
"transformers",
"GPT-2",
"Spanish",
"ebooks",
"nlg",
"license:mit",
"has_space"
] | text-generation | {
"architectures": [
"GPT2LMHeadModel"
],
"model_type": "gpt2",
"task_specific_params": {
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},
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}
} | 1,463 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-3
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__sst2__train-32-3
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: 0.5694
- Accuracy: 0.7073
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7118 | 1.0 | 13 | 0.6844 | 0.5385 |
| 0.6587 | 2.0 | 26 | 0.6707 | 0.6154 |
| 0.6067 | 3.0 | 39 | 0.6295 | 0.5385 |
| 0.4714 | 4.0 | 52 | 0.5811 | 0.6923 |
| 0.2444 | 5.0 | 65 | 0.5932 | 0.7692 |
| 0.1007 | 6.0 | 78 | 0.7386 | 0.6923 |
| 0.0332 | 7.0 | 91 | 0.6962 | 0.6154 |
| 0.0147 | 8.0 | 104 | 0.8200 | 0.7692 |
| 0.0083 | 9.0 | 117 | 0.9250 | 0.7692 |
| 0.0066 | 10.0 | 130 | 0.9345 | 0.7692 |
| 0.005 | 11.0 | 143 | 0.9313 | 0.7692 |
| 0.0036 | 12.0 | 156 | 0.9356 | 0.7692 |
| 0.0031 | 13.0 | 169 | 0.9395 | 0.7692 |
| 0.0029 | 14.0 | 182 | 0.9504 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/bert-base-bg-cs-pl-ru-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"bg",
"cs",
"pl",
"ru",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
"task_specific_params": {
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}
} | 1,614 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-32-4
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: 0.5001
- Accuracy: 0.7650
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7175 | 1.0 | 13 | 0.6822 | 0.5385 |
| 0.6559 | 2.0 | 26 | 0.6533 | 0.6154 |
| 0.6052 | 3.0 | 39 | 0.5762 | 0.7692 |
| 0.4587 | 4.0 | 52 | 0.4477 | 0.8462 |
| 0.2459 | 5.0 | 65 | 0.4288 | 0.7692 |
| 0.1001 | 6.0 | 78 | 0.5219 | 0.7692 |
| 0.0308 | 7.0 | 91 | 0.8540 | 0.7692 |
| 0.014 | 8.0 | 104 | 0.7789 | 0.7692 |
| 0.0083 | 9.0 | 117 | 0.7996 | 0.7692 |
| 0.0064 | 10.0 | 130 | 0.8342 | 0.7692 |
| 0.0049 | 11.0 | 143 | 0.8612 | 0.7692 |
| 0.0036 | 12.0 | 156 | 0.8834 | 0.7692 |
| 0.0032 | 13.0 | 169 | 0.9067 | 0.7692 |
| 0.003 | 14.0 | 182 | 0.9332 | 0.7692 |
| 0.0028 | 15.0 | 195 | 0.9511 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/bert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"en",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
"model_type": "bert",
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}
} | 3,009 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-32-5
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: 0.6248
- Accuracy: 0.6826
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7136 | 1.0 | 13 | 0.6850 | 0.5385 |
| 0.6496 | 2.0 | 26 | 0.6670 | 0.6154 |
| 0.5895 | 3.0 | 39 | 0.6464 | 0.7692 |
| 0.4271 | 4.0 | 52 | 0.6478 | 0.7692 |
| 0.2182 | 5.0 | 65 | 0.6809 | 0.6923 |
| 0.103 | 6.0 | 78 | 0.9119 | 0.6923 |
| 0.0326 | 7.0 | 91 | 1.0718 | 0.6923 |
| 0.0154 | 8.0 | 104 | 1.0721 | 0.7692 |
| 0.0087 | 9.0 | 117 | 1.1416 | 0.7692 |
| 0.0067 | 10.0 | 130 | 1.2088 | 0.7692 |
| 0.005 | 11.0 | 143 | 1.2656 | 0.7692 |
| 0.0037 | 12.0 | 156 | 1.3104 | 0.7692 |
| 0.0032 | 13.0 | 169 | 1.3428 | 0.6923 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/bert-base-multilingual-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"multilingual",
"arxiv:1704.05426",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers"
] | feature-extraction | {
"architectures": [
"BertModel"
],
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},
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}
} | 140 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-6
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__sst2__train-32-6
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: 0.5072
- Accuracy: 0.7650
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 13 | 0.6704 | 0.6923 |
| 0.6489 | 2.0 | 26 | 0.6228 | 0.8462 |
| 0.5475 | 3.0 | 39 | 0.5079 | 0.8462 |
| 0.4014 | 4.0 | 52 | 0.4203 | 0.8462 |
| 0.1923 | 5.0 | 65 | 0.3872 | 0.8462 |
| 0.1014 | 6.0 | 78 | 0.4909 | 0.8462 |
| 0.0349 | 7.0 | 91 | 0.5460 | 0.8462 |
| 0.0173 | 8.0 | 104 | 0.4867 | 0.8462 |
| 0.0098 | 9.0 | 117 | 0.5274 | 0.8462 |
| 0.0075 | 10.0 | 130 | 0.6086 | 0.8462 |
| 0.0057 | 11.0 | 143 | 0.6604 | 0.8462 |
| 0.0041 | 12.0 | 156 | 0.6904 | 0.8462 |
| 0.0037 | 13.0 | 169 | 0.7164 | 0.8462 |
| 0.0034 | 14.0 | 182 | 0.7368 | 0.8462 |
| 0.0031 | 15.0 | 195 | 0.7565 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/distilrubert-base-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
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} | 6,324 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-7
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__sst2__train-32-7
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: 0.6736
- Accuracy: 0.5931
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7094 | 1.0 | 13 | 0.6887 | 0.5385 |
| 0.651 | 2.0 | 26 | 0.6682 | 0.6923 |
| 0.6084 | 3.0 | 39 | 0.6412 | 0.6923 |
| 0.4547 | 4.0 | 52 | 0.6095 | 0.6923 |
| 0.2903 | 5.0 | 65 | 0.6621 | 0.6923 |
| 0.1407 | 6.0 | 78 | 0.7130 | 0.7692 |
| 0.0444 | 7.0 | 91 | 0.9007 | 0.6923 |
| 0.0176 | 8.0 | 104 | 0.9525 | 0.7692 |
| 0.0098 | 9.0 | 117 | 1.0289 | 0.7692 |
| 0.0071 | 10.0 | 130 | 1.0876 | 0.7692 |
| 0.0052 | 11.0 | 143 | 1.1431 | 0.6923 |
| 0.0038 | 12.0 | 156 | 1.1687 | 0.7692 |
| 0.0034 | 13.0 | 169 | 1.1792 | 0.7692 |
| 0.0031 | 14.0 | 182 | 1.2033 | 0.7692 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/distilrubert-tiny-cased-conversational-v1 | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
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}
} | 9,141 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-8
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__sst2__train-32-8
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: 0.6880
- Accuracy: 0.5014
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.712 | 1.0 | 13 | 0.6936 | 0.5385 |
| 0.665 | 2.0 | 26 | 0.6960 | 0.3846 |
| 0.6112 | 3.0 | 39 | 0.7138 | 0.3846 |
| 0.4521 | 4.0 | 52 | 0.8243 | 0.4615 |
| 0.2627 | 5.0 | 65 | 0.7723 | 0.6154 |
| 0.0928 | 6.0 | 78 | 1.2666 | 0.5385 |
| 0.0312 | 7.0 | 91 | 1.2306 | 0.6154 |
| 0.0132 | 8.0 | 104 | 1.3385 | 0.6154 |
| 0.0082 | 9.0 | 117 | 1.4584 | 0.6154 |
| 0.0063 | 10.0 | 130 | 1.5429 | 0.6154 |
