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25
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201
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|
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
question-answering
| null |
<div align = "center">
<img src = "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true">
</div>
This dataset contains the [**google/rembert**](https://huggingface.co/transformers/model_doc/rembert.html) model weights according to my team's experimentation strategy during the [**chaii - Hindi and Tamil Question Answering**](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition. They are listed below with their corresponding public LB score:-
| Huggingface Hub Link | Public LB Score |
| :---: | :---: |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii) | 0.724 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii) | 0.723 |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii) | 0.737 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii) | 0.725 |
|
{"language": "multilingual", "license": "cc0-1.0", "tags": ["kaggle", "rembert", "pytorch", "question-answering"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true", "inference": false}
|
SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii
| null |
[
"kaggle",
"rembert",
"pytorch",
"question-answering",
"multilingual",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"multilingual"
] |
TAGS
#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us
|
![]()
This dataset contains the google/rembert model weights according to my team's experimentation strategy during the chaii - Hindi and Tamil Question Answering competition. They are listed below with their corresponding public LB score:-
|
[] |
[
"TAGS\n#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us \n"
] |
question-answering
| null |
<div align = "center">
<img src = "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true">
</div>
This dataset contains the [**google/rembert**](https://huggingface.co/transformers/model_doc/rembert.html) model weights according to my team's experimentation strategy during the [**chaii - Hindi and Tamil Question Answering**](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition. They are listed below with their corresponding public LB score:-
| Huggingface Hub Link | Public LB Score |
| :---: | :---: |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii) | 0.724 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii) | 0.723 |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii) | 0.737 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii) | 0.725 |
|
{"language": "multilingual", "license": "cc0-1.0", "tags": ["kaggle", "rembert", "pytorch", "question-answering"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true", "inference": false}
|
SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii
| null |
[
"kaggle",
"rembert",
"pytorch",
"question-answering",
"multilingual",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"multilingual"
] |
TAGS
#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us
|
![]()
This dataset contains the google/rembert model weights according to my team's experimentation strategy during the chaii - Hindi and Tamil Question Answering competition. They are listed below with their corresponding public LB score:-
|
[] |
[
"TAGS\n#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us \n"
] |
question-answering
| null |
<div align = "center">
<img src = "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true">
</div>
This dataset contains the [**google/rembert**](https://huggingface.co/transformers/model_doc/rembert.html) model weights according to my team's experimentation strategy during the [**chaii - Hindi and Tamil Question Answering**](https://www.kaggle.com/c/chaii-hindi-and-tamil-question-answering) competition. They are listed below with their corresponding public LB score:-
| Huggingface Hub Link | Public LB Score |
| :---: | :---: |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-128-chaii) | 0.724 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-135-chaii) | 0.723 |
| [**SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii) | 0.737 |
| [**SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii**](https://huggingface.co/SauravMaheshkar/rembert-maxseq-384-docstride-128-chaii) | 0.725 |
|
{"language": "multilingual", "license": "cc0-1.0", "tags": ["kaggle", "rembert", "pytorch", "question-answering"], "datasets": ["Commonlit-Readibility"], "thumbnail": "https://github.com/SauravMaheshkar/chaii-Hindi-Tamil-QA/blob/main/assets/Coffee%20Banner.png?raw=true", "inference": false}
|
SauravMaheshkar/rembert-maxseq-400-docstride-135-chaii
| null |
[
"kaggle",
"rembert",
"pytorch",
"question-answering",
"multilingual",
"dataset:Commonlit-Readibility",
"license:cc0-1.0",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"multilingual"
] |
TAGS
#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us
|
![]()
This dataset contains the google/rembert model weights according to my team's experimentation strategy during the chaii - Hindi and Tamil Question Answering competition. They are listed below with their corresponding public LB score:-
|
[] |
[
"TAGS\n#kaggle #rembert #pytorch #question-answering #multilingual #dataset-Commonlit-Readibility #license-cc0-1.0 #region-us \n"
] |
image-classification
|
transformers
|
Practice/Demo repository following the tutorial `run_image_classification_flax.py` script
|
{}
|
SauravMaheshkar/vit-base-patch16-imagenette
| null |
[
"transformers",
"jax",
"tensorboard",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #jax #tensorboard #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us
|
Practice/Demo repository following the tutorial 'run_image_classification_flax.py' script
|
[] |
[
"TAGS\n#transformers #jax #tensorboard #vit #image-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation
|
transformers
|
# My Awesome Model
|
{"tags": ["conversational"]}
|
Saviour/ChandlerBot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# My Awesome Model
|
[
"# My Awesome Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# My Awesome Model"
] |
text-generation
|
transformers
|
# Paimon DialoGPT Model
|
{"tags": ["conversational"]}
|
Saz/DialoGPT-small-paimon
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Paimon DialoGPT Model
|
[
"# Paimon DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Paimon DialoGPT Model"
] |
text-generation
|
transformers
|
# Saz DialoGPT Model
|
{"tags": ["conversational"]}
|
Saz/DialoGPT-small-saz
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Saz DialoGPT Model
|
[
"# Saz DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Saz DialoGPT Model"
] |
text-generation
|
transformers
|
#13th Doctor DialoGPT model
|
{"tags": ["conversational"]}
|
Science-geek32/DialoGPT-small-doctor
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
#13th Doctor DialoGPT model
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
13th doctor model DialoGPT-small
|
{"tags": ["conversational"]}
|
Science-geek32/DialoGPT-small-doctor2.0
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
13th doctor model DialoGPT-small
|
[] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
text-generation
|
transformers
|
# Sandal Bot
Quick and dumb model for a discord chat bot. Based on DialoGPT-Medium
|
{"tags": ["conversational"]}
|
Scoops/SandalBot
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sandal Bot
Quick and dumb model for a discord chat bot. Based on DialoGPT-Medium
|
[
"# Sandal Bot\n\nQuick and dumb model for a discord chat bot. Based on DialoGPT-Medium"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sandal Bot\n\nQuick and dumb model for a discord chat bot. Based on DialoGPT-Medium"
] |
text-generation
|
transformers
|
# 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-Scott")
model = AutoModelWithLMHead.from_pretrained("scottastrong/DialogGPT-medium-Scott")
# 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)))
```
|
{"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"}
|
ScottaStrong/DialogGPT-medium-Scott
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
|
[
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
text-generation
|
transformers
|
# 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)))
```
|
{"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"}
|
ScottaStrong/DialogGPT-medium-joshua
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
|
[
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
text-generation
|
transformers
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of [microsoft/DialoGPT-medium](https://huggingface.co/microsoft/DialoGPT-small) 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-small-Scott")
model = AutoModelWithLMHead.from_pretrained("scottastrong/DialogGPT-small-Scott")
# 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)))
```
|
{"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"}
|
ScottaStrong/DialogGPT-small-Scott
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
|
[
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
text-generation
|
transformers
|
# 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-small-joshua")
model = AutoModelWithLMHead.from_pretrained("scottastrong/DialogGPT-small-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)))
```
|
{"license": "mit", "tags": ["conversational"], "thumbnail": "https://huggingface.co/front/thumbnails/dialogpt.png"}
|
ScottaStrong/DialogGPT-small-joshua
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# DialoGPT Trained on the Speech of a Game Character
This is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.
