id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Foxasdf/whisper-base-en | Foxasdf | 2023-11-29T18:38:41Z | 0 | 1 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"en-asr-leaderboard",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_3_0",
"base_model:openai/whisper-base",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:38:41Z | 2023-11-28T02:35:11.000Z | null | null | ---
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- en-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_3_0
metrics:
- wer
model-index:
- name: Whisper base en - spongebob
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 3.0
type: mozilla-foundation/common_voice_3_0
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 18.36301062397127
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper base en - spongebob
This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Voice 3.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3451
- Wer: 18.3630
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.2586 | 0.84 | 500 | 0.3588 | 19.4733 |
| 0.1667 | 1.68 | 1000 | 0.3451 | 17.4892 |
| 0.1069 | 2.53 | 1500 | 0.3451 | 18.3630 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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NurtureAI/Starling-LM-11B-alpha-v1-GGUF | NurtureAI | 2023-11-30T01:17:52Z | 0 | 2 | null | [
"transformers",
"gguf",
"reward model",
"RLHF",
"RLAIF",
"en",
"dataset:berkeley-nest/Nectar",
"arxiv:2306.02231",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:17:52Z | 2023-11-28T04:04:59.000Z | null | null | ---
license: cc-by-nc-4.0
datasets:
- berkeley-nest/Nectar
language:
- en
library_name: transformers
tags:
- reward model
- RLHF
- RLAIF
---
# Starling-RM-11B-alpha (GGUF)
IF USING LMSTUDIO RENAME THE MODEL AND MODEL DIRECTORY TO NOT HAVE "STAR" IN THE NAME OR LMSTUDIO THINKS IT IS A STARCODER MODEL AND GPU WILL BE DISABLED!
Special thanks to user Undi95 for their mistral passthrough explanation.
Special thanks to berkley too of course for the great model.
Special thanks to everyone contributing to open source!
Together we are strong!
mergekit configuration used:
```
slices:
- sources:
- model: berkeley-nest/Starling-LM-7B-alpha
layer_range: [0, 24]
- sources:
- model: berkeley-nest/Starling-LM-7B-alpha
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
```
Upon doing more text generation we have noticed that this is the best prompt when directing this model with a system prompt.
Replace {system} with your system prompt, and {instruction} with your instruction. Using GPT4 System is NOT optimal.
```
{system}<|end_of_turn|>\nGPT4 User: {instruction}<|end_of_turn|>GPT4 Assistant:
```
# Original Model Card
# Starling-RM-7B-alpha
<!-- Provide a quick summary of what the model is/does. -->
- **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
- **Model type:** Language Model finetuned with RLHF / RLAIF
- **License:** Non commercial license
- **Finetuned from model:** [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1))
We introduce Starling-7B, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). The model harnesses the power of our new GPT-4 labeled ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), and our new reward training and policy tuning pipeline. Starling-7B-alpha scores 8.09 in MT Bench with GPT-4 as a judge, outperforming every model to date on MT-Bench except for OpenAI's GPT-4 and GPT-4 Turbo. We release the ranking dataset [Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the reward model [Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) and the language model [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) on HuggingFace, and an online demo in LMSYS [Chatbot Arena](https://chat.lmsys.org). Stay tuned for our forthcoming code and paper, which will provide more details on the whole process.
Starling-LM-7B-alpha is a language model trained from [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5) with reward model [berkeley-nest/Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) and policy optimization method [advantage-induced policy alignment (APA)](https://arxiv.org/abs/2306.02231). The evaluation results are listed below.
| Model | Tuning Method | MT Bench | AlpacaEval | MMLU |
|-----------------------|------------------|----------|------------|------|
| GPT-4-Turbo | ? | 9.32 | 97.70 | |
| GPT-4 | SFT + PPO | 8.99 | 95.28 | 86.4 |
| **Starling-7B** | C-RLFT + APA | 8.09 | 91.99 | 63.9 |
| Claude-2 | ? | 8.06 | 91.36 | 78.5 |
| GPT-3.5-Turbo | ? | 7.94 | 89.37 | 70 |
| Claude-1 | ? | 7.9 | 88.39 | 77 |
| Tulu-2-dpo-70b | SFT + DPO | 7.89 | 95.1 | |
| Openchat-3.5 | C-RLFT | 7.81 | 88.51 | 64.3 |
| Zephyr-7B-beta | SFT + DPO | 7.34 | 90.60 | 61.4 |
| Llama-2-70b-chat-hf | SFT + PPO | 6.86 | 92.66 | 63 |
| Neural-chat-7b-v3-1 | SFT + DPO | 6.84 | 84.53 | 62.4 |
| Tulu-2-dpo-7b | SFT + DPO | 6.29 | 85.1 | |
For more detailed discussions, please check out our [blog post](https://starling.cs.berkeley.edu), and stay tuned for our upcoming code and paper!
<!-- Provide the basic links for the model. -->
- **Blog:** https://starling.cs.berkeley.edu/
- **Paper:** Coming soon!
- **Code:** Coming soon!
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
Our model follows the exact chat template and usage as [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5). Please refer to their model card for more details.
In addition, our model is hosted on LMSYS [Chatbot Arena](https://chat.lmsys.org) for free test.
## License
The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## Acknowledgment
We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.