| 0.0049 | 11.0 | 143 | 1.5913 | 0.6154 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/distilrubert-tiny-cased-conversational | [
"pytorch",
"distilbert",
"ru",
"arxiv:2205.02340",
"transformers"
] | null | {
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} | 5,993 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-32-9
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__sst2__train-32-9
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: 0.5625
- Accuracy: 0.7353
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 13 | 0.6805 | 0.5385 |
| 0.6642 | 2.0 | 26 | 0.6526 | 0.7692 |
| 0.5869 | 3.0 | 39 | 0.5773 | 0.8462 |
| 0.4085 | 4.0 | 52 | 0.4959 | 0.8462 |
| 0.2181 | 5.0 | 65 | 0.4902 | 0.6923 |
| 0.069 | 6.0 | 78 | 0.5065 | 0.8462 |
| 0.0522 | 7.0 | 91 | 0.6082 | 0.7692 |
| 0.0135 | 8.0 | 104 | 0.6924 | 0.7692 |
| 0.0084 | 9.0 | 117 | 0.5921 | 0.7692 |
| 0.0061 | 10.0 | 130 | 0.6477 | 0.7692 |
| 0.0047 | 11.0 | 143 | 0.6648 | 0.7692 |
| 0.0035 | 12.0 | 156 | 0.6640 | 0.7692 |
| 0.0031 | 13.0 | 169 | 0.6615 | 0.7692 |
| 0.0029 | 14.0 | 182 | 0.6605 | 0.7692 |
| 0.0026 | 15.0 | 195 | 0.6538 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/marianmt-tatoeba-enru | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-0
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__sst2__train-8-0
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: 0.6920
- Accuracy: 0.5189
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6916 | 1.0 | 3 | 0.7035 | 0.25 |
| 0.6852 | 2.0 | 6 | 0.7139 | 0.25 |
| 0.6533 | 3.0 | 9 | 0.7192 | 0.25 |
| 0.6211 | 4.0 | 12 | 0.7322 | 0.25 |
| 0.5522 | 5.0 | 15 | 0.7561 | 0.25 |
| 0.488 | 6.0 | 18 | 0.7883 | 0.25 |
| 0.48 | 7.0 | 21 | 0.8224 | 0.25 |
| 0.3948 | 8.0 | 24 | 0.8605 | 0.25 |
| 0.3478 | 9.0 | 27 | 0.8726 | 0.25 |
| 0.2723 | 10.0 | 30 | 0.8885 | 0.25 |
| 0.2174 | 11.0 | 33 | 0.8984 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/marianmt-tatoeba-ruen | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
"architectures": [
"MarianMTModel"
],
"model_type": "marian",
"task_specific_params": {
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} | 30 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-1
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. -->
# distilbert-base-uncased__sst2__train-8-1
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: 0.6930
- Accuracy: 0.5047
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7082 | 1.0 | 3 | 0.7048 | 0.25 |
| 0.6761 | 2.0 | 6 | 0.7249 | 0.25 |
| 0.6653 | 3.0 | 9 | 0.7423 | 0.25 |
| 0.6212 | 4.0 | 12 | 0.7727 | 0.25 |
| 0.5932 | 5.0 | 15 | 0.8098 | 0.25 |
| 0.5427 | 6.0 | 18 | 0.8496 | 0.25 |
| 0.5146 | 7.0 | 21 | 0.8992 | 0.25 |
| 0.4356 | 8.0 | 24 | 0.9494 | 0.25 |
| 0.4275 | 9.0 | 27 | 0.9694 | 0.25 |
| 0.3351 | 10.0 | 30 | 0.9968 | 0.25 |
| 0.2812 | 11.0 | 33 | 1.0056 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/roberta-large-winogrande | [
"pytorch",
"roberta",
"text-classification",
"en",
"dataset:winogrande",
"arxiv:1907.11692",
"transformers"
] | text-classification | {
"architectures": [
"RobertaForSequenceClassification"
],
"model_type": "roberta",
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} | 348 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6932
- Accuracy: 0.4931
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7081 | 1.0 | 3 | 0.7031 | 0.25 |
| 0.6853 | 2.0 | 6 | 0.7109 | 0.25 |
| 0.6696 | 3.0 | 9 | 0.7211 | 0.25 |
| 0.6174 | 4.0 | 12 | 0.7407 | 0.25 |
| 0.5717 | 5.0 | 15 | 0.7625 | 0.25 |
| 0.5096 | 6.0 | 18 | 0.7732 | 0.25 |
| 0.488 | 7.0 | 21 | 0.7798 | 0.25 |
| 0.4023 | 8.0 | 24 | 0.7981 | 0.25 |
| 0.3556 | 9.0 | 27 | 0.8110 | 0.25 |
| 0.2714 | 10.0 | 30 | 0.8269 | 0.25 |
| 0.2295 | 11.0 | 33 | 0.8276 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/rubert-base-cased-conversational | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"transformers",
"has_space"
] | feature-extraction | {
"architectures": [
"BertModel"
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} | 17,362 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-3
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__sst2__train-8-3
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: 0.6914
- Accuracy: 0.5195
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6931 | 1.0 | 3 | 0.7039 | 0.25 |
| 0.6615 | 2.0 | 6 | 0.7186 | 0.25 |
| 0.653 | 3.0 | 9 | 0.7334 | 0.25 |
| 0.601 | 4.0 | 12 | 0.7592 | 0.25 |
| 0.5555 | 5.0 | 15 | 0.7922 | 0.25 |
| 0.4832 | 6.0 | 18 | 0.8179 | 0.25 |
| 0.4565 | 7.0 | 21 | 0.8285 | 0.25 |
| 0.3996 | 8.0 | 24 | 0.8559 | 0.25 |
| 0.3681 | 9.0 | 27 | 0.8586 | 0.5 |
| 0.2901 | 10.0 | 30 | 0.8646 | 0.5 |
| 0.241 | 11.0 | 33 | 0.8524 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/rubert-base-cased-sentence | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1508.05326",
"arxiv:1809.05053",
"arxiv:1908.10084",
"transformers",
"has_space"
] | feature-extraction | {
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} | 46,991 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-4
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: 0.6921
- Accuracy: 0.5107
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.7100 | 0.25 |
| 0.6785 | 2.0 | 6 | 0.7209 | 0.25 |
| 0.6455 | 3.0 | 9 | 0.7321 | 0.25 |
| 0.6076 | 4.0 | 12 | 0.7517 | 0.25 |
| 0.5593 | 5.0 | 15 | 0.7780 | 0.25 |
| 0.5202 | 6.0 | 18 | 0.7990 | 0.25 |
| 0.4967 | 7.0 | 21 | 0.8203 | 0.25 |
| 0.4158 | 8.0 | 24 | 0.8497 | 0.25 |
| 0.3997 | 9.0 | 27 | 0.8638 | 0.25 |
| 0.3064 | 10.0 | 30 | 0.8732 | 0.25 |
| 0.2618 | 11.0 | 33 | 0.8669 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/rubert-base-cased | [
"pytorch",
"jax",
"bert",
"feature-extraction",
"ru",
"arxiv:1905.07213",
"transformers",
"has_space"
] | feature-extraction | {
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}
} | 148,127 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__sst2__train-8-5
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: 0.8419
- Accuracy: 0.6172
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7057 | 1.0 | 3 | 0.6848 | 0.75 |
| 0.6681 | 2.0 | 6 | 0.6875 | 0.5 |
| 0.6591 | 3.0 | 9 | 0.6868 | 0.25 |
| 0.6052 | 4.0 | 12 | 0.6943 | 0.25 |
| 0.557 | 5.0 | 15 | 0.7078 | 0.25 |
| 0.4954 | 6.0 | 18 | 0.7168 | 0.25 |
| 0.4593 | 7.0 | 21 | 0.7185 | 0.25 |
| 0.3936 | 8.0 | 24 | 0.7212 | 0.25 |
| 0.3699 | 9.0 | 27 | 0.6971 | 0.5 |