I built a Discord AI chatbot based on this model. Check out my GitHub repo.
Chat with the model:
|
[
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #license-mit #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# DialoGPT Trained on the Speech of a Game Character\nThis is an instance of microsoft/DialoGPT-medium trained on a game character, Joshua from The World Ends With You. The data comes from a Kaggle game script dataset.\nI built a Discord AI chatbot based on this model. Check out my GitHub repo.\nChat with the model:"
] |
fill-mask
|
transformers
|
# dummy
this is only a dummy model originally based on RoBERT model
## intended uses and limitations
not intended to be used, same limitations as camembert-base model
## how to use
it cant be used (lol)
## training data
French subcorpus of the newly available multilingual corpus OSCAR
## training procedure
evaluated on multiple downstream tasks
## variable and metrics
not explicitly stated
## evaluation metrics
maybe OSCAR
## evaluation results
not explicitly stated
|
{"language": "fr", "license": "mit", "datasets": ["oscar"]}
|
SebastianS/dummy-model
| null |
[
"transformers",
"pytorch",
"camembert",
"fill-mask",
"fr",
"dataset:oscar",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"fr"
] |
TAGS
#transformers #pytorch #camembert #fill-mask #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
# dummy
this is only a dummy model originally based on RoBERT model
## intended uses and limitations
not intended to be used, same limitations as camembert-base model
## how to use
it cant be used (lol)
## training data
French subcorpus of the newly available multilingual corpus OSCAR
## training procedure
evaluated on multiple downstream tasks
## variable and metrics
not explicitly stated
## evaluation metrics
maybe OSCAR
## evaluation results
not explicitly stated
|
[
"# dummy\nthis is only a dummy model originally based on RoBERT model",
"## intended uses and limitations\nnot intended to be used, same limitations as camembert-base model",
"## how to use\nit cant be used (lol)",
"## training data\nFrench subcorpus of the newly available multilingual corpus OSCAR",
"## training procedure\nevaluated on multiple downstream tasks",
"## variable and metrics\nnot explicitly stated",
"## evaluation metrics\nmaybe OSCAR",
"## evaluation results\nnot explicitly stated"
] |
[
"TAGS\n#transformers #pytorch #camembert #fill-mask #fr #dataset-oscar #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"# dummy\nthis is only a dummy model originally based on RoBERT model",
"## intended uses and limitations\nnot intended to be used, same limitations as camembert-base model",
"## how to use\nit cant be used (lol)",
"## training data\nFrench subcorpus of the newly available multilingual corpus OSCAR",
"## training procedure\nevaluated on multiple downstream tasks",
"## variable and metrics\nnot explicitly stated",
"## evaluation metrics\nmaybe OSCAR",
"## evaluation results\nnot explicitly stated"
] |
text-generation
|
transformers
|
# Melchior DialoGPT Model
|
{"tags": ["conversational"]}
|
Sebastianthecrab/DialoGPT-small-melchior
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Melchior DialoGPT Model
|
[
"# Melchior DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Melchior DialoGPT Model"
] |
text-generation
|
transformers
|
# Sedged DialoGPT Model
|
{"tags": ["conversational"]}
|
Sedge/DialoGPT-small-Sedge
| null |
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Sedged DialoGPT Model
|
[
"# Sedged DialoGPT Model"
] |
[
"TAGS\n#transformers #pytorch #gpt2 #text-generation #conversational #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Sedged DialoGPT Model"
] |
automatic-speech-recognition
|
transformers
|
# wav2vec2-irish-lite Speech to Text
## Usage
The model can be used directly (without a language model) as follows:
```python
import torch
import torchaudio
from datasets import load_dataset
from transformers import Wav2Vec2ForCTC, Wav2Vec2Processor
test_dataset = load_dataset("common_voice", "ga-IE", split="test[:2%]")
processor = Wav2Vec2Processor.from_pretrained("Semih/wav2vec2_Irish_Large")
model = Wav2Vec2ForCTC.from_pretrained("Semih/wav2vec2_Irish_Large")
resampler = torchaudio.transforms.Resample(48_000, 16_000)
```
Test Result: 55.11
|
{"language": "ga-IE", "license": "apache-2.0", "tags": ["audio", "automatic-speech-recognition", "speech"], "datasets": ["common_voice"], "metrics": ["wer"]}
|
Semih/wav2vec2_Irish_Large
| null |
[
"transformers",
"pytorch",
"jax",
"wav2vec2",
"automatic-speech-recognition",
"audio",
"speech",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"ga-IE"
] |
TAGS
#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us
|
# wav2vec2-irish-lite Speech to Text
## Usage
The model can be used directly (without a language model) as follows:
Test Result: 55.11
|
[
"# wav2vec2-irish-lite Speech to Text",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\nTest Result: 55.11"
] |
[
"TAGS\n#transformers #pytorch #jax #wav2vec2 #automatic-speech-recognition #audio #speech #dataset-common_voice #license-apache-2.0 #endpoints_compatible #region-us \n",
"# wav2vec2-irish-lite Speech to Text",
"## Usage\nThe model can be used directly (without a language model) as follows:\n\nTest Result: 55.11"
] |
image-classification
|
transformers
|
# dog
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### buldog
#### golden
#### pug
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
Sena/dog
| null |
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# dog
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### buldog
!buldog
#### golden
!golden
#### pug
!pug
|
[
"# dog\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### buldog\n\n!buldog",
"#### golden\n\n!golden",
"#### pug\n\n!pug"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# dog\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### buldog\n\n!buldog",
"#### golden\n\n!golden",
"#### pug\n\n!pug"
] |
image-classification
|
transformers
|
# flowers
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### karanfil
#### leylak
#### menekse
#### nergis
#### zambak
|
{"tags": ["image-classification", "pytorch", "huggingpics"], "metrics": ["accuracy"]}
|
Sena/flowers
| null |
[
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us
|
# flowers
Autogenerated by HuggingPics️
Create your own image classifier for anything by running the demo on Google Colab.