## Citation
```
@misc{starling2023,
title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
url = {},
author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao},
month = {November},
year = {2023}
}
```
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bentleyjeon/distilbert-base-uncased-finetuned-emotion | bentleyjeon | 2023-11-29T08:13:32Z | 0 | 0 | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:13:32Z | 2023-11-28T04:07:26.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | bentleyjeon/distilbert-base-uncased-finetuned-emotion | [
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lu-vae/zephyr-7b-dpo-lora | lu-vae | 2023-11-29T10:38:16Z | 0 | 0 | null | [
"transformers",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:38:16Z | 2023-11-28T05:17:35.000Z | null | null | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-dpo-lora
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-dpo-lora
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5254
- Rewards/chosen: -0.1284
- Rewards/rejected: -0.9165
- Rewards/accuracies: 0.7400
- Rewards/margins: 0.7881
- Logps/rejected: -229.5277
- Logps/chosen: -266.8034
- Logits/rejected: -2.8774
- Logits/chosen: -2.9668
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.545 | 1.0 | 968 | 0.5543 | -0.0951 | -0.6704 | 0.7270 | 0.5752 | -227.0664 | -266.4709 | -2.8955 | -2.9826 |
| 0.5144 | 2.0 | 1936 | 0.5310 | -0.1339 | -0.8883 | 0.7360 | 0.7544 | -229.2458 | -266.8581 | -2.8813 | -2.9701 |
| 0.518 | 3.0 | 2904 | 0.5254 | -0.1284 | -0.9165 | 0.7400 | 0.7881 | -229.5277 | -266.8034 | -2.8774 | -2.9668 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | lu-vae/zephyr-7b-dpo-lora | [
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cc4900/Llama-2-7b-chat-finetune | cc4900 | 2023-11-29T22:16:35Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:16:35Z | 2023-11-28T05:27:57.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | cc4900/Llama-2-7b-chat-finetune | [
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vishnu027/donut_marriage_ms_560-140_002 | vishnu027 | 2023-11-30T00:42:33Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:42:33Z | 2023-11-28T06:41:02.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | vishnu027/donut_marriage_ms_560-140_002 | [
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Anshulmango/LLaMa2_13B_Chat-finetuned-Dolly | Anshulmango | 2023-11-29T05:23:00Z | 0 | 0 | null | [
"generated_from_trainer",
"region:us"
] | 2023-11-29T05:23:00Z | 2023-11-28T07:02:10.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: LLaMa2_13B_Chat-finetuned-Dolly
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# LLaMa2_13B_Chat-finetuned-Dolly
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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DichenLi/llama2_lora_r32_lora_alpha8_lora_dropout0.15 | DichenLi | 2023-11-29T02:07:19Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T02:07:19Z | 2023-11-28T07:40:14.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DichenLi/llama2_lora_r32_lora_alpha8_lora_dropout0.15 | [
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dethfest/distilbert-base-uncased-zeron | dethfest | 2023-11-29T07:28:14Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:28:14Z | 2023-11-28T07:53:19.000Z | null | null | Entry not found | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | dethfest/distilbert-base-uncased-zeron | [
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Almahmood/whisper-medium-checkpoint | Almahmood | 2023-11-30T01:15:23Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:15:23Z | 2023-11-28T07:53:48.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Almahmood/whisper-medium-checkpoint | [
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Noveled/test | Noveled | 2023-11-29T06:18:41Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:beomi/polyglot-ko-12.8b-safetensors",
"region:us"
] | 2023-11-29T06:18:41Z | 2023-11-28T07:56:33.000Z | null | null | ---
library_name: peft
base_model: beomi/polyglot-ko-12.8b-safetensors
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Evaluation
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
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KnutJaegersberg/Galpaca-30b-MiniOrca | KnutJaegersberg | 2023-11-29T22:19:01Z | 0 | 0 | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"galactica",
"dataset:TinyPixel/orca-mini",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:19:01Z | 2023-11-28T08:05:57.000Z | null | null | ---
license: cc-by-nc-4.0
tags:
- galactica
pipeline_tag: text-generation
datasets:
- TinyPixel/orca-mini
---
Galpaca trained for 2.7 epochs on the 50k shortest records of miniorca dataset with NEFTune.
Prompt Example:
```
### System:
You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
### User:
What is AGI?
### Assistant:
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | KnutJaegersberg/Galpaca-30b-MiniOrca | [
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DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.1 | DichenLi | 2023-11-29T01:41:32Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T01:41:32Z | 2023-11-28T08:06:07.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.1 | [
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tyzhu/clm_data | tyzhu | 2023-11-29T12:41:38Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T12:41:38Z | 2023-11-28T08:18:18.000Z | null | null | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: clm_data
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# clm_data
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0599
- Accuracy: 0.8787
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 100.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 5.8666 | 1.0 | 10 | 5.8677 | 0.0915 |
| 5.8547 | 2.0 | 20 | 5.8474 | 0.0915 |
| 5.8283 | 3.0 | 30 | 5.8101 | 0.0957 |
| 5.7813 | 4.0 | 40 | 5.7489 | 0.1 |
| 5.7091 | 5.0 | 50 | 5.6529 | 0.1 |
| 5.5909 | 6.0 | 60 | 5.5196 | 0.1021 |
| 5.4485 | 7.0 | 70 | 5.3634 | 0.1213 |
| 5.2857 | 8.0 | 80 | 5.1770 | 0.1255 |
| 5.0707 | 9.0 | 90 | 4.9415 | 0.1340 |
| 4.8288 | 10.0 | 100 | 4.6974 | 0.1426 |
| 4.5652 | 11.0 | 110 | 4.4202 | 0.1745 |
| 4.3271 | 12.0 | 120 | 4.1808 | 0.2064 |
| 4.1027 | 13.0 | 130 | 3.9769 | 0.2106 |
| 3.9293 | 14.0 | 140 | 3.8599 | 0.2170 |
| 3.7955 | 15.0 | 150 | 3.7126 | 0.2319 |
| 3.6812 | 16.0 | 160 | 3.5900 | 0.2426 |
| 3.5678 | 17.0 | 170 | 3.4842 | 0.2681 |
| 3.4685 | 18.0 | 180 | 3.3662 | 0.2574 |
| 3.3764 | 19.0 | 190 | 3.3006 | 0.2766 |
| 3.3056 | 20.0 | 200 | 3.2061 | 0.2787 |
| 3.1958 | 21.0 | 210 | 3.1063 | 0.2957 |
| 3.07 | 22.0 | 220 | 2.9807 | 0.3191 |
| 2.953 | 23.0 | 230 | 2.8520 | 0.3532 |
| 2.8352 | 24.0 | 240 | 2.7290 | 0.3468 |
| 2.6954 | 25.0 | 250 | 2.5918 | 0.3894 |
| 2.5495 | 26.0 | 260 | 2.4329 | 0.4170 |
| 2.4005 | 27.0 | 270 | 2.2778 | 0.4574 |
| 2.2324 | 28.0 | 280 | 2.0997 | 0.4809 |
| 2.0615 | 29.0 | 290 | 1.9242 | 0.5128 |
| 1.8948 | 30.0 | 300 | 1.7541 | 0.5553 |
| 1.7209 | 31.0 | 310 | 1.6312 | 0.5894 |
| 1.568 | 32.0 | 320 | 1.4449 | 0.6170 |
| 1.4112 | 33.0 | 330 | 1.2837 | 0.6787 |
| 1.2586 | 34.0 | 340 | 1.1530 | 0.7 |
| 1.1189 | 35.0 | 350 | 1.0183 | 0.7255 |
| 0.9879 | 36.0 | 360 | 0.9012 | 0.7617 |
| 0.8636 | 37.0 | 370 | 0.7902 | 0.7830 |
| 0.768 | 38.0 | 380 | 0.7040 | 0.7915 |
| 0.686 | 39.0 | 390 | 0.6284 | 0.8085 |