| 0.2916 | 10.0 | 30 | 0.6827 | 0.5 |
| 0.2511 | 11.0 | 33 | 0.6464 | 0.5 |
| 0.2109 | 12.0 | 36 | 0.6344 | 0.75 |
| 0.1655 | 13.0 | 39 | 0.6377 | 0.75 |
| 0.1412 | 14.0 | 42 | 0.6398 | 0.75 |
| 0.1157 | 15.0 | 45 | 0.6315 | 0.75 |
| 0.0895 | 16.0 | 48 | 0.6210 | 0.75 |
| 0.0783 | 17.0 | 51 | 0.5918 | 0.75 |
| 0.0606 | 18.0 | 54 | 0.5543 | 0.75 |
| 0.0486 | 19.0 | 57 | 0.5167 | 0.75 |
| 0.0405 | 20.0 | 60 | 0.4862 | 0.75 |
| 0.0376 | 21.0 | 63 | 0.4644 | 0.75 |
| 0.0294 | 22.0 | 66 | 0.4497 | 0.75 |
| 0.0261 | 23.0 | 69 | 0.4428 | 0.75 |
| 0.0238 | 24.0 | 72 | 0.4408 | 0.75 |
| 0.0217 | 25.0 | 75 | 0.4392 | 0.75 |
| 0.0187 | 26.0 | 78 | 0.4373 | 0.75 |
| 0.0177 | 27.0 | 81 | 0.4360 | 0.75 |
| 0.0136 | 28.0 | 84 | 0.4372 | 0.75 |
| 0.0144 | 29.0 | 87 | 0.4368 | 0.75 |
| 0.014 | 30.0 | 90 | 0.4380 | 0.75 |
| 0.0137 | 31.0 | 93 | 0.4383 | 0.75 |
| 0.0133 | 32.0 | 96 | 0.4409 | 0.75 |
| 0.013 | 33.0 | 99 | 0.4380 | 0.75 |
| 0.0096 | 34.0 | 102 | 0.4358 | 0.75 |
| 0.012 | 35.0 | 105 | 0.4339 | 0.75 |
| 0.0122 | 36.0 | 108 | 0.4305 | 0.75 |
| 0.0109 | 37.0 | 111 | 0.4267 | 0.75 |
| 0.0121 | 38.0 | 114 | 0.4231 | 0.75 |
| 0.0093 | 39.0 | 117 | 0.4209 | 0.75 |
| 0.0099 | 40.0 | 120 | 0.4199 | 0.75 |
| 0.0091 | 41.0 | 123 | 0.4184 | 0.75 |
| 0.0116 | 42.0 | 126 | 0.4173 | 0.75 |
| 0.01 | 43.0 | 129 | 0.4163 | 0.75 |
| 0.0098 | 44.0 | 132 | 0.4153 | 0.75 |
| 0.0101 | 45.0 | 135 | 0.4155 | 0.75 |
| 0.0088 | 46.0 | 138 | 0.4149 | 0.75 |
| 0.0087 | 47.0 | 141 | 0.4150 | 0.75 |
| 0.0093 | 48.0 | 144 | 0.4147 | 0.75 |
| 0.0081 | 49.0 | 147 | 0.4147 | 0.75 |
| 0.009 | 50.0 | 150 | 0.4150 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/xlm-roberta-large-en-ru-mnli | [
"pytorch",
"xlm-roberta",
"text-classification",
"en",
"ru",
"dataset:glue",
"dataset:mnli",
"transformers",
"xlm-roberta-large",
"xlm-roberta-large-en-ru",
"xlm-roberta-large-en-ru-mnli",
"has_space"
] | text-classification | {
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"XLMRobertaForSequenceClassification"
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}
}
} | 227 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-6
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__sst2__train-8-6
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: 0.5336
- Accuracy: 0.7523
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7161 | 1.0 | 3 | 0.6941 | 0.5 |
| 0.6786 | 2.0 | 6 | 0.7039 | 0.25 |
| 0.6586 | 3.0 | 9 | 0.7090 | 0.25 |
| 0.6121 | 4.0 | 12 | 0.7183 | 0.25 |
| 0.5696 | 5.0 | 15 | 0.7266 | 0.25 |
| 0.522 | 6.0 | 18 | 0.7305 | 0.25 |
| 0.4899 | 7.0 | 21 | 0.7339 | 0.25 |
| 0.3985 | 8.0 | 24 | 0.7429 | 0.25 |
| 0.3758 | 9.0 | 27 | 0.7224 | 0.25 |
| 0.2876 | 10.0 | 30 | 0.7068 | 0.5 |
| 0.2498 | 11.0 | 33 | 0.6751 | 0.75 |
| 0.1921 | 12.0 | 36 | 0.6487 | 0.75 |
| 0.1491 | 13.0 | 39 | 0.6261 | 0.75 |
| 0.1276 | 14.0 | 42 | 0.6102 | 0.75 |
| 0.0996 | 15.0 | 45 | 0.5964 | 0.75 |
| 0.073 | 16.0 | 48 | 0.6019 | 0.75 |
| 0.0627 | 17.0 | 51 | 0.5933 | 0.75 |
| 0.053 | 18.0 | 54 | 0.5768 | 0.75 |
| 0.0403 | 19.0 | 57 | 0.5698 | 0.75 |
| 0.0328 | 20.0 | 60 | 0.5656 | 0.75 |
| 0.03 | 21.0 | 63 | 0.5634 | 0.75 |
| 0.025 | 22.0 | 66 | 0.5620 | 0.75 |
| 0.0209 | 23.0 | 69 | 0.5623 | 0.75 |
| 0.0214 | 24.0 | 72 | 0.5606 | 0.75 |
| 0.0191 | 25.0 | 75 | 0.5565 | 0.75 |
| 0.0173 | 26.0 | 78 | 0.5485 | 0.75 |
| 0.0175 | 27.0 | 81 | 0.5397 | 0.75 |
| 0.0132 | 28.0 | 84 | 0.5322 | 0.75 |
| 0.0138 | 29.0 | 87 | 0.5241 | 0.75 |
| 0.0128 | 30.0 | 90 | 0.5235 | 0.75 |
| 0.0126 | 31.0 | 93 | 0.5253 | 0.75 |
| 0.012 | 32.0 | 96 | 0.5317 | 0.75 |
| 0.0118 | 33.0 | 99 | 0.5342 | 0.75 |
| 0.0092 | 34.0 | 102 | 0.5388 | 0.75 |
| 0.0117 | 35.0 | 105 | 0.5414 | 0.75 |
| 0.0124 | 36.0 | 108 | 0.5453 | 0.75 |
| 0.0109 | 37.0 | 111 | 0.5506 | 0.75 |
| 0.0112 | 38.0 | 114 | 0.5555 | 0.75 |
| 0.0087 | 39.0 | 117 | 0.5597 | 0.75 |
| 0.01 | 40.0 | 120 | 0.5640 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeepPavlov/xlm-roberta-large-en-ru | [
"pytorch",
"xlm-roberta",
"feature-extraction",
"en",
"ru",
"transformers"
] | feature-extraction | {
"architectures": [
"XLMRobertaModel"
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} | 190 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-7
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__sst2__train-8-7
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: 0.6950
- Accuracy: 0.4618
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7156 | 1.0 | 3 | 0.6965 | 0.25 |
| 0.6645 | 2.0 | 6 | 0.7059 | 0.25 |
| 0.6368 | 3.0 | 9 | 0.7179 | 0.25 |
| 0.5944 | 4.0 | 12 | 0.7408 | 0.25 |
| 0.5369 | 5.0 | 15 | 0.7758 | 0.25 |
| 0.449 | 6.0 | 18 | 0.8009 | 0.25 |
| 0.4352 | 7.0 | 21 | 0.8209 | 0.5 |
| 0.3462 | 8.0 | 24 | 0.8470 | 0.5 |
| 0.3028 | 9.0 | 27 | 0.8579 | 0.5 |
| 0.2365 | 10.0 | 30 | 0.8704 | 0.5 |
| 0.2023 | 11.0 | 33 | 0.8770 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeividasM/wav2vec2-large-xlsr-53-lithuanian | [
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"lt",
"dataset:common_voice",
"transformers",
"audio",
"speech",
"xlsr-fine-tuning-week",
"license:apache-2.0",
"model-index"
] | automatic-speech-recognition | {
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"Wav2Vec2ForCTC"
],
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}
} | 7 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-8
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__sst2__train-8-8
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: 0.6925
- Accuracy: 0.5200
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7061 | 1.0 | 3 | 0.6899 | 0.75 |
| 0.6627 | 2.0 | 6 | 0.7026 | 0.25 |
| 0.644 | 3.0 | 9 | 0.7158 | 0.25 |
| 0.6087 | 4.0 | 12 | 0.7325 | 0.25 |
| 0.5602 | 5.0 | 15 | 0.7555 | 0.25 |
| 0.5034 | 6.0 | 18 | 0.7725 | 0.25 |
| 0.4672 | 7.0 | 21 | 0.7983 | 0.25 |
| 0.403 | 8.0 | 24 | 0.8314 | 0.25 |
| 0.3571 | 9.0 | 27 | 0.8555 | 0.25 |
| 0.2792 | 10.0 | 30 | 0.9065 | 0.25 |
| 0.2373 | 11.0 | 33 | 0.9286 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeltaHub/adapter_t5-3b_cola | [
"pytorch",
"transformers"
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} | 3 | 2022-02-02T21:07:51Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst2__train-8-9
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__sst2__train-8-9
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: 0.6925
- Accuracy: 0.5140
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7204 | 1.0 | 3 | 0.7025 | 0.5 |