Report any issues with the demo at the github repo.
## Example Images
#### karanfil
!karanfil
#### leylak
!leylak
#### menekse
!menekse
#### nergis
!nergis
#### zambak
!zambak
|
[
"# flowers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### karanfil\n\n!karanfil",
"#### leylak\n\n!leylak",
"#### menekse\n\n!menekse",
"#### nergis\n\n!nergis",
"#### zambak\n\n!zambak"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #vit #image-classification #huggingpics #model-index #autotrain_compatible #endpoints_compatible #region-us \n",
"# flowers\n\n\nAutogenerated by HuggingPics️\n\nCreate your own image classifier for anything by running the demo on Google Colab.\n\nReport any issues with the demo at the github repo.",
"## Example Images",
"#### karanfil\n\n!karanfil",
"#### leylak\n\n!leylak",
"#### menekse\n\n!menekse",
"#### nergis\n\n!nergis",
"#### zambak\n\n!zambak"
] |
image-classification
| null |
# UniFormer (image model)
UniFormer models are trained on ImageNet at resolution 224x224.
It was introduced in the paper [UniFormer: Unifying Convolution and Self-attention for Visual Recognition](https://arxiv.org/abs/2201.09450) by Li et al,
and first released in [this repository](https://github.com/Sense-X/UniFormer).
## Model description
The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
Without any extra training data,
UniFormer achieves **86.3** top-1 accuracy on ImageNet-1K classification.
With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks.
UniFormer obtains **82.9/84.8** top-1 accuracy on Kinetics-400/600,
and **60.9/71.2** top-1 accuracy on Something-Something V1/V2 video classification tasks.
It also achieves **53.8** box AP and **46.4** mask AP on COCO object detection task,
**50.8** mIoU on ADE20K semantic segmentation task,
and **77.4** AP on COCO pose estimation task.
[Source](https://paperswithcode.com/paper/uniformer-unifying-convolution-and-self)
## Intended uses & limitations
You can use the raw model for image classification.
We now only upload the models trained without Token Labeling and Layer Scale.
More powerful models can be found in [the model hub](https://github.com/Sense-X/UniFormer/tree/main/image_classification).
### ImageNet
| Model | Pretrain | Resolution | Top-1 | #Param. | FLOPs |
| --------------- | ----------- | ---------- | ----- | ------- | ----- |
| UniFormer-S | ImageNet-1K | 224x224 | 82.9 | 22M | 3.6G |
| UniFormer-S† | ImageNet-1K | 224x224 | 83.4 | 24M | 4.2G |
| UniFormer-B | ImageNet-1K | 224x224 | 83.8 | 50M | 8.3G |
### How to use
You can followed our [demo](https://huggingface.co/spaces/Sense-X/uniformer_image_demo/tree/main) to use our models.
```python
from uniformer import uniformer_small
from imagenet_class_index import imagenet_classnames
model = uniformer_small()
# load state
model_path = hf_hub_download(repo_id="Sense-X/uniformer_image", filename="uniformer_small_in1k.pth")
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict)
# set to eval mode
model = model.to(device)
model = model.eval()
# process image
image = img
image_transform = T.Compose(
[
T.Resize(224),
T.CenterCrop(224),
T.ToTensor(),
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
]
)
image = image_transform(image)
image = image.unsqueeze(0)
# model predicts one of the 1000 ImageNet classes
prediction = model(image)
predicted_class_idx = prediction.flatten().argmax(-1).item()
print("Predicted class:", imagenet_classnames[str(predicted_class_idx)][1])
```
### BibTeX entry and citation info
```bibtex
@misc{li2022uniformer,
title={UniFormer: Unifying Convolution and Self-attention for Visual Recognition},
author={Kunchang Li and Yali Wang and Junhao Zhang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
year={2022},
eprint={2201.09450},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
{"license": "mit", "tags": ["vision", "image-classification"], "datasets": ["imagenet"]}
|
Sense-X/uniformer_image
| null |
[
"vision",
"image-classification",
"dataset:imagenet",
"arxiv:2201.09450",
"license:mit",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[
"2201.09450"
] |
[] |
TAGS
#vision #image-classification #dataset-imagenet #arxiv-2201.09450 #license-mit #has_space #region-us
|
UniFormer (image model)
=======================
UniFormer models are trained on ImageNet at resolution 224x224.
It was introduced in the paper UniFormer: Unifying Convolution and Self-attention for Visual Recognition by Li et al,
and first released in this repository.
Model description
-----------------
The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
Without any extra training data,
UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification.
With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks.
UniFormer obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600,
and 60.9/71.2 top-1 accuracy on Something-Something V1/V2 video classification tasks.
It also achieves 53.8 box AP and 46.4 mask AP on COCO object detection task,
50.8 mIoU on ADE20K semantic segmentation task,
and 77.4 AP on COCO pose estimation task.
!teaser
Source
Intended uses & limitations
---------------------------
You can use the raw model for image classification.
We now only upload the models trained without Token Labeling and Layer Scale.
More powerful models can be found in the model hub.
### ImageNet
### How to use
You can followed our demo to use our models.
### BibTeX entry and citation info
|
[
"### ImageNet",
"### How to use\n\n\nYou can followed our demo to use our models.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#vision #image-classification #dataset-imagenet #arxiv-2201.09450 #license-mit #has_space #region-us \n",
"### ImageNet",
"### How to use\n\n\nYou can followed our demo to use our models.",
"### BibTeX entry and citation info"
] |
video-classification
| null |
# UniFormer (video model)
UniFormer models are trained on [Kinetics](https://deepmind.com/research/open-source/kinetics) and [Something-Something](https://20bn.com/datasets/something-something) at resolution 224x224.
It was introduced in the paper [UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning](https://arxiv.org/abs/2201.04676) by Li et al,
and first released in [this repository](https://github.com/Sense-X/UniFormer).
## Model description
The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
Without any extra training data,
UniFormer achieves **86.3** top-1 accuracy on ImageNet-1K classification.
With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks.
UniFormer obtains **82.9/84.8** top-1 accuracy on Kinetics-400/600,
and **60.9/71.2** top-1 accuracy on Something-Something V1/V2 video classification tasks.
It also achieves **53.8** box AP and **46.4** mask AP on COCO object detection task,
**50.8** mIoU on ADE20K semantic segmentation task,
and **77.4** AP on COCO pose estimation task.
[Source](https://paperswithcode.com/paper/uniformer-unified-transformer-for-efficient)
## Intended uses & limitations
You can use the raw model for video classification.