| 0.6215 | 40.0 | 400 | 0.5771 | 0.8128 |
| 0.5718 | 41.0 | 410 | 0.5308 | 0.8170 |
| 0.5198 | 42.0 | 420 | 0.4875 | 0.8255 |
| 0.4733 | 43.0 | 430 | 0.4400 | 0.8319 |
| 0.4329 | 44.0 | 440 | 0.3925 | 0.8340 |
| 0.379 | 45.0 | 450 | 0.3410 | 0.8447 |
| 0.327 | 46.0 | 460 | 0.2925 | 0.8489 |
| 0.2797 | 47.0 | 470 | 0.2401 | 0.8532 |
| 0.2269 | 48.0 | 480 | 0.1968 | 0.8596 |
| 0.1876 | 49.0 | 490 | 0.1633 | 0.8617 |
| 0.1538 | 50.0 | 500 | 0.1344 | 0.8681 |
| 0.134 | 51.0 | 510 | 0.1148 | 0.8723 |
| 0.1123 | 52.0 | 520 | 0.1028 | 0.8723 |
| 0.1025 | 53.0 | 530 | 0.0912 | 0.8745 |
| 0.09 | 54.0 | 540 | 0.0829 | 0.8745 |
| 0.0823 | 55.0 | 550 | 0.0781 | 0.8745 |
| 0.0778 | 56.0 | 560 | 0.0748 | 0.8766 |
| 0.0761 | 57.0 | 570 | 0.0694 | 0.8787 |
| 0.0712 | 58.0 | 580 | 0.0663 | 0.8787 |
| 0.0683 | 59.0 | 590 | 0.0656 | 0.8787 |
| 0.0674 | 60.0 | 600 | 0.0636 | 0.8787 |
| 0.0662 | 61.0 | 610 | 0.0636 | 0.8787 |
| 0.066 | 62.0 | 620 | 0.0633 | 0.8787 |
| 0.0652 | 63.0 | 630 | 0.0621 | 0.8787 |
| 0.0641 | 64.0 | 640 | 0.0615 | 0.8787 |
| 0.0643 | 65.0 | 650 | 0.0617 | 0.8787 |
| 0.0646 | 66.0 | 660 | 0.0623 | 0.8787 |
| 0.0644 | 67.0 | 670 | 0.0610 | 0.8787 |
| 0.0628 | 68.0 | 680 | 0.0606 | 0.8787 |
| 0.0622 | 69.0 | 690 | 0.0609 | 0.8787 |
| 0.0629 | 70.0 | 700 | 0.0610 | 0.8787 |
| 0.0623 | 71.0 | 710 | 0.0609 | 0.8787 |
| 0.0631 | 72.0 | 720 | 0.0615 | 0.8787 |
| 0.0639 | 73.0 | 730 | 0.0614 | 0.8787 |
| 0.0624 | 74.0 | 740 | 0.0608 | 0.8787 |
| 0.0623 | 75.0 | 750 | 0.0607 | 0.8787 |
| 0.062 | 76.0 | 760 | 0.0605 | 0.8787 |
| 0.0618 | 77.0 | 770 | 0.0600 | 0.8787 |
| 0.0613 | 78.0 | 780 | 0.0599 | 0.8787 |
| 0.0613 | 79.0 | 790 | 0.0596 | 0.8787 |
| 0.061 | 80.0 | 800 | 0.0600 | 0.8787 |
| 0.0614 | 81.0 | 810 | 0.0599 | 0.8787 |
| 0.0615 | 82.0 | 820 | 0.0603 | 0.8787 |
| 0.0614 | 83.0 | 830 | 0.0600 | 0.8787 |
| 0.061 | 84.0 | 840 | 0.0600 | 0.8787 |
| 0.0612 | 85.0 | 850 | 0.0602 | 0.8787 |
| 0.0612 | 86.0 | 860 | 0.0603 | 0.8787 |
| 0.0616 | 87.0 | 870 | 0.0604 | 0.8787 |
| 0.0614 | 88.0 | 880 | 0.0601 | 0.8787 |
| 0.0609 | 89.0 | 890 | 0.0600 | 0.8787 |
| 0.0607 | 90.0 | 900 | 0.0599 | 0.8787 |
| 0.0606 | 91.0 | 910 | 0.0599 | 0.8787 |
| 0.0606 | 92.0 | 920 | 0.0599 | 0.8787 |
| 0.0602 | 93.0 | 930 | 0.0598 | 0.8787 |
| 0.0602 | 94.0 | 940 | 0.0598 | 0.8787 |
| 0.0604 | 95.0 | 950 | 0.0598 | 0.8787 |
| 0.0604 | 96.0 | 960 | 0.0599 | 0.8787 |
| 0.0601 | 97.0 | 970 | 0.0599 | 0.8787 |
| 0.0602 | 98.0 | 980 | 0.0598 | 0.8787 |
| 0.0604 | 99.0 | 990 | 0.0599 | 0.8787 |
| 0.0601 | 100.0 | 1000 | 0.0599 | 0.8787 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0+cu121
- Datasets 2.14.5
- Tokenizers 0.14.1
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phuong-tk-nguyen/resnet-50-finetuned | phuong-tk-nguyen | 2023-11-29T03:24:53Z | 0 | 0 | null | [
"transformers",
"pytorch",
"safetensors",
"resnet",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/resnet-50",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:24:53Z | 2023-11-28T09:12:06.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/resnet-50
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: resnet-50-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.199
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# resnet-50-finetuned
This model is a fine-tuned version of [microsoft/resnet-50](https://huggingface.co/microsoft/resnet-50) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2724
- Accuracy: 0.199
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.3021 | 0.14 | 10 | 2.2994 | 0.112 |
| 2.2929 | 0.28 | 20 | 2.2911 | 0.137 |
| 2.2875 | 0.43 | 30 | 2.2848 | 0.151 |
| 2.2824 | 0.57 | 40 | 2.2812 | 0.175 |
| 2.2792 | 0.71 | 50 | 2.2758 | 0.191 |
| 2.2766 | 0.85 | 60 | 2.2726 | 0.197 |
| 2.2765 | 0.99 | 70 | 2.2724 | 0.199 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.6
- Tokenizers 0.13.3
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phuong-tk-nguyen/vit-base-patch16-224-finetuned | phuong-tk-nguyen | 2023-11-29T04:01:41Z | 0 | 0 | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:01:41Z | 2023-11-28T09:15:03.000Z | null | null | ---
license: apache-2.0
base_model: google/vit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: vit-base-patch16-224-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.967
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit-base-patch16-224-finetuned
This model is a fine-tuned version of [google/vit-base-patch16-224](https://huggingface.co/google/vit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2073
- Accuracy: 0.967
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.1833 | 0.14 | 10 | 1.6004 | 0.626 |
| 1.3976 | 0.28 | 20 | 0.8484 | 0.909 |
| 0.9003 | 0.43 | 30 | 0.4514 | 0.946 |
| 0.6423 | 0.57 | 40 | 0.3037 | 0.96 |
| 0.5084 | 0.71 | 50 | 0.2468 | 0.96 |
| 0.47 | 0.85 | 60 | 0.2161 | 0.965 |
| 0.4753 | 0.99 | 70 | 0.2073 | 0.967 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1
- Datasets 2.14.6
- Tokenizers 0.14.1
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phuong-tk-nguyen/swin-tiny-patch4-window7-224-finetuned | phuong-tk-nguyen | 2023-11-29T03:42:21Z | 0 | 0 | null | [
"transformers",
"pytorch",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:42:21Z | 2023-11-28T09:21:55.000Z | null | null | ---
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.845
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5803
- Accuracy: 0.845
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.2602 | 0.14 | 10 | 1.9846 | 0.365 |
| 1.8138 | 0.28 | 20 | 1.4061 | 0.614 |
| 1.3707 | 0.43 | 30 | 0.9769 | 0.702 |
| 1.1183 | 0.57 | 40 | 0.7637 | 0.78 |
| 0.9688 | 0.71 | 50 | 0.6487 | 0.812 |
| 0.9258 | 0.85 | 60 | 0.6009 | 0.838 |
| 0.8917 | 0.99 | 70 | 0.5803 | 0.845 |
### Framework versions
- Transformers 4.31.0
- Pytorch 1.10.1+cu111
- Datasets 2.14.6
- Tokenizers 0.13.3
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | phuong-tk-nguyen/swin-tiny-patch4-window7-224-finetuned | [
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DichenLi/llama2_lora_r32_lora_alpha8_lora_dropout0.1 | DichenLi | 2023-11-29T03:24:41Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T03:24:41Z | 2023-11-28T09:23:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DichenLi/llama2_lora_r32_lora_alpha8_lora_dropout0.1 | [
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saumyasinha0510/T5-Kaggle_resource_pipeline | saumyasinha0510 | 2023-11-29T06:10:39Z | 0 | 0 | null | [
"transformers",
"tf",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:10:39Z | 2023-11-28T09:42:48.000Z | null | null | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_keras_callback
model-index:
- name: saumyasinha0510/T5-Kaggle_resource_pipeline
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# saumyasinha0510/T5-Kaggle_resource_pipeline
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.0704
- Validation Loss: 1.8716
- Train Lr: 2e-05
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Lr | Epoch |
|:----------:|:---------------:|:--------:|:-----:|
| 2.2602 | 1.9319 | 2e-05 | 0 |
| 2.1136 | 1.8929 | 2e-05 | 1 |
| 2.0704 | 1.8716 | 2e-05 | 2 |
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | saumyasinha0510/T5-Kaggle_resource_pipeline | [
-0.4295545518398285,