| 0.6885 | 2.0 | 6 | 0.7145 | 0.5 |
| 0.6662 | 3.0 | 9 | 0.7222 | 0.5 |
| 0.6182 | 4.0 | 12 | 0.7427 | 0.25 |
| 0.5707 | 5.0 | 15 | 0.7773 | 0.25 |
| 0.5247 | 6.0 | 18 | 0.8137 | 0.25 |
| 0.5003 | 7.0 | 21 | 0.8556 | 0.25 |
| 0.4195 | 8.0 | 24 | 0.9089 | 0.5 |
| 0.387 | 9.0 | 27 | 0.9316 | 0.25 |
| 0.2971 | 10.0 | 30 | 0.9558 | 0.25 |
| 0.2581 | 11.0 | 33 | 0.9420 | 0.25 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeltaHub/adapter_t5-3b_mrpc | [
"pytorch",
"transformers"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__sst5__all-train
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__sst5__all-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3757
- Accuracy: 0.5045
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2492 | 1.0 | 534 | 1.1163 | 0.4991 |
| 0.9937 | 2.0 | 1068 | 1.1232 | 0.5122 |
| 0.7867 | 3.0 | 1602 | 1.2097 | 0.5045 |
| 0.595 | 4.0 | 2136 | 1.3757 | 0.5045 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DeltaHub/adapter_t5-3b_qnli | [
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"transformers"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__all-train
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__subj__all-train
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3193
- Accuracy: 0.9485
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1992 | 1.0 | 500 | 0.1236 | 0.963 |
| 0.084 | 2.0 | 1000 | 0.1428 | 0.963 |
| 0.0333 | 3.0 | 1500 | 0.1906 | 0.965 |
| 0.0159 | 4.0 | 2000 | 0.3193 | 0.9485 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DeltaHub/lora_t5-base_mrpc | [
"pytorch",
"transformers"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-0
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__subj__train-8-0
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: 0.4440
- Accuracy: 0.789
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.6868 | 0.5 |
| 0.6683 | 2.0 | 6 | 0.6804 | 0.75 |
| 0.6375 | 3.0 | 9 | 0.6702 | 0.75 |
| 0.5997 | 4.0 | 12 | 0.6686 | 0.75 |
| 0.5345 | 5.0 | 15 | 0.6720 | 0.75 |
| 0.4673 | 6.0 | 18 | 0.6646 | 0.75 |
| 0.4214 | 7.0 | 21 | 0.6494 | 0.75 |
| 0.3439 | 8.0 | 24 | 0.6313 | 0.75 |
| 0.3157 | 9.0 | 27 | 0.6052 | 0.75 |
| 0.2329 | 10.0 | 30 | 0.5908 | 0.75 |
| 0.1989 | 11.0 | 33 | 0.5768 | 0.75 |
| 0.1581 | 12.0 | 36 | 0.5727 | 0.75 |
| 0.1257 | 13.0 | 39 | 0.5678 | 0.75 |
| 0.1005 | 14.0 | 42 | 0.5518 | 0.75 |
| 0.0836 | 15.0 | 45 | 0.5411 | 0.75 |
| 0.0611 | 16.0 | 48 | 0.5320 | 0.75 |
| 0.0503 | 17.0 | 51 | 0.5299 | 0.75 |
| 0.0407 | 18.0 | 54 | 0.5368 | 0.75 |
| 0.0332 | 19.0 | 57 | 0.5455 | 0.75 |
| 0.0293 | 20.0 | 60 | 0.5525 | 0.75 |
| 0.0254 | 21.0 | 63 | 0.5560 | 0.75 |
| 0.0231 | 22.0 | 66 | 0.5569 | 0.75 |
| 0.0201 | 23.0 | 69 | 0.5572 | 0.75 |
| 0.0179 | 24.0 | 72 | 0.5575 | 0.75 |
| 0.0184 | 25.0 | 75 | 0.5547 | 0.75 |
| 0.0148 | 26.0 | 78 | 0.5493 | 0.75 |
| 0.0149 | 27.0 | 81 | 0.5473 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DemangeJeremy/4-sentiments-with-flaubert | [
"pytorch",
"flaubert",
"text-classification",
"fr",
"transformers",
"sentiments",
"french",
"flaubert-large"
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} | 226 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-1
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. -->
# distilbert-base-uncased__subj__train-8-1
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: 0.5488
- Accuracy: 0.791
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.703 | 1.0 | 3 | 0.6906 | 0.5 |
| 0.666 | 2.0 | 6 | 0.6945 | 0.25 |
| 0.63 | 3.0 | 9 | 0.6885 | 0.5 |
| 0.588 | 4.0 | 12 | 0.6888 | 0.25 |
| 0.5181 | 5.0 | 15 | 0.6899 | 0.25 |
| 0.4508 | 6.0 | 18 | 0.6770 | 0.5 |
| 0.4025 | 7.0 | 21 | 0.6579 | 0.5 |
| 0.3361 | 8.0 | 24 | 0.6392 | 0.5 |
| 0.2919 | 9.0 | 27 | 0.6113 | 0.5 |
| 0.2151 | 10.0 | 30 | 0.5774 | 0.75 |
| 0.1728 | 11.0 | 33 | 0.5248 | 0.75 |
| 0.1313 | 12.0 | 36 | 0.4824 | 0.75 |
| 0.1046 | 13.0 | 39 | 0.4456 | 0.75 |
| 0.0858 | 14.0 | 42 | 0.4076 | 0.75 |
| 0.0679 | 15.0 | 45 | 0.3755 | 0.75 |
| 0.0485 | 16.0 | 48 | 0.3422 | 0.75 |
| 0.0416 | 17.0 | 51 | 0.3055 | 0.75 |
| 0.0358 | 18.0 | 54 | 0.2731 | 1.0 |
| 0.0277 | 19.0 | 57 | 0.2443 | 1.0 |
| 0.0234 | 20.0 | 60 | 0.2187 | 1.0 |
| 0.0223 | 21.0 | 63 | 0.1960 | 1.0 |
| 0.0187 | 22.0 | 66 | 0.1762 | 1.0 |
| 0.017 | 23.0 | 69 | 0.1629 | 1.0 |
| 0.0154 | 24.0 | 72 | 0.1543 | 1.0 |
| 0.0164 | 25.0 | 75 | 0.1476 | 1.0 |
| 0.0131 | 26.0 | 78 | 0.1423 | 1.0 |
| 0.0139 | 27.0 | 81 | 0.1387 | 1.0 |
| 0.0107 | 28.0 | 84 | 0.1360 | 1.0 |
| 0.0108 | 29.0 | 87 | 0.1331 | 1.0 |
| 0.0105 | 30.0 | 90 | 0.1308 | 1.0 |
| 0.0106 | 31.0 | 93 | 0.1276 | 1.0 |
| 0.0104 | 32.0 | 96 | 0.1267 | 1.0 |
| 0.0095 | 33.0 | 99 | 0.1255 | 1.0 |
| 0.0076 | 34.0 | 102 | 0.1243 | 1.0 |
| 0.0094 | 35.0 | 105 | 0.1235 | 1.0 |
| 0.0103 | 36.0 | 108 | 0.1228 | 1.0 |
| 0.0086 | 37.0 | 111 | 0.1231 | 1.0 |
| 0.0094 | 38.0 | 114 | 0.1236 | 1.0 |
| 0.0074 | 39.0 | 117 | 0.1240 | 1.0 |
| 0.0085 | 40.0 | 120 | 0.1246 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.1253 | 1.0 |
| 0.0088 | 42.0 | 126 | 0.1248 | 1.0 |
| 0.0082 | 43.0 | 129 | 0.1244 | 1.0 |
| 0.0082 | 44.0 | 132 | 0.1234 | 1.0 |
| 0.0082 | 45.0 | 135 | 0.1223 | 1.0 |
| 0.0071 | 46.0 | 138 | 0.1212 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.1208 | 1.0 |
| 0.0081 | 48.0 | 144 | 0.1205 | 1.0 |
| 0.0067 | 49.0 | 147 | 0.1202 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.1202 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Denilson/gbert-base-germaner | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3081
- Accuracy: 0.8755
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7146 | 1.0 | 3 | 0.6798 | 0.75 |
| 0.6737 | 2.0 | 6 | 0.6847 | 0.75 |
| 0.6519 | 3.0 | 9 | 0.6783 | 0.75 |
| 0.6105 | 4.0 | 12 | 0.6812 | 0.25 |
| 0.5463 | 5.0 | 15 | 0.6869 | 0.25 |
| 0.4922 | 6.0 | 18 | 0.6837 | 0.5 |
| 0.4543 | 7.0 | 21 | 0.6716 | 0.5 |
| 0.3856 | 8.0 | 24 | 0.6613 | 0.75 |
| 0.3475 | 9.0 | 27 | 0.6282 | 0.75 |
| 0.2717 | 10.0 | 30 | 0.6045 | 0.75 |
| 0.2347 | 11.0 | 33 | 0.5620 | 0.75 |
| 0.1979 | 12.0 | 36 | 0.5234 | 1.0 |
| 0.1535 | 13.0 | 39 | 0.4771 | 1.0 |
| 0.1332 | 14.0 | 42 | 0.4277 | 1.0 |
| 0.1041 | 15.0 | 45 | 0.3785 | 1.0 |
| 0.082 | 16.0 | 48 | 0.3318 | 1.0 |
| 0.0672 | 17.0 | 51 | 0.2885 | 1.0 |
| 0.0538 | 18.0 | 54 | 0.2568 | 1.0 |
| 0.0412 | 19.0 | 57 | 0.2356 | 1.0 |
| 0.0361 | 20.0 | 60 | 0.2217 | 1.0 |