We now only upload the powerful models with **single clip**.
More models can be found in [the model hub](https://github.com/Sense-X/UniFormer/tree/main/video_classification).
### Kinetics
| Model | #Frame | Sampling Stride | FLOPs | K400 Top-1 | K600 Top-1 |
| ----------- | ------ | --------------- | ----- | ---------- | ---------- |
| UniFormer-S | 16x1x1 | 8 | 41.8G | 78.4 | 80.8 |
| UniFormer-B | 16x1x1 | 8 | 96.7G | 79.3 | 81.7 |
| UniFormer-B | 32x1x1 | 4 | 259G | 80.9 | 82.4 |
### Something-Something
| Model | #Frame | FLOPs | SSV1 Top-1 | SSV2 Top-1 |
| ----------- | ------ | ----- | ---------- | ---------- |
| UniFormer-S | 16x1x1 | 41.8G | 54.4 | 65.0 |
| UniFormer-B | 32x1x1 | 259G | 58.0 | 67.5 |
### How to use
You can followed our [demo](https://huggingface.co/spaces/Sense-X/uniformer_video_demo/tree/main) to use our models.
```python
from uniformer import uniformer_small
from kinetics_class_index import kinetics_classnames
model = uniformer_small()
# load state
model_path = hf_hub_download(repo_id="Sense-X/uniformer_video", filename="uniformer_small_k400_16x8.pth")
state_dict = torch.load(model_path, map_location='cpu')
model.load_state_dict(state_dict)
# set to eval mode
model = model.to(device)
model = model.eval()
# please refer to the following url to process video of Kinetics:
# https://huggingface.co/spaces/Sense-X/uniformer_video_demo/blob/main/app.py
vid = load_video(video)
# model predicts one of the 400 Kintics classes
prediction = model(vid)
predicted_class_idx = prediction.flatten().argmax(-1).item()
print("Predicted class:", kinetics_classnames[str(predicted_class_idx)])
```
### BibTeX entry and citation info
```bibtex
@misc{li2022uniformer,
title={UniFormer: Unified Transformer for Efficient Spatiotemporal Representation Learning},
author={Kunchang Li and Yali Wang and Peng Gao and Guanglu Song and Yu Liu and Hongsheng Li and Yu Qiao},
year={2022},
eprint={2201.04676},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
|
{"license": "mit", "tags": ["vision", "video-classification"], "datasets": ["kinetics-400", "kinetics-600", "something-something-v1", "something-something-v2"]}
|
Sense-X/uniformer_video
| null |
[
"vision",
"video-classification",
"dataset:kinetics-400",
"dataset:kinetics-600",
"dataset:something-something-v1",
"dataset:something-something-v2",
"arxiv:2201.04676",
"license:mit",
"has_space",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[
"2201.04676"
] |
[] |
TAGS
#vision #video-classification #dataset-kinetics-400 #dataset-kinetics-600 #dataset-something-something-v1 #dataset-something-something-v2 #arxiv-2201.04676 #license-mit #has_space #region-us
|
UniFormer (video model)
=======================
UniFormer models are trained on Kinetics and Something-Something at resolution 224x224.
It was introduced in the paper UniFormer: Unified Transformer for Efficient Spatial-Temporal Representation Learning by Li et al,
and first released in this repository.
Model description
-----------------
The UniFormer is a type of Vision Transformer, which can seamlessly integrate merits of convolution and self-attention in a concise transformer format.
It adopt local MHRA in shallow layers to largely reduce computation burden and global MHRA in deep layers to learn global token relation.
Without any extra training data,
UniFormer achieves 86.3 top-1 accuracy on ImageNet-1K classification.
With only ImageNet-1K pre-training, it can simply achieve state-of-the-art performance in a broad range of downstream tasks.
UniFormer obtains 82.9/84.8 top-1 accuracy on Kinetics-400/600,
and 60.9/71.2 top-1 accuracy on Something-Something V1/V2 video classification tasks.
It also achieves 53.8 box AP and 46.4 mask AP on COCO object detection task,
50.8 mIoU on ADE20K semantic segmentation task,
and 77.4 AP on COCO pose estimation task.
!teaser
Source
Intended uses & limitations
---------------------------
You can use the raw model for video classification.
We now only upload the powerful models with single clip.
More models can be found in the model hub.
### Kinetics
### Something-Something
### How to use
You can followed our demo to use our models.
### BibTeX entry and citation info
|
[
"### Kinetics",
"### Something-Something",
"### How to use\n\n\nYou can followed our demo to use our models.",
"### BibTeX entry and citation info"
] |
[
"TAGS\n#vision #video-classification #dataset-kinetics-400 #dataset-kinetics-600 #dataset-something-something-v1 #dataset-something-something-v2 #arxiv-2201.04676 #license-mit #has_space #region-us \n",
"### Kinetics",
"### Something-Something",
"### How to use\n\n\nYou can followed our demo to use our models.",
"### BibTeX entry and citation info"
] |
text-generation
|
transformers
|
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*
|
{"language": "code", "license": "mit", "tags": ["Code", "GPyT", "code generator"]}
|
Sentdex/GPyT
| null |
[
"transformers",
"pytorch",
"tf",
"gpt2",
"text-generation",
"Code",
"GPyT",
"code generator",
"code",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[
"code"
] |
TAGS
#transformers #pytorch #tf #gpt2 #text-generation #Code #GPyT #code generator #code #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us
|
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:
Should produce:
This model does a ton more than just imports, however. For a bunch of examples and a better understanding of the model's capabilities:
URL
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*
|
[] |
[
"TAGS\n#transformers #pytorch #tf #gpt2 #text-generation #Code #GPyT #code generator #code #license-mit #autotrain_compatible #endpoints_compatible #has_space #text-generation-inference #region-us \n"
] |
question-answering
|
transformers
|
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0458
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0179 | 1.0 | 6194 | 0.9548 |
| 0.7277 | 2.0 | 12388 | 0.9717 |
| 0.507 | 3.0 | 18582 | 1.0458 |
### Framework versions
- Transformers 4.12.5
- Pytorch 1.10.0+cu111
- Datasets 1.16.1
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["squad"], "model-index": [{"name": "bert-base-cased-finetuned-squad", "results": []}]}
|
Seongkyu/bert-base-cased-finetuned-squad
| null |
[
"transformers",
"pytorch",
"tensorboard",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us
|
bert-base-cased-finetuned-squad
===============================
This model is a fine-tuned version of bert-base-cased on the squad dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0458
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.12.5
* Pytorch 1.10.0+cu111
* Datasets 1.16.1
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #tensorboard #bert #question-answering #generated_from_trainer #dataset-squad #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.12.5\n* Pytorch 1.10.0+cu111\n* Datasets 1.16.1\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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. -->
# MiniLM-L12-H384-uncased__sst2__all-train
This model is a fine-tuned version of [microsoft/MiniLM-L12-H384-uncased](https://huggingface.co/microsoft/MiniLM-L12-H384-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2632