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handsomeboyMMk/bbgg2 | handsomeboyMMk | 2023-11-29T07:46:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T07:46:28Z | 2023-11-28T09:50:25.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | handsomeboyMMk/bbgg2 | [
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jkhsong/distilbert-base-uncased-finetuned-bioasq | jkhsong | 2023-11-29T12:11:35Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:11:35Z | 2023-11-28T10:25:58.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-base-uncased-finetuned-bioasq
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-bioasq
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: 4.0877
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 112 | 4.1108 |
| No log | 2.0 | 224 | 4.0518 |
| No log | 3.0 | 336 | 4.0877 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
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neolord/xlm-roberta-base-finetuned-panx-de | neolord | 2023-11-29T10:33:51Z | 0 | 0 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"dataset:xtreme",
"base_model:xlm-roberta-base",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:33:51Z | 2023-11-28T10:50:47.000Z | null | null | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
config: PAN-X.de
split: validation
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8619356306152621
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1379
- F1: 0.8619
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2682 | 1.0 | 525 | 0.1562 | 0.8146 |
| 0.1348 | 2.0 | 1050 | 0.1472 | 0.8460 |
| 0.0851 | 3.0 | 1575 | 0.1379 | 0.8619 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.0.dev0
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Ba2han/HermesStar-OrcaWind-Synth-11B | Ba2han | 2023-11-29T18:54:15Z | 0 | 0 | null | [
"transformers",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:54:15Z | 2023-11-28T11:41:10.000Z | null | null | ---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
---
Open Hermes + Starling passthrough merged
SlimOrca(?)+Zephyr Beta linear merged, then passthrough merged with Synthia
Then both models were merged again in 1 to 0.3 ratio.
# My findings:
Increasing repetition penalty usually makes the model smarter up to a degree but it also causes stability issues.
Since most of the merged models were trained with ChatML, use ChatML template. Rarely the model throws another EOS token though.
- My favorite preset has been uploaded.
- You can use some sort of CoT prompt instead of "system" in ChatML. It does improve the quality of most output.
(You are an assistant. Break down the question and come to a conclusion.)
I don't know what I am doing, you are very welcome to put the model through benchmarks.
I'll also upload q6 GGUF but my internet is shit, so don't hesitate to share other quantizations. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Ba2han/HermesStar-OrcaWind-Synth-11B | [
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kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-removal-3e-4 | kanishka | 2023-11-29T09:45:49Z | 0 | 0 | null | [
"transformers",
"safetensors",
"opt",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T09:45:49Z | 2023-11-28T11:59:10.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | kanishka/smolm-autoreg-bpe-counterfactual-babylm-indef-removal-3e-4 | [
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lakxs/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 | lakxs | 2023-11-29T10:29:52Z | 0 | 0 | null | [
"transformers",
"safetensors",
"git",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:29:52Z | 2023-11-28T12:40:32.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | lakxs/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 | [
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Givi08/my_awesome_model | Givi08 | 2023-11-29T03:34:45Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:34:45Z | 2023-11-28T12:48:58.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Givi08/my_awesome_model | [
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aosaf/bert-bias | aosaf | 2023-11-29T05:09:18Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:09:18Z | 2023-11-28T13:16:05.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | aosaf/bert-bias | [
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Realgon/distilbert_imdb_padding0model | Realgon | 2023-11-29T06:01:00Z | 0 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:01:00Z | 2023-11-28T14:00:47.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
model-index:
- name: distilbert_imdb_padding0model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_imdb_padding0model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the imdb dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.01 | 16 | 0.6895 | 0.531 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_imdb_padding0model | [
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badmonk/models | badmonk | 2023-11-29T04:23:18Z | 0 | 1 | null | [
"region:us"
] | 2023-11-29T04:23:18Z | 2023-11-28T14:03:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | badmonk/models | [
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mtc/leo-mistral-absinth-finetuned-3-epochs-GGUF | mtc | 2023-11-29T15:08:31Z | 0 | 0 | null | [
"gguf",
"region:us"
] | 2023-11-29T15:08:31Z | 2023-11-28T14:09:45.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mtc/leo-mistral-absinth-finetuned-3-epochs-GGUF | [
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erbacher/burgers-revin-vq | erbacher | 2023-11-30T00:59:12Z | 0 | 0 | null | [
"transformers",
"safetensors",
"pdetokenizer",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:59:12Z | 2023-11-28T14:29:16.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | erbacher/burgers-revin-vq | [
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DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.15 | DichenLi | 2023-11-29T02:33:08Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T02:33:08Z | 2023-11-28T14:33:33.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.15 | [
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abeeraljabri/t5-base-medium-title-generation | abeeraljabri | 2023-11-29T22:10:50Z | 0 | 0 | null | [
"transformers",
"tf",
"safetensors",
"t5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T22:10:50Z | 2023-11-28T14:49:50.000Z | null | null | ---
tags:
- generated_from_keras_callback
model-index:
- name: t5-base-medium-title-generation
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# t5-base-medium-title-generation
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.35.2
- TensorFlow 2.14.0
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | abeeraljabri/t5-base-medium-title-generation | [
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YanweiLi/llama-vid-7b-full-224 | YanweiLi | 2023-11-29T06:04:35Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llava",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:04:35Z | 2023-11-28T14:51:36.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | YanweiLi/llama-vid-7b-full-224 | [
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NamProlah/upload-testing | NamProlah | 2023-11-29T14:24:29Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:24:29Z | 2023-11-28T14:52:35.000Z | null | null | # BloomSage Machine Learning & MLOps Backend Component
---
## Project Structure
```
.