| 0.0303 | 21.0 | 63 | 0.2125 | 1.0 |
| 0.0268 | 22.0 | 66 | 0.2060 | 1.0 |
| 0.0229 | 23.0 | 69 | 0.2015 | 1.0 |
| 0.0215 | 24.0 | 72 | 0.1989 | 1.0 |
| 0.0211 | 25.0 | 75 | 0.1969 | 1.0 |
| 0.0172 | 26.0 | 78 | 0.1953 | 1.0 |
| 0.0165 | 27.0 | 81 | 0.1935 | 1.0 |
| 0.0132 | 28.0 | 84 | 0.1923 | 1.0 |
| 0.0146 | 29.0 | 87 | 0.1914 | 1.0 |
| 0.0125 | 30.0 | 90 | 0.1904 | 1.0 |
| 0.0119 | 31.0 | 93 | 0.1897 | 1.0 |
| 0.0122 | 32.0 | 96 | 0.1886 | 1.0 |
| 0.0118 | 33.0 | 99 | 0.1875 | 1.0 |
| 0.0097 | 34.0 | 102 | 0.1866 | 1.0 |
| 0.0111 | 35.0 | 105 | 0.1861 | 1.0 |
| 0.0111 | 36.0 | 108 | 0.1855 | 1.0 |
| 0.0102 | 37.0 | 111 | 0.1851 | 1.0 |
| 0.0109 | 38.0 | 114 | 0.1851 | 1.0 |
| 0.0085 | 39.0 | 117 | 0.1854 | 1.0 |
| 0.0089 | 40.0 | 120 | 0.1855 | 1.0 |
| 0.0092 | 41.0 | 123 | 0.1863 | 1.0 |
| 0.0105 | 42.0 | 126 | 0.1868 | 1.0 |
| 0.0089 | 43.0 | 129 | 0.1874 | 1.0 |
| 0.0091 | 44.0 | 132 | 0.1877 | 1.0 |
| 0.0096 | 45.0 | 135 | 0.1881 | 1.0 |
| 0.0081 | 46.0 | 138 | 0.1881 | 1.0 |
| 0.0086 | 47.0 | 141 | 0.1883 | 1.0 |
| 0.009 | 48.0 | 144 | 0.1884 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Deniskin/emailer_medium_300 | [
"pytorch",
"gpt2",
"text-generation",
"transformers"
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} | 14 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-3
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__subj__train-8-3
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: 0.3496
- Accuracy: 0.859
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7136 | 1.0 | 3 | 0.6875 | 0.75 |
| 0.6702 | 2.0 | 6 | 0.6824 | 0.75 |
| 0.6456 | 3.0 | 9 | 0.6687 | 0.75 |
| 0.5934 | 4.0 | 12 | 0.6564 | 0.75 |
| 0.537 | 5.0 | 15 | 0.6428 | 0.75 |
| 0.4812 | 6.0 | 18 | 0.6180 | 0.75 |
| 0.4279 | 7.0 | 21 | 0.5864 | 0.75 |
| 0.3608 | 8.0 | 24 | 0.5540 | 0.75 |
| 0.3076 | 9.0 | 27 | 0.5012 | 1.0 |
| 0.2292 | 10.0 | 30 | 0.4497 | 1.0 |
| 0.1991 | 11.0 | 33 | 0.3945 | 1.0 |
| 0.1495 | 12.0 | 36 | 0.3483 | 1.0 |
| 0.1176 | 13.0 | 39 | 0.3061 | 1.0 |
| 0.0947 | 14.0 | 42 | 0.2683 | 1.0 |
| 0.0761 | 15.0 | 45 | 0.2295 | 1.0 |
| 0.0584 | 16.0 | 48 | 0.1996 | 1.0 |
| 0.0451 | 17.0 | 51 | 0.1739 | 1.0 |
| 0.0387 | 18.0 | 54 | 0.1521 | 1.0 |
| 0.0272 | 19.0 | 57 | 0.1333 | 1.0 |
| 0.0247 | 20.0 | 60 | 0.1171 | 1.0 |
| 0.0243 | 21.0 | 63 | 0.1044 | 1.0 |
| 0.0206 | 22.0 | 66 | 0.0943 | 1.0 |
| 0.0175 | 23.0 | 69 | 0.0859 | 1.0 |
| 0.0169 | 24.0 | 72 | 0.0799 | 1.0 |
| 0.0162 | 25.0 | 75 | 0.0746 | 1.0 |
| 0.0137 | 26.0 | 78 | 0.0705 | 1.0 |
| 0.0141 | 27.0 | 81 | 0.0674 | 1.0 |
| 0.0107 | 28.0 | 84 | 0.0654 | 1.0 |
| 0.0117 | 29.0 | 87 | 0.0634 | 1.0 |
| 0.0113 | 30.0 | 90 | 0.0617 | 1.0 |
| 0.0107 | 31.0 | 93 | 0.0599 | 1.0 |
| 0.0106 | 32.0 | 96 | 0.0585 | 1.0 |
| 0.0101 | 33.0 | 99 | 0.0568 | 1.0 |
| 0.0084 | 34.0 | 102 | 0.0553 | 1.0 |
| 0.0101 | 35.0 | 105 | 0.0539 | 1.0 |
| 0.0102 | 36.0 | 108 | 0.0529 | 1.0 |
| 0.009 | 37.0 | 111 | 0.0520 | 1.0 |
| 0.0092 | 38.0 | 114 | 0.0511 | 1.0 |
| 0.0073 | 39.0 | 117 | 0.0504 | 1.0 |
| 0.0081 | 40.0 | 120 | 0.0497 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.0492 | 1.0 |
| 0.0092 | 42.0 | 126 | 0.0488 | 1.0 |
| 0.008 | 43.0 | 129 | 0.0483 | 1.0 |
| 0.0087 | 44.0 | 132 | 0.0479 | 1.0 |
| 0.009 | 45.0 | 135 | 0.0474 | 1.0 |
| 0.0076 | 46.0 | 138 | 0.0470 | 1.0 |
| 0.0075 | 47.0 | 141 | 0.0467 | 1.0 |
| 0.008 | 48.0 | 144 | 0.0465 | 1.0 |
| 0.0069 | 49.0 | 147 | 0.0464 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.0464 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-4
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: 0.3305
- Accuracy: 0.8565
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6991 | 1.0 | 3 | 0.6772 | 0.75 |
| 0.6707 | 2.0 | 6 | 0.6704 | 0.75 |
| 0.6402 | 3.0 | 9 | 0.6608 | 1.0 |
| 0.5789 | 4.0 | 12 | 0.6547 | 0.75 |
| 0.5211 | 5.0 | 15 | 0.6434 | 0.75 |
| 0.454 | 6.0 | 18 | 0.6102 | 1.0 |
| 0.4187 | 7.0 | 21 | 0.5701 | 1.0 |
| 0.3401 | 8.0 | 24 | 0.5289 | 1.0 |
| 0.3107 | 9.0 | 27 | 0.4737 | 1.0 |
| 0.2381 | 10.0 | 30 | 0.4255 | 1.0 |
| 0.1982 | 11.0 | 33 | 0.3685 | 1.0 |
| 0.1631 | 12.0 | 36 | 0.3200 | 1.0 |
| 0.1234 | 13.0 | 39 | 0.2798 | 1.0 |
| 0.0993 | 14.0 | 42 | 0.2455 | 1.0 |
| 0.0781 | 15.0 | 45 | 0.2135 | 1.0 |
| 0.0586 | 16.0 | 48 | 0.1891 | 1.0 |
| 0.0513 | 17.0 | 51 | 0.1671 | 1.0 |
| 0.043 | 18.0 | 54 | 0.1427 | 1.0 |
| 0.0307 | 19.0 | 57 | 0.1225 | 1.0 |
| 0.0273 | 20.0 | 60 | 0.1060 | 1.0 |
| 0.0266 | 21.0 | 63 | 0.0920 | 1.0 |
| 0.0233 | 22.0 | 66 | 0.0823 | 1.0 |
| 0.0185 | 23.0 | 69 | 0.0751 | 1.0 |
| 0.0173 | 24.0 | 72 | 0.0698 | 1.0 |
| 0.0172 | 25.0 | 75 | 0.0651 | 1.0 |
| 0.0142 | 26.0 | 78 | 0.0613 | 1.0 |
| 0.0151 | 27.0 | 81 | 0.0583 | 1.0 |
| 0.0117 | 28.0 | 84 | 0.0563 | 1.0 |
| 0.0123 | 29.0 | 87 | 0.0546 | 1.0 |
| 0.0121 | 30.0 | 90 | 0.0531 | 1.0 |
| 0.0123 | 31.0 | 93 | 0.0511 | 1.0 |
| 0.0112 | 32.0 | 96 | 0.0496 | 1.0 |
| 0.0103 | 33.0 | 99 | 0.0481 | 1.0 |
| 0.0086 | 34.0 | 102 | 0.0468 | 1.0 |
| 0.0096 | 35.0 | 105 | 0.0457 | 1.0 |
| 0.0107 | 36.0 | 108 | 0.0447 | 1.0 |
| 0.0095 | 37.0 | 111 | 0.0439 | 1.0 |
| 0.0102 | 38.0 | 114 | 0.0429 | 1.0 |
| 0.0077 | 39.0 | 117 | 0.0422 | 1.0 |
| 0.0092 | 40.0 | 120 | 0.0415 | 1.0 |
| 0.0083 | 41.0 | 123 | 0.0409 | 1.0 |
| 0.0094 | 42.0 | 126 | 0.0404 | 1.0 |
| 0.0084 | 43.0 | 129 | 0.0400 | 1.0 |
| 0.0085 | 44.0 | 132 | 0.0396 | 1.0 |
| 0.0092 | 45.0 | 135 | 0.0392 | 1.0 |
| 0.0076 | 46.0 | 138 | 0.0389 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.0388 | 1.0 |
| 0.0085 | 48.0 | 144 | 0.0387 | 1.0 |
| 0.0071 | 49.0 | 147 | 0.0386 | 1.0 |
| 0.0079 | 50.0 | 150 | 0.0386 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Deniskin/essays_small_2000i | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased__subj__train-8-5
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: 0.6927
- Accuracy: 0.506
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7102 | 1.0 | 3 | 0.6790 | 0.75 |
| 0.6693 | 2.0 | 6 | 0.6831 | 0.75 |
| 0.6438 | 3.0 | 9 | 0.6876 | 0.75 |
| 0.6047 | 4.0 | 12 | 0.6970 | 0.75 |
| 0.547 | 5.0 | 15 | 0.7065 | 0.75 |
| 0.4885 | 6.0 | 18 | 0.7114 | 0.75 |
| 0.4601 | 7.0 | 21 | 0.7147 | 0.5 |
| 0.4017 | 8.0 | 24 | 0.7178 | 0.5 |
| 0.3474 | 9.0 | 27 | 0.7145 | 0.5 |
| 0.2624 | 10.0 | 30 | 0.7153 | 0.5 |
| 0.2175 | 11.0 | 33 | 0.7158 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Deniskin/gpt3_medium | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"has_space"