- Accuracy: 0.9055
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4183 | 1.0 | 433 | 0.3456 | 0.8720 |
| 0.2714 | 2.0 | 866 | 0.2632 | 0.9055 |
| 0.2016 | 3.0 | 1299 | 0.3357 | 0.8990 |
| 0.1501 | 4.0 | 1732 | 0.4474 | 0.8863 |
| 0.1119 | 5.0 | 2165 | 0.3998 | 0.8979 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.1+cu102
- Datasets 1.17.0
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "MiniLM-L12-H384-uncased__sst2__all-train", "results": []}]}
|
SetFit/MiniLM-L12-H384-uncased__sst2__all-train
| null |
[
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
MiniLM-L12-H384-uncased\_\_sst2\_\_all-train
============================================
This model is a fine-tuned version of microsoft/MiniLM-L12-H384-uncased on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.2632
* Accuracy: 0.9055
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu102
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-base__sst2__all-train", "results": []}]}
|
SetFit/deberta-v3-base__sst2__all-train
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-base\_\_sst2\_\_all-train
====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-0", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-0
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-0
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-1", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-1
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-1
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-2", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-2
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-2
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-3", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-3
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-3
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-4", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-4
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-4
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-5
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: 0.5433
- Accuracy: 0.7924
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6774 | 1.0 | 7 | 0.7450 | 0.2857 |
| 0.7017 | 2.0 | 14 | 0.7552 | 0.2857 |
| 0.6438 | 3.0 | 21 | 0.7140 | 0.4286 |
| 0.3525 | 4.0 | 28 | 0.5570 | 0.7143 |
| 0.2061 | 5.0 | 35 | 0.5303 | 0.8571 |
| 0.0205 | 6.0 | 42 | 0.6706 | 0.8571 |
| 0.0068 | 7.0 | 49 | 0.8284 | 0.8571 |
| 0.0029 | 8.0 | 56 | 0.9281 | 0.8571 |
| 0.0015 | 9.0 | 63 | 0.9871 | 0.8571 |
| 0.0013 | 10.0 | 70 | 1.0208 | 0.8571 |
| 0.0008 | 11.0 | 77 | 1.0329 | 0.8571 |
| 0.0005 | 12.0 | 84 | 1.0348 | 0.8571 |
| 0.0004 | 13.0 | 91 | 1.0437 | 0.8571 |
| 0.0005 | 14.0 | 98 | 1.0512 | 0.8571 |
| 0.0004 | 15.0 | 105 | 1.0639 | 0.8571 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-5", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-5
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-5
======================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.5433
* Accuracy: 0.7924
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-6
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: 0.6846
- Accuracy: 0.5058
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6673 | 1.0 | 7 | 0.7580 | 0.2857 |
| 0.5896 | 2.0 | 14 | 0.7885 | 0.5714 |
| 0.5294 | 3.0 | 21 | 1.0040 | 0.4286 |
| 0.3163 | 4.0 | 28 | 1.1761 | 0.5714 |
| 0.1315 | 5.0 | 35 | 1.4315 | 0.4286 |
| 0.0312 | 6.0 | 42 | 2.6115 | 0.2857 |
| 0.1774 | 7.0 | 49 | 2.1631 | 0.5714 |
| 0.0052 | 8.0 | 56 | 2.3838 | 0.4286 |
| 0.0043 | 9.0 | 63 | 2.6553 | 0.4286 |
| 0.0032 | 10.0 | 70 | 2.2774 | 0.4286 |
| 0.0015 | 11.0 | 77 | 1.9467 | 0.7143 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-6", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-6
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-6
======================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6846
* Accuracy: 0.5058
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-7", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-7
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-7
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-16-8", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-8
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-8
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-9
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: 1.2598
- Accuracy: 0.7809
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6887 | 1.0 | 7 | 0.7452 | 0.2857 |
| 0.6889 | 2.0 | 14 | 0.7988 | 0.2857 |
| 0.6501 | 3.0 | 21 | 0.8987 | 0.2857 |
| 0.4286 | 4.0 | 28 | 0.9186 | 0.4286 |
| 0.3591 | 5.0 | 35 | 0.5566 | 0.7143 |
| 0.0339 | 6.0 | 42 | 1.1130 | 0.5714 |
| 0.013 | 7.0 | 49 | 1.8296 | 0.7143 |
| 0.0041 | 8.0 | 56 | 1.7069 | 0.7143 |
| 0.0023 | 9.0 | 63 | 1.1942 | 0.7143 |
| 0.0022 | 10.0 | 70 | 0.6054 | 0.7143 |
| 0.0011 | 11.0 | 77 | 0.3872 | 0.7143 |
| 0.0006 | 12.0 | 84 | 0.3217 | 0.7143 |
| 0.0005 | 13.0 | 91 | 0.2879 | 0.8571 |
| 0.0005 | 14.0 | 98 | 0.2640 | 0.8571 |
| 0.0004 | 15.0 | 105 | 0.2531 | 0.8571 |
| 0.0003 | 16.0 | 112 | 0.2384 | 0.8571 |
| 0.0004 | 17.0 | 119 | 0.2338 | 0.8571 |
| 0.0003 | 18.0 | 126 | 0.2314 | 0.8571 |
| 0.0003 | 19.0 | 133 | 0.2276 | 0.8571 |
| 0.0003 | 20.0 | 140 | 0.2172 | 0.8571 |
| 0.0003 | 21.0 | 147 | 0.2069 | 0.8571 |
| 0.0002 | 22.0 | 154 | 0.2018 | 0.8571 |
| 0.0002 | 23.0 | 161 | 0.2005 | 0.8571 |
| 0.0002 | 24.0 | 168 | 0.1985 | 0.8571 |
| 0.0002 | 25.0 | 175 | 0.1985 | 1.0 |
| 0.0002 | 26.0 | 182 | 0.1955 | 1.0 |
| 0.0002 | 27.0 | 189 | 0.1967 | 1.0 |
| 0.0002 | 28.0 | 196 | 0.1918 | 1.0 |
| 0.0002 | 29.0 | 203 | 0.1888 | 1.0 |
| 0.0002 | 30.0 | 210 | 0.1864 | 1.0 |
| 0.0002 | 31.0 | 217 | 0.1870 | 1.0 |
| 0.0002 | 32.0 | 224 | 0.1892 | 1.0 |
| 0.0002 | 33.0 | 231 | 0.1917 | 1.0 |
| 0.0002 | 34.0 | 238 | 0.1869 | 1.0 |
| 0.0002 | 35.0 | 245 | 0.1812 | 1.0 |
| 0.0001 | 36.0 | 252 | 0.1777 | 1.0 |
| 0.0002 | 37.0 | 259 | 0.1798 | 1.0 |
| 0.0002 | 38.0 | 266 | 0.1824 | 0.8571 |
| 0.0002 | 39.0 | 273 | 0.1846 | 0.8571 |
| 0.0002 | 40.0 | 280 | 0.1839 | 0.8571 |
| 0.0001 | 41.0 | 287 | 0.1826 | 0.8571 |
| 0.0001 | 42.0 | 294 | 0.1779 | 0.8571 |
| 0.0002 | 43.0 | 301 | 0.1762 | 0.8571 |
| 0.0001 | 44.0 | 308 | 0.1742 | 1.0 |
| 0.0002 | 45.0 | 315 | 0.1708 | 1.0 |
| 0.0001 | 46.0 | 322 | 0.1702 | 1.0 |
| 0.0001 | 47.0 | 329 | 0.1699 | 1.0 |
| 0.0001 | 48.0 | 336 | 0.1695 | 1.0 |
| 0.0001 | 49.0 | 343 | 0.1683 | 1.0 |
| 0.0001 | 50.0 | 350 | 0.1681 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-16-9", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-16-9
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-16-9
======================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2598
* Accuracy: 0.7809
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-32-0
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: 0.4849
- Accuracy: 0.7716
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7059 | 1.0 | 13 | 0.6840 | 0.5385 |
| 0.6595 | 2.0 | 26 | 0.6214 | 0.6923 |
| 0.4153 | 3.0 | 39 | 0.1981 | 0.9231 |