├── font/
├── notebooks/
│ ├── images/
│ ├── Step1.EDA.ipynb
│ ├── Step2.DataPrep.ipynb
│ └── Step3.Classifier-BaselineModel.ipynb
├── scraping/
│ └── scrape.py
├── classify.py
├── recommend.py
├── requirements.txt
├── .gitignore
├── project-statement.md
├── README.md
└── LICENSE
```
1. `font/`: This folder contains the fonts used in our client script's GUI mode.
2. `notebooks/`: This folder contains all Jupyter Notebooks for this project and their exported plots in `notebooks/images/`.
3. `scrape/`: This folder contains a scraping script to get more images from the internet for our dataset. All downloaded images will also be in this folder.
4. `classify.py`: Client script for classifying flower images using trained models.
5. `recommend.py`: Client script for recommending flower images using trained models.
6. `requirements.txt`: Text file for `pip` installation of necessary packages for development environment.
7. `.gitignore`: This file contains ignore VCS ignore rules.
8. `README.md`: A text file containing useful reference information about this project, including how to run the algorithm.
9. `LICENSE`: MIT
Additionally, these folders will be created during dataset fetching and model training:
1. `data/`: This folder contains out datasets.
2. `log/`: This folder contains training logs exported from training our models.
3. `models/`: This folder contains trained models exported after training.
---
## Getting Started 🚀
Clone this repository:
```bash
git clone https://github.com/rmit-denominator/bloomsage-ml.git
```
### Development Environment
To set up the necessary packages for this project, run:
```bash
pip install -r requirements.txt
```
Refer to [requirements.txt](requirements.txt) for package dependencies and their versions.
<span style="color:gold">**NOTE:**</span> It is recommended that you use a Python virtual environment to avoid conflict with your global packages, and to keep your global Python installation clean. This is because we require specific versions of Numpy, Tensorflow and Keras in our code to maintain backward compatibility and compatibility between trained models and client code.
### Download Dataset
The dataset for this project is available on [Kaggle](https://kaggle.com/datasets/979207e9d5e6d91d26e8eb340941ae176c82fbdb2a25b4a436c273895ab96bb1). Follow these steps to download and set it up for training and testing:
1. Navigate to project's root directory.
2. Clean all existing files in the `data/` folders (if exists) before downloading or updating this dataset:
```bash
rm -r ./data/*
```
3. Download and **extract contents of** the `.zip` from [Kaggle](https://kaggle.com/datasets/979207e9d5e6d91d26e8eb340941ae176c82fbdb2a25b4a436c273895ab96bb1) into `data/raw` folder.
Alternatively, use the [Kaggle CLI](https://github.com/Kaggle/kaggle-api):
```bash
kaggle datasets download -d miketvo/rmit-flowers -p ./data/raw/ --unzip
```
The resulting folder structure should look like this:
```
.
├── data/
│ └── raw/
│ ├── Baby/
│ ├── Calimerio/
│ ├── Chrysanthemum/
│ ...
│ └── Tana/
│
...
```
where each folder corresponds to a flower class, and contains images of only that class.
4. Setup for training and testing: Run [notebooks/Step2.DataPrep.ipynb](notebooks/Step2.DataPrep.ipynb) and [Step5.Recommender.ipynb](notebooks/Step5.Recommender.ipynb). They will clean, process, and split the raw dataset and the resulting train and test set into `data/train/` and `data/test/`, respectively. They will also generate a database for our image recommendation system in `data/recommender-database/`, along with `data/recommender-database.csv` that contains the feature vectors for all images in the recommender database, in addition to exporting two helper models `models/fe-cnn` and `models/clu-kmeans.model` for the recommendation system. **Note:** Clean these folders and files before you run these two notebook:
```bash
rmdir -r ./data/train
rmdir -r ./data/test
rmdir -r ./data/recommender-database
rm ./data/recommender-database.csv
```
**<span style="color:red">Important:</span>** Clean and rerun this step every time you modify the raw dataset to get the most updated train dataset, test dataset, and recommender database.
### Training
Skip this step if you just want to use on of the pre-trained model packages available from [Releases](https://github.com/miketvo/rmit2023a-cosc2753-assignment2/releases).
- Run each Jupyter Notebook in `notebooks/` in their prefixed order starting `Step1.`, `Step2.`, `Step3.`, and so on, <span style="color:red">**one file at a time**</span>.
- Skip [Step2.DataPrep.ipynb](notebooks/Step2.DataPrep.ipynb) if you have already run it after downloading the raw dataset in the step above.
- Skip [Step5.Recommender.ipynb](notebooks/Step5.Recommender.ipynb) if you have already run it after downloading the raw dataset in the step above.
- The resulting models are exported into `models/` folder. Their training logs are stored in `log/` folder.
**Note:** Beware: any existing model with conflicting name in `models/` will be replaced with newly trained models.
### Using Trained Models
If you are using one of our pre-trained model packages, download your desired version from [Releases](https://github.com/miketvo/rmit2023a-cosc2753-assignment2/releases) (.zip archives) and extract its contents into this project's root directory using your preferred zip program. Make sure to check and clean `models/` folder (if exists) to avoid naming conflict with existing trained model before the extraction.
These trained models can then be loaded into your code with:
```python
import tensorflow as tf
model = tf.keras.models.load_model('path/to/model')
```
Additionally, two Python files, `classify.py` and `recommend.py`, are provided as simple front-ends to our trained model. You can either run them as standalone script in the terminal or import them as Python module in your own Python script or Jupyter Notebook to programmatically classify multiple images and recommend similar images for each of them.
To use them as standalone script, see instruction below:
On your terminal, make sure that you have the environment activated for the client script to have access to all required packages:
- Python Virtualenv:
```bash
./venv/Scripts/activate
```
- Conda:
```bash
conda activate ./envs
```
#### Classifying Flower Images
Use the `classify.py` client script. Its syntax is as follows:
```text
usage: classify.py [-h] -f FILE [-c CLASSIFIER] [-g] [-v {0,1,2}]
options:
-h, --help show this help message and exit
-f FILE, --file FILE the image to be classified
-c CLASSIFIER, --classifier CLASSIFIER the machine learning model used for classification, defaults: models/clf-cnn
-g, --gui show classification result using GUI
-v {0,1,2}, --verbose-level {0,1,2} verbose level, default: 0
```
Example use:
```text
$ python ./classify.py -f path/to/your/your/image.png -c ./models/clf -v=1
Image image.png is classified as "Chrysanthemum" (model: "clf")
```
It also has a rudimentary GUI mode using Matplotlib, which will display the image with a caption of what flower type it is classified as:
```bash
python ./classify.py --gui -f path/to/your/your/image.png -m ./models/clf
```
**Note:** Alternatively, you can import its `classify.classify()` function into your own script or notebook to programmatically classify multiple images (see its docstring for instruction on how to use).