] | text-generation | {
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} | 52 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-6
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__subj__train-8-6
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: 0.6075
- Accuracy: 0.7485
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7163 | 1.0 | 3 | 0.6923 | 0.5 |
| 0.6648 | 2.0 | 6 | 0.6838 | 0.5 |
| 0.6329 | 3.0 | 9 | 0.6747 | 0.75 |
| 0.5836 | 4.0 | 12 | 0.6693 | 0.5 |
| 0.5287 | 5.0 | 15 | 0.6670 | 0.25 |
| 0.4585 | 6.0 | 18 | 0.6517 | 0.5 |
| 0.415 | 7.0 | 21 | 0.6290 | 0.5 |
| 0.3353 | 8.0 | 24 | 0.6019 | 0.5 |
| 0.2841 | 9.0 | 27 | 0.5613 | 0.75 |
| 0.2203 | 10.0 | 30 | 0.5222 | 1.0 |
| 0.1743 | 11.0 | 33 | 0.4769 | 1.0 |
| 0.1444 | 12.0 | 36 | 0.4597 | 1.0 |
| 0.1079 | 13.0 | 39 | 0.4462 | 1.0 |
| 0.0891 | 14.0 | 42 | 0.4216 | 1.0 |
| 0.0704 | 15.0 | 45 | 0.3880 | 1.0 |
| 0.0505 | 16.0 | 48 | 0.3663 | 1.0 |
| 0.0428 | 17.0 | 51 | 0.3536 | 1.0 |
| 0.0356 | 18.0 | 54 | 0.3490 | 1.0 |
| 0.0283 | 19.0 | 57 | 0.3531 | 1.0 |
| 0.025 | 20.0 | 60 | 0.3595 | 1.0 |
| 0.0239 | 21.0 | 63 | 0.3594 | 1.0 |
| 0.0202 | 22.0 | 66 | 0.3521 | 1.0 |
| 0.0168 | 23.0 | 69 | 0.3475 | 1.0 |
| 0.0159 | 24.0 | 72 | 0.3458 | 1.0 |
| 0.0164 | 25.0 | 75 | 0.3409 | 1.0 |
| 0.0132 | 26.0 | 78 | 0.3360 | 1.0 |
| 0.0137 | 27.0 | 81 | 0.3302 | 1.0 |
| 0.0112 | 28.0 | 84 | 0.3235 | 1.0 |
| 0.0113 | 29.0 | 87 | 0.3178 | 1.0 |
| 0.0111 | 30.0 | 90 | 0.3159 | 1.0 |
| 0.0113 | 31.0 | 93 | 0.3108 | 1.0 |
| 0.0107 | 32.0 | 96 | 0.3101 | 1.0 |
| 0.0101 | 33.0 | 99 | 0.3100 | 1.0 |
| 0.0083 | 34.0 | 102 | 0.3110 | 1.0 |
| 0.0092 | 35.0 | 105 | 0.3117 | 1.0 |
| 0.0102 | 36.0 | 108 | 0.3104 | 1.0 |
| 0.0086 | 37.0 | 111 | 0.3086 | 1.0 |
| 0.0092 | 38.0 | 114 | 0.3047 | 1.0 |
| 0.0072 | 39.0 | 117 | 0.3024 | 1.0 |
| 0.0079 | 40.0 | 120 | 0.3014 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.2983 | 1.0 |
| 0.0091 | 42.0 | 126 | 0.2948 | 1.0 |
| 0.0077 | 43.0 | 129 | 0.2915 | 1.0 |
| 0.0085 | 44.0 | 132 | 0.2890 | 1.0 |
| 0.009 | 45.0 | 135 | 0.2870 | 1.0 |
| 0.0073 | 46.0 | 138 | 0.2856 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.2844 | 1.0 |
| 0.0076 | 48.0 | 144 | 0.2841 | 1.0 |
| 0.0065 | 49.0 | 147 | 0.2836 | 1.0 |
| 0.0081 | 50.0 | 150 | 0.2835 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Denny29/DialoGPT-medium-asunayuuki | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
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} | 9 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-7
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__subj__train-8-7
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: 0.2766
- Accuracy: 0.8845
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7044 | 1.0 | 3 | 0.6909 | 0.5 |
| 0.6678 | 2.0 | 6 | 0.6901 | 0.5 |
| 0.6336 | 3.0 | 9 | 0.6807 | 0.5 |
| 0.5926 | 4.0 | 12 | 0.6726 | 0.5 |
| 0.5221 | 5.0 | 15 | 0.6648 | 0.5 |
| 0.4573 | 6.0 | 18 | 0.6470 | 0.5 |
| 0.4177 | 7.0 | 21 | 0.6251 | 0.5 |
| 0.3252 | 8.0 | 24 | 0.5994 | 0.5 |
| 0.2831 | 9.0 | 27 | 0.5529 | 0.5 |
| 0.213 | 10.0 | 30 | 0.5078 | 0.75 |
| 0.1808 | 11.0 | 33 | 0.4521 | 1.0 |
| 0.1355 | 12.0 | 36 | 0.3996 | 1.0 |
| 0.1027 | 13.0 | 39 | 0.3557 | 1.0 |
| 0.0862 | 14.0 | 42 | 0.3121 | 1.0 |
| 0.0682 | 15.0 | 45 | 0.2828 | 1.0 |
| 0.0517 | 16.0 | 48 | 0.2603 | 1.0 |
| 0.0466 | 17.0 | 51 | 0.2412 | 1.0 |
| 0.038 | 18.0 | 54 | 0.2241 | 1.0 |
| 0.0276 | 19.0 | 57 | 0.2096 | 1.0 |
| 0.0246 | 20.0 | 60 | 0.1969 | 1.0 |
| 0.0249 | 21.0 | 63 | 0.1859 | 1.0 |
| 0.0201 | 22.0 | 66 | 0.1770 | 1.0 |
| 0.018 | 23.0 | 69 | 0.1703 | 1.0 |
| 0.0164 | 24.0 | 72 | 0.1670 | 1.0 |
| 0.0172 | 25.0 | 75 | 0.1639 | 1.0 |
| 0.0135 | 26.0 | 78 | 0.1604 | 1.0 |
| 0.014 | 27.0 | 81 | 0.1585 | 1.0 |
| 0.0108 | 28.0 | 84 | 0.1569 | 1.0 |
| 0.0116 | 29.0 | 87 | 0.1549 | 1.0 |
| 0.0111 | 30.0 | 90 | 0.1532 | 1.0 |
| 0.0113 | 31.0 | 93 | 0.1513 | 1.0 |
| 0.0104 | 32.0 | 96 | 0.1503 | 1.0 |
| 0.01 | 33.0 | 99 | 0.1490 | 1.0 |
| 0.0079 | 34.0 | 102 | 0.1479 | 1.0 |
| 0.0097 | 35.0 | 105 | 0.1466 | 1.0 |
| 0.0112 | 36.0 | 108 | 0.1458 | 1.0 |
| 0.0091 | 37.0 | 111 | 0.1457 | 1.0 |
| 0.0098 | 38.0 | 114 | 0.1454 | 1.0 |
| 0.0076 | 39.0 | 117 | 0.1451 | 1.0 |
| 0.0085 | 40.0 | 120 | 0.1448 | 1.0 |
| 0.0079 | 41.0 | 123 | 0.1445 | 1.0 |
| 0.0096 | 42.0 | 126 | 0.1440 | 1.0 |
| 0.0081 | 43.0 | 129 | 0.1430 | 1.0 |
| 0.0083 | 44.0 | 132 | 0.1424 | 1.0 |
| 0.0088 | 45.0 | 135 | 0.1418 | 1.0 |
| 0.0077 | 46.0 | 138 | 0.1414 | 1.0 |
| 0.0073 | 47.0 | 141 | 0.1413 | 1.0 |
| 0.0084 | 48.0 | 144 | 0.1412 | 1.0 |
| 0.0072 | 49.0 | 147 | 0.1411 | 1.0 |
| 0.0077 | 50.0 | 150 | 0.1411 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
Denver/distilbert-base-uncased-finetuned-squad | [] | null | {
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} | 0 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-8
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__subj__train-8-8
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: 0.3160
- Accuracy: 0.8735
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7187 | 1.0 | 3 | 0.6776 | 1.0 |
| 0.684 | 2.0 | 6 | 0.6608 | 1.0 |
| 0.6532 | 3.0 | 9 | 0.6364 | 1.0 |
| 0.5996 | 4.0 | 12 | 0.6119 | 1.0 |
| 0.5242 | 5.0 | 15 | 0.5806 | 1.0 |
| 0.4612 | 6.0 | 18 | 0.5320 | 1.0 |
| 0.4192 | 7.0 | 21 | 0.4714 | 1.0 |
| 0.3274 | 8.0 | 24 | 0.4071 | 1.0 |
| 0.2871 | 9.0 | 27 | 0.3378 | 1.0 |
| 0.2082 | 10.0 | 30 | 0.2822 | 1.0 |
| 0.1692 | 11.0 | 33 | 0.2271 | 1.0 |
| 0.1242 | 12.0 | 36 | 0.1793 | 1.0 |
| 0.0977 | 13.0 | 39 | 0.1417 | 1.0 |
| 0.0776 | 14.0 | 42 | 0.1117 | 1.0 |
| 0.0631 | 15.0 | 45 | 0.0894 | 1.0 |
| 0.0453 | 16.0 | 48 | 0.0733 | 1.0 |
| 0.0399 | 17.0 | 51 | 0.0617 | 1.0 |
| 0.0333 | 18.0 | 54 | 0.0528 | 1.0 |
| 0.0266 | 19.0 | 57 | 0.0454 | 1.0 |
| 0.0234 | 20.0 | 60 | 0.0393 | 1.0 |
| 0.0223 | 21.0 | 63 | 0.0345 | 1.0 |
| 0.0195 | 22.0 | 66 | 0.0309 | 1.0 |
| 0.0161 | 23.0 | 69 | 0.0281 | 1.0 |
| 0.0167 | 24.0 | 72 | 0.0260 | 1.0 |
| 0.0163 | 25.0 | 75 | 0.0242 | 1.0 |
| 0.0134 | 26.0 | 78 | 0.0227 | 1.0 |
| 0.0128 | 27.0 | 81 | 0.0214 | 1.0 |
| 0.0101 | 28.0 | 84 | 0.0204 | 1.0 |
| 0.0109 | 29.0 | 87 | 0.0194 | 1.0 |
| 0.0112 | 30.0 | 90 | 0.0186 | 1.0 |
| 0.0108 | 31.0 | 93 | 0.0179 | 1.0 |
| 0.011 | 32.0 | 96 | 0.0174 | 1.0 |
| 0.0099 | 33.0 | 99 | 0.0169 | 1.0 |
| 0.0083 | 34.0 | 102 | 0.0164 | 1.0 |