| 0.0733 | 4.0 | 52 | 0.5068 | 0.9231 |
| 0.2092 | 5.0 | 65 | 1.3114 | 0.6923 |
| 0.003 | 6.0 | 78 | 1.1062 | 0.8462 |
| 0.0012 | 7.0 | 91 | 1.5948 | 0.7692 |
| 0.0008 | 8.0 | 104 | 1.6913 | 0.7692 |
| 0.0006 | 9.0 | 117 | 1.7191 | 0.7692 |
| 0.0005 | 10.0 | 130 | 1.6527 | 0.7692 |
| 0.0003 | 11.0 | 143 | 1.4840 | 0.7692 |
| 0.0002 | 12.0 | 156 | 1.3076 | 0.8462 |
| 0.0002 | 13.0 | 169 | 1.3130 | 0.8462 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-32-0", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-32-0
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-32-0
======================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.4849
* Accuracy: 0.7716
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-32-1", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-32-1
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-32-1
======================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "microsoft/deberta-v3-large", "model-index": [{"name": "deberta-v3-large__sst2__train-8-0", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-0
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-0
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #base_model-microsoft/deberta-v3-large #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-1", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-1
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-1
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-8-2
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: 0.6794
- Accuracy: 0.6063
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6942 | 1.0 | 3 | 0.7940 | 0.25 |
| 0.6068 | 2.0 | 6 | 0.9326 | 0.25 |
| 0.6553 | 3.0 | 9 | 0.7979 | 0.25 |
| 0.475 | 4.0 | 12 | 0.7775 | 0.25 |
| 0.377 | 5.0 | 15 | 0.7477 | 0.25 |
| 0.3176 | 6.0 | 18 | 0.6856 | 0.75 |
| 0.2708 | 7.0 | 21 | 0.6554 | 0.75 |
| 0.2855 | 8.0 | 24 | 0.8129 | 0.5 |
| 0.148 | 9.0 | 27 | 0.7074 | 0.75 |
| 0.0947 | 10.0 | 30 | 0.7090 | 0.75 |
| 0.049 | 11.0 | 33 | 0.7885 | 0.75 |
| 0.0252 | 12.0 | 36 | 0.9203 | 0.75 |
| 0.0165 | 13.0 | 39 | 1.0937 | 0.75 |
| 0.0084 | 14.0 | 42 | 1.2502 | 0.75 |
| 0.0059 | 15.0 | 45 | 1.3726 | 0.75 |
| 0.0037 | 16.0 | 48 | 1.4784 | 0.75 |
| 0.003 | 17.0 | 51 | 1.5615 | 0.75 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-2", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-2
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-2
=====================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6794
* Accuracy: 0.6063
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-3", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-3
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-3
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-8-4
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: 1.3023
- Accuracy: 0.7057
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6816 | 1.0 | 3 | 0.8072 | 0.25 |
| 0.6672 | 2.0 | 6 | 0.8740 | 0.25 |
| 0.6667 | 3.0 | 9 | 0.8578 | 0.25 |
| 0.5346 | 4.0 | 12 | 1.0353 | 0.25 |
| 0.4517 | 5.0 | 15 | 1.1030 | 0.25 |
| 0.3095 | 6.0 | 18 | 0.9986 | 0.25 |
| 0.2464 | 7.0 | 21 | 0.9286 | 0.5 |
| 0.1342 | 8.0 | 24 | 0.4063 | 1.0 |
| 0.0851 | 9.0 | 27 | 0.2210 | 1.0 |
| 0.0491 | 10.0 | 30 | 0.2302 | 1.0 |
| 0.0211 | 11.0 | 33 | 0.4020 | 0.75 |
| 0.017 | 12.0 | 36 | 0.2382 | 1.0 |
| 0.0084 | 13.0 | 39 | 0.0852 | 1.0 |
| 0.0051 | 14.0 | 42 | 0.0354 | 1.0 |
| 0.0047 | 15.0 | 45 | 0.0208 | 1.0 |
| 0.0029 | 16.0 | 48 | 0.0155 | 1.0 |
| 0.0022 | 17.0 | 51 | 0.0139 | 1.0 |
| 0.0019 | 18.0 | 54 | 0.0144 | 1.0 |
| 0.0016 | 19.0 | 57 | 0.0168 | 1.0 |
| 0.0013 | 20.0 | 60 | 0.0231 | 1.0 |
| 0.0011 | 21.0 | 63 | 0.0369 | 1.0 |
| 0.0009 | 22.0 | 66 | 0.0528 | 1.0 |
| 0.001 | 23.0 | 69 | 0.0639 | 1.0 |
| 0.0009 | 24.0 | 72 | 0.0670 | 1.0 |
| 0.0009 | 25.0 | 75 | 0.0526 | 1.0 |
| 0.0008 | 26.0 | 78 | 0.0425 | 1.0 |
| 0.0011 | 27.0 | 81 | 0.0135 | 1.0 |
| 0.0007 | 28.0 | 84 | 0.0076 | 1.0 |
| 0.0007 | 29.0 | 87 | 0.0057 | 1.0 |
| 0.0007 | 30.0 | 90 | 0.0049 | 1.0 |
| 0.0008 | 31.0 | 93 | 0.0045 | 1.0 |
| 0.0007 | 32.0 | 96 | 0.0044 | 1.0 |
| 0.0008 | 33.0 | 99 | 0.0043 | 1.0 |
| 0.0005 | 34.0 | 102 | 0.0044 | 1.0 |
| 0.0006 | 35.0 | 105 | 0.0045 | 1.0 |
| 0.0006 | 36.0 | 108 | 0.0046 | 1.0 |
| 0.0007 | 37.0 | 111 | 0.0048 | 1.0 |
| 0.0006 | 38.0 | 114 | 0.0049 | 1.0 |
| 0.0005 | 39.0 | 117 | 0.0050 | 1.0 |
| 0.0005 | 40.0 | 120 | 0.0050 | 1.0 |
| 0.0004 | 41.0 | 123 | 0.0051 | 1.0 |
| 0.0005 | 42.0 | 126 | 0.0051 | 1.0 |
| 0.0004 | 43.0 | 129 | 0.0051 | 1.0 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-4", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-4
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-4
=====================================
This model is a fine-tuned version of microsoft/deberta-v3-large on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.3023
* Accuracy: 0.7057
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-5", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-5
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-5
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-6", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-6
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-6
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-7", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-7
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-7
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-8", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-8
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-8
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "deberta-v3-large__sst2__train-8-9", "results": []}]}
|
SetFit/deberta-v3-large__sst2__train-8-9
| null |
[
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us
|
deberta-v3-large\_\_sst2\_\_train-8-9
=====================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #deberta-v2 #text-classification #generated_from_trainer #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-0
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: 1.2707
- Accuracy: 0.517
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0943 | 1.0 | 10 | 1.1095 | 0.3 |
| 1.0602 | 2.0 | 20 | 1.1086 | 0.4 |
| 1.0159 | 3.0 | 30 | 1.1165 | 0.4 |
| 0.9027 | 4.0 | 40 | 1.1377 | 0.4 |
| 0.8364 | 5.0 | 50 | 1.0126 | 0.5 |
| 0.6653 | 6.0 | 60 | 0.9298 | 0.5 |
| 0.535 | 7.0 | 70 | 0.9555 | 0.5 |
| 0.3713 | 8.0 | 80 | 0.8543 | 0.4 |
| 0.1633 | 9.0 | 90 | 0.9876 | 0.4 |
| 0.1069 | 10.0 | 100 | 0.8383 | 0.6 |
| 0.0591 | 11.0 | 110 | 0.8056 | 0.6 |
| 0.0344 | 12.0 | 120 | 0.8915 | 0.6 |
| 0.0265 | 13.0 | 130 | 0.8722 | 0.6 |
| 0.0196 | 14.0 | 140 | 1.0064 | 0.6 |
| 0.0158 | 15.0 | 150 | 1.0479 | 0.6 |
| 0.0128 | 16.0 | 160 | 1.0723 | 0.6 |
| 0.0121 | 17.0 | 170 | 1.0758 | 0.6 |
| 0.0093 | 18.0 | 180 | 1.1236 | 0.6 |
| 0.0085 | 19.0 | 190 | 1.1480 | 0.6 |
| 0.0084 | 20.0 | 200 | 1.1651 | 0.6 |
| 0.0077 | 21.0 | 210 | 1.1832 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-0", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-0
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2707
* Accuracy: 0.517