#### Recommending Flower Images
Use the `recommend.py` client script. Its syntax is as follows:
```text
usage: recommend.py [-h] -f FILE [-d DATABASE] [-c CLASSIFIER] [-e FEATURE_EXTRACTOR] [-k CLUSTERING_MODEL] [-n NUM]
options:
-h, --help show this help message and exit
-f FILE, --file FILE reference image
-d DATABASE, --database DATABASE the database containing the images to be recommended, default: data/recommender-database
-c CLASSIFIER, --classifier CLASSIFIER the machine learning model used for image classification, default: models/clf-cnn
-e FEATURE_EXTRACTOR, --feature-extractor FEATURE_EXTRACTOR the machine learning model used for image feature extraction, default: models/fe-cnn
-k CLUSTERING_MODEL, --clustering-model CLUSTERING_MODEL the machine learning model used for image clustering, default: models/clu-kmeans.model
-n NUM, --num NUM number of recommendations, default: 10
```
Example:
```bash
python ./recommend.py -f path/to/your/your/image.png
```
When executed, the code above will display 10 similar flower images (GUI mode) of the same type, taken from the recommender database in `data/recommender-database/`, based on your reference image, using the default classifier, feature extractor, and clustering model
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Kevalon/loras | Kevalon | 2023-11-29T17:18:10Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T17:18:10Z | 2023-11-28T15:54:26.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Kevalon/loras | [
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pphuc25/whisper-small-vi-flax-20ep-v3 | pphuc25 | 2023-11-29T23:35:20Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:35:20Z | 2023-11-28T16:11:08.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | pphuc25/whisper-small-vi-flax-20ep-v3 | [
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DDingcheol/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 | DDingcheol | 2023-11-29T13:41:08Z | 0 | 0 | null | [
"transformers",
"safetensors",
"git",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:41:08Z | 2023-11-28T16:19:48.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | DDingcheol/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 | [
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mcfatbeard/flant5-large-aio | mcfatbeard | 2023-11-29T08:28:49Z | 0 | 0 | null | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"region:us"
] | 2023-11-29T08:28:49Z | 2023-11-28T16:44:35.000Z | null | null | ---
library_name: peft
base_model: google/flan-t5-large
---
# Model Card for Model ID
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## Training procedure
### Framework versions
- PEFT 0.6.2
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pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2 | pavitemple | 2023-11-29T17:16:21Z | 0 | 0 | null | [
"transformers",
"safetensors",
"videomae",
"video-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:16:21Z | 2023-11-28T16:46:31.000Z | null | null | Entry not found | null | transformers | video-classification | null | null | null | null | null | null | null | null | null | pavitemple/finetuned-Accident-MultipleLabels-Video-subset-v2 | [
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poiretclement/arthur | poiretclement | 2023-11-29T10:38:16Z | 0 | 0 | null | [
"onnx",
"region:us"
] | 2023-11-29T10:38:16Z | 2023-11-28T17:10:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | poiretclement/arthur | [
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EJosnin/taxi-tuto | EJosnin | 2023-11-29T12:31:30Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T12:31:30Z | 2023-11-28T17:18:47.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-tuto
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="EJosnin/taxi-tuto", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | EJosnin/taxi-tuto | [
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Rambo-007/bert-base-uncased-finetuned-swag | Rambo-007 | 2023-11-29T18:03:23Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:03:23Z | 2023-11-28T17:50:49.000Z | null | null | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-base-uncased-finetuned-swag
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased-finetuned-swag
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3863
- Accuracy: 0.2077
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3975 | 1.0 | 6316 | 1.3863 | 0.2637 |
| 1.3898 | 2.0 | 12632 | 1.3863 | 0.1595 |
| 1.3898 | 3.0 | 18948 | 1.3863 | 0.2077 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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Weni/Test-ZeroShot-Multilanguage-3.0-Llama2-13B | Weni | 2023-11-29T13:17:50Z | 0 | 0 | null | [
"peft",
"pytorch",
"tensorboard",
"region:us"
] | 2023-11-29T13:17:50Z | 2023-11-28T18:18:17.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
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Realgon/distilbert_sst2_padding0model | Realgon | 2023-11-29T06:09:55Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:09:55Z | 2023-11-28T18:23:46.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding0model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding0model
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: 0.8469
- Accuracy: 0.9039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2735 | 0.8863 |
| 0.3353 | 2.0 | 866 | 0.2990 | 0.9012 |
| 0.1656 | 3.0 | 1299 | 0.4399 | 0.8929 |
| 0.0808 | 4.0 | 1732 | 0.5182 | 0.8995 |
| 0.0356 | 5.0 | 2165 | 0.7006 | 0.8874 |
| 0.0245 | 6.0 | 2598 | 0.6024 | 0.9023 |
| 0.011 | 7.0 | 3031 | 0.6860 | 0.8995 |
| 0.011 | 8.0 | 3464 | 0.7609 | 0.9012 |
| 0.0075 | 9.0 | 3897 | 0.7456 | 0.9033 |
| 0.0111 | 10.0 | 4330 | 0.9955 | 0.8841 |
| 0.0071 | 11.0 | 4763 | 0.7824 | 0.9006 |
| 0.0102 | 12.0 | 5196 | 0.8189 | 0.9017 |
| 0.0072 | 13.0 | 5629 | 0.8564 | 0.9012 |