| 0.0096 | 35.0 | 105 | 0.0160 | 1.0 |
| 0.01 | 36.0 | 108 | 0.0156 | 1.0 |
| 0.0084 | 37.0 | 111 | 0.0152 | 1.0 |
| 0.0089 | 38.0 | 114 | 0.0149 | 1.0 |
| 0.0073 | 39.0 | 117 | 0.0146 | 1.0 |
| 0.0082 | 40.0 | 120 | 0.0143 | 1.0 |
| 0.008 | 41.0 | 123 | 0.0141 | 1.0 |
| 0.0093 | 42.0 | 126 | 0.0139 | 1.0 |
| 0.0078 | 43.0 | 129 | 0.0138 | 1.0 |
| 0.0086 | 44.0 | 132 | 0.0136 | 1.0 |
| 0.009 | 45.0 | 135 | 0.0135 | 1.0 |
| 0.0072 | 46.0 | 138 | 0.0134 | 1.0 |
| 0.0075 | 47.0 | 141 | 0.0133 | 1.0 |
| 0.0082 | 48.0 | 144 | 0.0133 | 1.0 |
| 0.0068 | 49.0 | 147 | 0.0132 | 1.0 |
| 0.0074 | 50.0 | 150 | 0.0132 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeskDown/MarianMixFT_en-fil | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased__subj__train-8-9
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__subj__train-8-9
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: 0.4865
- Accuracy: 0.778
## 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: 50
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7024 | 1.0 | 3 | 0.6843 | 0.75 |
| 0.67 | 2.0 | 6 | 0.6807 | 0.5 |
| 0.6371 | 3.0 | 9 | 0.6677 | 0.5 |
| 0.585 | 4.0 | 12 | 0.6649 | 0.5 |
| 0.5122 | 5.0 | 15 | 0.6707 | 0.5 |
| 0.4379 | 6.0 | 18 | 0.6660 | 0.5 |
| 0.4035 | 7.0 | 21 | 0.6666 | 0.5 |
| 0.323 | 8.0 | 24 | 0.6672 | 0.5 |
| 0.2841 | 9.0 | 27 | 0.6534 | 0.5 |
| 0.21 | 10.0 | 30 | 0.6456 | 0.5 |
| 0.1735 | 11.0 | 33 | 0.6325 | 0.5 |
| 0.133 | 12.0 | 36 | 0.6214 | 0.5 |
| 0.0986 | 13.0 | 39 | 0.6351 | 0.5 |
| 0.081 | 14.0 | 42 | 0.6495 | 0.5 |
| 0.0638 | 15.0 | 45 | 0.6671 | 0.5 |
| 0.0449 | 16.0 | 48 | 0.7156 | 0.5 |
| 0.0399 | 17.0 | 51 | 0.7608 | 0.5 |
| 0.0314 | 18.0 | 54 | 0.7796 | 0.5 |
| 0.0243 | 19.0 | 57 | 0.7789 | 0.5 |
| 0.0227 | 20.0 | 60 | 0.7684 | 0.5 |
| 0.0221 | 21.0 | 63 | 0.7628 | 0.5 |
| 0.0192 | 22.0 | 66 | 0.7728 | 0.5 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
DeskDown/MarianMixFT_en-ms | [
"pytorch",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
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} | 5 | null | ---
language: cs
license: cc-by-4.0
---
# Small-E-Czech
Small-E-Czech is an [Electra](https://arxiv.org/abs/2003.10555)-small model pretrained on a Czech web corpus created at [Seznam.cz](https://www.seznam.cz/) and introduced in an [IAAI 2022 paper](https://arxiv.org/abs/2112.01810). Like other pretrained models, it should be finetuned on a downstream task of interest before use. At Seznam.cz, it has helped improve [web search ranking](https://blog.seznam.cz/2021/02/vyhledavani-pomoci-vyznamovych-vektoru/), query typo correction or clickbait titles detection. We release it under [CC BY 4.0 license](https://creativecommons.org/licenses/by/4.0/) (i.e. allowing commercial use). To raise an issue, please visit our [github](https://github.com/seznam/small-e-czech).
### How to use the discriminator in transformers
```python
from transformers import ElectraForPreTraining, ElectraTokenizerFast
import torch
discriminator = ElectraForPreTraining.from_pretrained("Seznam/small-e-czech")
tokenizer = ElectraTokenizerFast.from_pretrained("Seznam/small-e-czech")
sentence = "Za hory, za doly, mé zlaté parohy"
fake_sentence = "Za hory, za doly, kočka zlaté parohy"
fake_sentence_tokens = ["[CLS]"] + tokenizer.tokenize(fake_sentence) + ["[SEP]"]
fake_inputs = tokenizer.encode(fake_sentence, return_tensors="pt")
outputs = discriminator(fake_inputs)
predictions = torch.nn.Sigmoid()(outputs[0]).cpu().detach().numpy()
for token in fake_sentence_tokens:
print("{:>7s}".format(token), end="")
print()
for prediction in predictions.squeeze():
print("{:7.1f}".format(prediction), end="")
print()
```
In the output we can see the probabilities of particular tokens not belonging in the sentence (i.e. having been faked by the generator) according to the discriminator:
```
[CLS] za hory , za dol ##y , kočka zlaté paro ##hy [SEP]
0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.8 0.3 0.2 0.1 0.0
```
### Finetuning
For instructions on how to finetune the model on a new task, see the official HuggingFace transformers [tutorial](https://huggingface.co/transformers/training.html). |
DeskDown/MarianMix_en-ja-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"MarianMTModel"
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}
} | 1 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
datasets:
- mlsum
language: de
metrics:
- rouge
model-index:
- name: mode-bart-deutsch
results:
- task:
name: Summarization
type: summarization
dataset:
name: mlsum de
type: mlsum
args: de
metrics:
- name: Rouge1
type: rouge
value: 41.698
---
<!-- 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. -->
# mode-bart-deutsch
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on the mlsum de dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2152
- Rouge1: 41.698
- Rouge2: 31.3548
- Rougel: 38.2817
- Rougelsum: 39.6349
- Gen Len: 63.1723
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DeskDown/MarianMix_en-zh-10 | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 3 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
- summarization
datasets:
- mlsum
language: de
metrics:
- rouge
model-index:
- name: t5-seven-epoch-base-german
results:
- task:
name: Summarization
type: summarization
dataset:
name: mlsum de
type: mlsum
args: de
metrics:
- name: Rouge1
type: rouge
value: 42.3787
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-seven-epoch-base-german
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the mlsum de dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5491
- Rouge1: 42.3787
- Rouge2: 32.0253
- Rougel: 38.9529
- Rougelsum: 40.4544
- Gen Len: 47.7873
## 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: 6
- eval_batch_size: 6
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7.0
### Training results
### Framework versions
- Transformers 4.16.0.dev0
- Pytorch 1.10.0+cu111
- Datasets 1.17.0
- Tokenizers 0.10.3
|
DeskDown/MarianMix_en-zh_to_vi-ms-hi-ja | [
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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} | 5 | null | ---
tags:
- conversational
---
# Spongebob DialoGPT model |
Despin89/test | [] | null | {
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} | 0 | null | # ChineseBERT-base
This repository contains code, model, dataset for **ChineseBERT** at ACL2021.