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-1", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-1
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-1
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-2
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: 0.9210
- Accuracy: 0.5635
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0915 | 1.0 | 10 | 1.1051 | 0.4 |
| 1.0663 | 2.0 | 20 | 1.0794 | 0.3 |
| 1.0307 | 3.0 | 30 | 1.0664 | 0.5 |
| 0.9443 | 4.0 | 40 | 1.0729 | 0.5 |
| 0.8373 | 5.0 | 50 | 1.0175 | 0.4 |
| 0.6892 | 6.0 | 60 | 0.9624 | 0.5 |
| 0.538 | 7.0 | 70 | 0.9924 | 0.5 |
| 0.4173 | 8.0 | 80 | 1.0136 | 0.6 |
| 0.1846 | 9.0 | 90 | 1.0683 | 0.6 |
| 0.1125 | 10.0 | 100 | 1.2376 | 0.6 |
| 0.0754 | 11.0 | 110 | 1.2537 | 0.6 |
| 0.0401 | 12.0 | 120 | 1.4387 | 0.6 |
| 0.0285 | 13.0 | 130 | 1.5702 | 0.6 |
| 0.0241 | 14.0 | 140 | 1.6795 | 0.6 |
| 0.0175 | 15.0 | 150 | 1.7228 | 0.6 |
| 0.0147 | 16.0 | 160 | 1.7892 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-2", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-2
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9210
* Accuracy: 0.5635
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-3
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: 1.0675
- Accuracy: 0.44
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0951 | 1.0 | 10 | 1.1346 | 0.1 |
| 1.0424 | 2.0 | 20 | 1.1120 | 0.2 |
| 0.957 | 3.0 | 30 | 1.1002 | 0.3 |
| 0.7889 | 4.0 | 40 | 1.0838 | 0.4 |
| 0.6162 | 5.0 | 50 | 1.0935 | 0.5 |
| 0.4849 | 6.0 | 60 | 1.0867 | 0.5 |
| 0.3089 | 7.0 | 70 | 1.1145 | 0.5 |
| 0.2145 | 8.0 | 80 | 1.1278 | 0.6 |
| 0.0805 | 9.0 | 90 | 1.2801 | 0.6 |
| 0.0497 | 10.0 | 100 | 1.3296 | 0.6 |
| 0.0328 | 11.0 | 110 | 1.2913 | 0.6 |
| 0.0229 | 12.0 | 120 | 1.3692 | 0.6 |
| 0.0186 | 13.0 | 130 | 1.4642 | 0.6 |
| 0.0161 | 14.0 | 140 | 1.5568 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-3", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-3
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-3
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0675
* Accuracy: 0.44
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-16-4
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: 1.0903
- Accuracy: 0.4805
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.0974 | 1.0 | 10 | 1.1139 | 0.1 |
| 1.0637 | 2.0 | 20 | 1.0988 | 0.1 |
| 0.9758 | 3.0 | 30 | 1.1013 | 0.1 |
| 0.9012 | 4.0 | 40 | 1.0769 | 0.3 |
| 0.6993 | 5.0 | 50 | 1.0484 | 0.6 |
| 0.5676 | 6.0 | 60 | 1.0223 | 0.6 |
| 0.4069 | 7.0 | 70 | 0.9190 | 0.6 |
| 0.3192 | 8.0 | 80 | 1.1370 | 0.6 |
| 0.1112 | 9.0 | 90 | 1.1728 | 0.6 |
| 0.07 | 10.0 | 100 | 1.1998 | 0.6 |
| 0.0397 | 11.0 | 110 | 1.3700 | 0.6 |
| 0.027 | 12.0 | 120 | 1.3329 | 0.6 |
| 0.021 | 13.0 | 130 | 1.2697 | 0.6 |
| 0.0177 | 14.0 | 140 | 1.4195 | 0.6 |
| 0.0142 | 15.0 | 150 | 1.5342 | 0.6 |
| 0.0118 | 16.0 | 160 | 1.5999 | 0.6 |
| 0.0108 | 17.0 | 170 | 1.6327 | 0.6 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-4", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-4
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-4
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0903
* Accuracy: 0.4805
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-5", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-5
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-5
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-6", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-6
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-6
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-7", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-7
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-7
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-8", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-8
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-8
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-16-9", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-16-9
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-16-9
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-0", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-0
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-1", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-1
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-1
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-2", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-2
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-3", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-3
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-3
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-4", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-4
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-4
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-5", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-5
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-5
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-6", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-6
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-6
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-32-7
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: 0.8210
- Accuracy: 0.6305
## 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 | 19 | 1.0655 | 0.4 |
| 1.0102 | 2.0 | 38 | 0.9927 | 0.6 |
| 0.8063 | 3.0 | 57 | 0.9117 | 0.5 |
| 0.5284 | 4.0 | 76 | 0.8058 | 0.55 |
| 0.2447 | 5.0 | 95 | 0.8393 | 0.45 |
| 0.098 | 6.0 | 114 | 0.8438 | 0.6 |
| 0.0388 | 7.0 | 133 | 1.1901 | 0.45 |
| 0.0188 | 8.0 | 152 | 1.4429 | 0.45 |
| 0.0121 | 9.0 | 171 | 1.3648 | 0.4 |
| 0.0082 | 10.0 | 190 | 1.4768 | 0.4 |
| 0.0066 | 11.0 | 209 | 1.4830 | 0.45 |
| 0.0057 | 12.0 | 228 | 1.4936 | 0.45 |
| 0.0053 | 13.0 | 247 | 1.5649 | 0.4 |
| 0.0041 | 14.0 | 266 | 1.6306 | 0.4 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-7", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-7
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-7
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8210
* Accuracy: 0.6305
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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-32-8
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: 0.9191
- Accuracy: 0.632
## 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.1008 | 1.0 | 19 | 1.0877 | 0.4 |
| 1.0354 | 2.0 | 38 | 1.0593 | 0.35 |
| 0.8765 | 3.0 | 57 | 0.9722 | 0.5 |
| 0.6365 | 4.0 | 76 | 0.9271 | 0.55 |
| 0.3944 | 5.0 | 95 | 0.7852 | 0.5 |
| 0.2219 | 6.0 | 114 | 0.9360 | 0.55 |
| 0.126 | 7.0 | 133 | 1.0610 | 0.55 |
| 0.0389 | 8.0 | 152 | 1.0884 | 0.6 |
| 0.0191 | 9.0 | 171 | 1.3483 | 0.55 |
| 0.0108 | 10.0 | 190 | 1.4226 | 0.55 |
| 0.0082 | 11.0 | 209 | 1.4270 | 0.55 |
| 0.0065 | 12.0 | 228 | 1.5074 | 0.55 |
| 0.0059 | 13.0 | 247 | 1.5577 | 0.55 |
| 0.0044 | 14.0 | 266 | 1.5798 | 0.55 |
| 0.0042 | 15.0 | 285 | 1.6196 | 0.55 |
### Framework versions
- Transformers 4.15.0
- Pytorch 1.10.2+cu102
- Datasets 1.18.2
- Tokenizers 0.10.3
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-8", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-8
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-8