| 0.008 | 14.0 | 6062 | 0.8270 | 0.9001 |
| 0.008 | 15.0 | 6495 | 0.8077 | 0.9094 |
| 0.0022 | 16.0 | 6928 | 0.8089 | 0.9066 |
| 0.0037 | 17.0 | 7361 | 0.7955 | 0.9066 |
| 0.0036 | 18.0 | 7794 | 0.8304 | 0.9039 |
| 0.0037 | 19.0 | 8227 | 0.8192 | 0.9039 |
| 0.0008 | 20.0 | 8660 | 0.8469 | 0.9039 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding0model | [
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Realgon/distilbert_sst2_padding10model | Realgon | 2023-11-29T06:16:58Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:16:58Z | 2023-11-28T18:24:14.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding10model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding10model
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: 0.8972
- Accuracy: 0.9017
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2551 | 0.9023 |
| 0.3447 | 2.0 | 866 | 0.3196 | 0.8979 |
| 0.1734 | 3.0 | 1299 | 0.3882 | 0.9001 |
| 0.0877 | 4.0 | 1732 | 0.4801 | 0.9050 |
| 0.0444 | 5.0 | 2165 | 0.6567 | 0.8918 |
| 0.0206 | 6.0 | 2598 | 0.6090 | 0.9023 |
| 0.0145 | 7.0 | 3031 | 0.6415 | 0.9028 |
| 0.0145 | 8.0 | 3464 | 0.7532 | 0.9023 |
| 0.0083 | 9.0 | 3897 | 0.6840 | 0.9116 |
| 0.0073 | 10.0 | 4330 | 0.8115 | 0.9001 |
| 0.0131 | 11.0 | 4763 | 0.7755 | 0.9017 |
| 0.0083 | 12.0 | 5196 | 0.7370 | 0.9083 |
| 0.0045 | 13.0 | 5629 | 0.8288 | 0.9066 |
| 0.0072 | 14.0 | 6062 | 0.8585 | 0.9017 |
| 0.0072 | 15.0 | 6495 | 0.8054 | 0.9028 |
| 0.0064 | 16.0 | 6928 | 0.8080 | 0.9039 |
| 0.0059 | 17.0 | 7361 | 0.8245 | 0.9050 |
| 0.0019 | 18.0 | 7794 | 0.9924 | 0.8940 |
| 0.001 | 19.0 | 8227 | 0.9138 | 0.8984 |
| 0.0016 | 20.0 | 8660 | 0.8972 | 0.9017 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding10model | [
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Realgon/distilbert_sst2_padding20model | Realgon | 2023-11-29T06:24:29Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:24:29Z | 2023-11-28T18:24:38.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding20model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding20model
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: 0.8375
- Accuracy: 0.9110
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2668 | 0.8935 |
| 0.3473 | 2.0 | 866 | 0.3801 | 0.8814 |
| 0.1828 | 3.0 | 1299 | 0.3816 | 0.9061 |
| 0.0818 | 4.0 | 1732 | 0.5191 | 0.8973 |
| 0.0522 | 5.0 | 2165 | 0.6669 | 0.8946 |
| 0.0251 | 6.0 | 2598 | 0.6408 | 0.9072 |
| 0.0123 | 7.0 | 3031 | 0.6982 | 0.9006 |
| 0.0123 | 8.0 | 3464 | 0.7690 | 0.8962 |
| 0.011 | 9.0 | 3897 | 0.7984 | 0.9012 |
| 0.0057 | 10.0 | 4330 | 0.7417 | 0.9044 |
| 0.02 | 11.0 | 4763 | 0.7771 | 0.9088 |
| 0.0088 | 12.0 | 5196 | 0.7492 | 0.9033 |
| 0.0105 | 13.0 | 5629 | 0.8034 | 0.9033 |
| 0.0077 | 14.0 | 6062 | 0.7967 | 0.9033 |
| 0.0077 | 15.0 | 6495 | 0.8108 | 0.9088 |
| 0.0031 | 16.0 | 6928 | 0.8189 | 0.9088 |
| 0.0033 | 17.0 | 7361 | 0.8219 | 0.9116 |
| 0.0017 | 18.0 | 7794 | 0.8460 | 0.9077 |
| 0.0009 | 19.0 | 8227 | 0.8520 | 0.9099 |
| 0.0002 | 20.0 | 8660 | 0.8375 | 0.9110 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding20model | [
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Realgon/distilbert_sst2_padding30model | Realgon | 2023-11-29T06:32:23Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:32:23Z | 2023-11-28T18:25:05.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding30model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding30model
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: 0.9632
- Accuracy: 0.9006
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2757 | 0.8858 |
| 0.3485 | 2.0 | 866 | 0.3100 | 0.8984 |
| 0.1782 | 3.0 | 1299 | 0.4273 | 0.8935 |
| 0.0895 | 4.0 | 1732 | 0.4325 | 0.9066 |
| 0.0512 | 5.0 | 2165 | 0.6135 | 0.8918 |
| 0.0236 | 6.0 | 2598 | 0.6313 | 0.9006 |
| 0.02 | 7.0 | 3031 | 0.7291 | 0.8962 |
| 0.02 | 8.0 | 3464 | 0.6923 | 0.9017 |
| 0.0115 | 9.0 | 3897 | 0.8131 | 0.8968 |
| 0.0097 | 10.0 | 4330 | 0.8803 | 0.8957 |
| 0.0065 | 11.0 | 4763 | 0.7678 | 0.9066 |
| 0.0042 | 12.0 | 5196 | 0.7838 | 0.8995 |
| 0.0079 | 13.0 | 5629 | 0.9639 | 0.8918 |
| 0.0029 | 14.0 | 6062 | 0.8444 | 0.9050 |
| 0.0029 | 15.0 | 6495 | 0.8437 | 0.9039 |
| 0.0039 | 16.0 | 6928 | 0.8723 | 0.9072 |
| 0.0034 | 17.0 | 7361 | 0.9462 | 0.9012 |
| 0.0016 | 18.0 | 7794 | 0.8981 | 0.9039 |
| 0.0019 | 19.0 | 8227 | 0.9824 | 0.9006 |
| 0.0011 | 20.0 | 8660 | 0.9632 | 0.9006 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding30model | [
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Realgon/distilbert_sst2_padding40model | Realgon | 2023-11-29T06:40:46Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:40:46Z | 2023-11-28T18:25:31.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding40model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding40model
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: 0.8990
- Accuracy: 0.8984
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2807 | 0.8918 |
| 0.352 | 2.0 | 866 | 0.2839 | 0.8984 |
| 0.1741 | 3.0 | 1299 | 0.4073 | 0.8979 |
| 0.0814 | 4.0 | 1732 | 0.5104 | 0.9039 |
| 0.0415 | 5.0 | 2165 | 0.8648 | 0.8693 |
| 0.0268 | 6.0 | 2598 | 0.6747 | 0.8957 |
| 0.0204 | 7.0 | 3031 | 0.7511 | 0.8940 |
| 0.0204 | 8.0 | 3464 | 0.6485 | 0.9017 |
| 0.0195 | 9.0 | 3897 | 0.8488 | 0.8929 |
| 0.0042 | 10.0 | 4330 | 0.8213 | 0.9001 |
| 0.0097 | 11.0 | 4763 | 0.8371 | 0.8979 |
| 0.0069 | 12.0 | 5196 | 0.9002 | 0.8935 |
| 0.0108 | 13.0 | 5629 | 0.8321 | 0.8973 |
| 0.0072 | 14.0 | 6062 | 0.9234 | 0.8885 |
| 0.0072 | 15.0 | 6495 | 0.8939 | 0.8962 |
| 0.0068 | 16.0 | 6928 | 0.9207 | 0.8940 |
| 0.0045 | 17.0 | 7361 | 0.8769 | 0.8968 |
| 0.004 | 18.0 | 7794 | 0.9354 | 0.8946 |
| 0.0024 | 19.0 | 8227 | 0.9192 | 0.8968 |
| 0.0012 | 20.0 | 8660 | 0.8990 | 0.8984 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding40model | [