paper:
**[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)**
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code:
[ChineseBERT github link](https://github.com/ShannonAI/ChineseBert)
## Model description
We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese
characters into language model pretraining.
First, for each Chinese character, we get three kind of embedding.
- **Char Embedding:** the same as origin BERT token embedding.
- **Glyph Embedding:** capture visual features based on different fonts of a Chinese character.
- **Pinyin Embedding:** capture phonetic feature from the pinyin sequence ot a Chinese Character.
Then, char embedding, glyph embedding and pinyin embedding
are first concatenated, and mapped to a D-dimensional embedding through a fully
connected layer to form the fusion embedding.
Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model.
The following image shows an overview architecture of ChineseBERT model.
ChineseBERT leverages the glyph and pinyin information of Chinese
characters to enhance the model's ability of capturing
context semantics from surface character forms and
disambiguating polyphonic characters in Chinese. |
Dev-DGT/food-dbert-multiling | [
"pytorch",
"distilbert",
"token-classification",
"transformers",
"autotrain_compatible"
] | token-classification | {
"architectures": [
"DistilBertForTokenClassification"
],
"model_type": "distilbert",
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}
} | 17 | null | # ChineseBERT-large
This repository contains code, model, dataset for **ChineseBERT** at ACL2021.
paper:
**[ChineseBERT: Chinese Pretraining Enhanced by Glyph and Pinyin Information](https://arxiv.org/abs/2106.16038)**
*Zijun Sun, Xiaoya Li, Xiaofei Sun, Yuxian Meng, Xiang Ao, Qing He, Fei Wu and Jiwei Li*
code:
[ChineseBERT github link](https://github.com/ShannonAI/ChineseBert)
## Model description
We propose ChineseBERT, which incorporates both the glyph and pinyin information of Chinese
characters into language model pretraining.
First, for each Chinese character, we get three kind of embedding.
- **Char Embedding:** the same as origin BERT token embedding.
- **Glyph Embedding:** capture visual features based on different fonts of a Chinese character.
- **Pinyin Embedding:** capture phonetic feature from the pinyin sequence ot a Chinese Character.
Then, char embedding, glyph embedding and pinyin embedding
are first concatenated, and mapped to a D-dimensional embedding through a fully
connected layer to form the fusion embedding.
Finally, the fusion embedding is added with the position embedding, which is fed as input to the BERT model.
The following image shows an overview architecture of ChineseBERT model.
ChineseBERT leverages the glyph and pinyin information of Chinese
characters to enhance the model's ability of capturing
context semantics from surface character forms and
disambiguating polyphonic characters in Chinese. |
Devrim/prism-default | [
"license:mit"
] | null | {
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} | 0 | null | ---
metrics:
- accuracy
widget:
- text: "In war resolution, in defeat defiance, in victory magnanimity"
- text: "en la guerra resolución en la derrota desafío en la victoria magnanimidad"
---
[](https://colab.research.google.com/drive/1dqeUwS_DZ-urrmYzB29nTCBUltwJxhbh?usp=sharing)
# 22 Language Identifier - BERT
This model is trained to identify the following 22 different languages.
- Arabic
- Chinese
- Dutch
- English
- Estonian
- French
- Hindi
- Indonesian
- Japanese
- Korean
- Latin
- Persian
- Portugese
- Pushto
- Romanian
- Russian
- Spanish
- Swedish
- Tamil
- Thai
- Turkish
- Urdu
## Loading the model
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
tokenizer = AutoTokenizer.from_pretrained("SharanSMenon/22-languages-bert-base-cased")
model = AutoModelForSequenceClassification.from_pretrained("SharanSMenon/22-languages-bert-base-cased")
```
## Inference
```python
def predict(sentence):
tokenized = tokenizer(sentence, return_tensors="pt")
outputs = model(**tokenized)
return model.config.id2label[outputs.logits.argmax(dim=1).item()]
```
### Examples
```python
sentence1 = "in war resolution, in defeat defiance, in victory magnanimity"
predict(sentence1) # English
sentence2 = "en la guerra resolución en la derrota desafío en la victoria magnanimidad"
predict(sentence2) # Spanish
sentence3 = "هذا هو أعظم إله على الإطلاق"
predict(sentence3) # Arabic
``` |
Dhruva/Interstellar | [] | null | {
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} | 0 | 2021-11-18T12:08:00Z | ---
language:
- ru
- en
pipeline_tag: text2text-generation
tags:
- PyTorch
- Transformers
- gpt2
- squad
- lm-head
- casual-lm
thumbnail: "https://github.com/RussianNLP/RusEnQA"
---
## RusEnQA
QA for Russian and English based on the [rugpt3xl](https://huggingface.co/sberbank-ai/rugpt3xl) model
### Fine-tuning format:
```
"<s>paragraph: "+eng_context+"\nlang: rus\nquestion: "+rus_question+' answer: '+ rus_answer+"</s>"
```
### About ruGPT-3 XL model
Model was trained with 512 sequence length using [Deepspeed](https://github.com/microsoft/DeepSpeed) and [Megatron](https://github.com/NVIDIA/Megatron-LM) code by [SberDevices](https://sberdevices.ru/) team, on 80B tokens dataset for 4 epochs. After that model was finetuned 1 epoch with sequence length 2048.
*Note! Model has sparse attention blocks.*
Total training time was around 10 days on 256 GPUs.
Final perplexity on test set is 12.05. Model parameters: 1.3B. |
DimaOrekhov/cubert-method-name | [
"pytorch",
"encoder-decoder",
"text2text-generation",
"transformers",
"autotrain_compatible"
] | text2text-generation | {
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"EncoderDecoderModel"
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}
} | 10 | null | ---
tags:
- conversational
---
# SHAY0 Dialo GPT Model |
Dimedrolza/DialoGPT-small-cyberpunk | [
"pytorch",
"gpt2",
"text-generation",
"transformers",
"conversational"
] | conversational | {
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"GPT2LMHeadModel"
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}
} | 9 | null | ---
tags:
- conversational
---
# Harry Potter DialGPT Model |
DivyanshuSheth/T5-Seq2Seq-Final | [] | null | {
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}
} | 0 | null | ---
tags:
- conversational
---
#isla DialoGPT Model
|
distilbert-base-uncased | [
"pytorch",
"tf",
"jax",
"rust",
"safetensors",
"distilbert",
"fill-mask",
"en",
"dataset:bookcorpus",
"dataset:wikipedia",
"arxiv:1910.01108",
"transformers",
"exbert",
"license:apache-2.0",
"autotrain_compatible",
"has_space"
] | fill-mask | {
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"DistilBertForMaskedLM"
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}
} | 10,887,471 | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: superglue-boolq
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. -->
# superglue-boolq
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2098
- Accuracy: 76.7584
- Average Metrics: 76.7584
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Average Metrics |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------------:|
| No log | 0.34 | 100 | 0.2293 | 73.2722 | 73.2722 |
| No log | 0.68 | 200 | 0.2098 | 76.7584 | 76.7584 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.10.2+cu111
- Datasets 1.17.0
- Tokenizers 0.12.1
|
AdapterHub/bert-base-uncased-pf-squad_v2 | [
"bert",
"en",
"dataset:squad_v2",
"arxiv:2104.08247",
"adapter-transformers",
"question-answering",
"adapterhub:qa/squad2"
] | question-answering | {
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}
} | 10 | null | ---
language:
- en
- de
- es
license: mit
tags:
- sentence_embedding
--- |
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