================================================================
This model is a fine-tuned version of distilbert-base-uncased on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.9191
* Accuracy: 0.632
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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-32-9", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-32-9
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-32-9
================================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-0", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-0
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-1", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-1
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-1
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-2", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-2
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-3", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-3
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-3
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-4", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-4
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-4
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-5", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-5
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-5
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-6", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-6
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-6
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-7", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-7
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-7
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-8", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-8
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-8
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__hate_speech_offensive__train-8-9", "results": []}]}
|
SetFit/distilbert-base-uncased__hate_speech_offensive__train-8-9
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_hate\_speech\_offensive\_\_train-8-9
===============================================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased__sst2__all-train", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__all-train
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_all-train
============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.1+cu102
* Datasets 1.17.0
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #base_model-distilbert-base-uncased #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.1+cu102\n* Datasets 1.17.0\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-0", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-0
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-1", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-1
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-1
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-2", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-2
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-3", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-3
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-3
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-4", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-4
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-4
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-5", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-5
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-5
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-6", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-6
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-6
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-7", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-7
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-7
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-8", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-8
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:04+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-8
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-16-9", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-16-9
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-16-9
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-0", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-0
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-1", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-1
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-1
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-2", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-2
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-2
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-3", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-3
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-3
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-4", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-4
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-4
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-5", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-5
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-5
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-6", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-6
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-6
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-7", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-7
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-7
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-8", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-8
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-8
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-32-9", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-32-9
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-32-9
=============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
text-classification
|
transformers
|
<!-- 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
|
{"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "distilbert-base-uncased__sst2__train-8-0", "results": []}]}
|
SetFit/distilbert-base-uncased__sst2__train-8-0
| null |
[
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null |
2022-03-02T23:29:05+00:00
|
[] |
[] |
TAGS
#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
distilbert-base-uncased\_\_sst2\_\_train-8-0
============================================
This model is a fine-tuned version of 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
### Framework versions
* Transformers 4.15.0
* Pytorch 1.10.2+cu102
* Datasets 1.18.2
* Tokenizers 0.10.3
|
[
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
[
"TAGS\n#transformers #pytorch #distilbert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 50\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.15.0\n* Pytorch 1.10.2+cu102\n* Datasets 1.18.2\n* Tokenizers 0.10.3"
] |
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