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Realgon/distilbert_sst2_padding50model | Realgon | 2023-11-29T06:49:28Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:49:28Z | 2023-11-28T18:25:59.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding50model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding50model
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: 0.8513
- Accuracy: 0.9044
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2978 | 0.8770 |
| 0.3562 | 2.0 | 866 | 0.2957 | 0.8951 |
| 0.1837 | 3.0 | 1299 | 0.4247 | 0.8979 |
| 0.0926 | 4.0 | 1732 | 0.5261 | 0.9006 |
| 0.0468 | 5.0 | 2165 | 0.5621 | 0.8995 |
| 0.0273 | 6.0 | 2598 | 0.6392 | 0.8995 |
| 0.017 | 7.0 | 3031 | 0.6437 | 0.9006 |
| 0.017 | 8.0 | 3464 | 0.6567 | 0.8973 |
| 0.0198 | 9.0 | 3897 | 0.6843 | 0.9017 |
| 0.0092 | 10.0 | 4330 | 0.7951 | 0.8984 |
| 0.0049 | 11.0 | 4763 | 0.8085 | 0.8984 |
| 0.0073 | 12.0 | 5196 | 0.7651 | 0.9055 |
| 0.011 | 13.0 | 5629 | 0.7794 | 0.9055 |
| 0.0066 | 14.0 | 6062 | 0.8064 | 0.9044 |
| 0.0066 | 15.0 | 6495 | 0.7824 | 0.9039 |
| 0.0063 | 16.0 | 6928 | 0.8132 | 0.9039 |
| 0.0065 | 17.0 | 7361 | 0.8399 | 0.9050 |
| 0.0024 | 18.0 | 7794 | 0.8511 | 0.9017 |
| 0.0056 | 19.0 | 8227 | 0.8723 | 0.9017 |
| 0.0025 | 20.0 | 8660 | 0.8513 | 0.9044 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding50model | [
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Realgon/distilbert_sst2_padding60model | Realgon | 2023-11-29T06:58:37Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T06:58:37Z | 2023-11-28T18:26:32.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding60model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding60model
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: 0.8463
- Accuracy: 0.9083
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2875 | 0.8803 |
| 0.388 | 2.0 | 866 | 0.3043 | 0.8907 |
| 0.2044 | 3.0 | 1299 | 0.4016 | 0.8902 |
| 0.103 | 4.0 | 1732 | 0.4731 | 0.9006 |
| 0.0553 | 5.0 | 2165 | 0.5760 | 0.8990 |
| 0.0328 | 6.0 | 2598 | 0.7112 | 0.8891 |
| 0.0198 | 7.0 | 3031 | 0.6813 | 0.9072 |
| 0.0198 | 8.0 | 3464 | 0.6863 | 0.9077 |
| 0.0155 | 9.0 | 3897 | 0.7397 | 0.8990 |
| 0.0132 | 10.0 | 4330 | 0.9001 | 0.8902 |
| 0.0091 | 11.0 | 4763 | 0.7295 | 0.9066 |
| 0.0065 | 12.0 | 5196 | 0.7631 | 0.9044 |
| 0.0106 | 13.0 | 5629 | 0.8739 | 0.8979 |
| 0.0039 | 14.0 | 6062 | 0.7804 | 0.9050 |
| 0.0039 | 15.0 | 6495 | 0.8326 | 0.9066 |
| 0.0071 | 16.0 | 6928 | 0.8924 | 0.8990 |
| 0.0043 | 17.0 | 7361 | 0.8991 | 0.9028 |
| 0.0052 | 18.0 | 7794 | 0.8528 | 0.9072 |
| 0.0025 | 19.0 | 8227 | 0.8650 | 0.9055 |
| 0.0021 | 20.0 | 8660 | 0.8463 | 0.9083 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding60model | [
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Realgon/distilbert_sst2_padding70model | Realgon | 2023-11-29T07:08:10Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:08:10Z | 2023-11-28T18:26:59.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding70model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding70model
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: 0.8873
- Accuracy: 0.9039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2844 | 0.8902 |
| 0.3687 | 2.0 | 866 | 0.3666 | 0.8819 |
| 0.1942 | 3.0 | 1299 | 0.3832 | 0.8968 |
| 0.0897 | 4.0 | 1732 | 0.5142 | 0.9012 |
| 0.0536 | 5.0 | 2165 | 0.5252 | 0.9028 |
| 0.0318 | 6.0 | 2598 | 0.6097 | 0.9001 |
| 0.0167 | 7.0 | 3031 | 0.7827 | 0.8858 |
| 0.0167 | 8.0 | 3464 | 0.7161 | 0.9044 |
| 0.0068 | 9.0 | 3897 | 0.7272 | 0.9072 |
| 0.012 | 10.0 | 4330 | 0.7150 | 0.9094 |
| 0.0076 | 11.0 | 4763 | 0.7475 | 0.9083 |
| 0.0098 | 12.0 | 5196 | 0.7788 | 0.9023 |
| 0.0108 | 13.0 | 5629 | 0.8121 | 0.9039 |
| 0.0051 | 14.0 | 6062 | 0.8801 | 0.8995 |
| 0.0051 | 15.0 | 6495 | 0.9357 | 0.8957 |
| 0.0089 | 16.0 | 6928 | 0.8661 | 0.8979 |
| 0.0036 | 17.0 | 7361 | 0.7991 | 0.9088 |
| 0.0027 | 18.0 | 7794 | 0.9503 | 0.8968 |
| 0.0013 | 19.0 | 8227 | 0.8711 | 0.9044 |
| 0.0011 | 20.0 | 8660 | 0.8873 | 0.9039 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding70model | [
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Realgon/distilbert_sst2_padding80model | Realgon | 2023-11-29T07:18:03Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:18:03Z | 2023-11-28T18:27:27.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding80model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding80model
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: 0.8177
- Accuracy: 0.9105
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2861 | 0.8819 |
| 0.3536 | 2.0 | 866 | 0.4254 | 0.8677 |
| 0.1914 | 3.0 | 1299 | 0.4265 | 0.8962 |
| 0.0987 | 4.0 | 1732 | 0.4661 | 0.9001 |
| 0.0533 | 5.0 | 2165 | 0.6232 | 0.9001 |
| 0.0231 | 6.0 | 2598 | 0.7215 | 0.8935 |
| 0.0291 | 7.0 | 3031 | 0.6435 | 0.9028 |
| 0.0291 | 8.0 | 3464 | 0.8011 | 0.8924 |
| 0.0168 | 9.0 | 3897 | 0.6787 | 0.9039 |
| 0.0155 | 10.0 | 4330 | 0.7006 | 0.9028 |
| 0.0094 | 11.0 | 4763 | 0.6975 | 0.9055 |
| 0.0103 | 12.0 | 5196 | 0.7654 | 0.9044 |
| 0.0074 | 13.0 | 5629 | 0.7544 | 0.9077 |
| 0.0061 | 14.0 | 6062 | 0.7732 | 0.9033 |
| 0.0061 | 15.0 | 6495 | 0.7836 | 0.9094 |
| 0.003 | 16.0 | 6928 | 0.9079 | 0.9028 |
| 0.0046 | 17.0 | 7361 | 0.8466 | 0.9055 |
| 0.0018 | 18.0 | 7794 | 0.8358 | 0.9044 |
| 0.004 | 19.0 | 8227 | 0.8357 | 0.9083 |
| 0.0016 | 20.0 | 8660 | 0.8177 | 0.9105 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding80model | [
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Realgon/distilbert_sst2_padding90model | Realgon | 2023-11-29T07:28:26Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:28:26Z | 2023-11-28T18:27:55.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding90model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding90model
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: 0.8603
- Accuracy: 0.9077
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2769 | 0.8858 |
| 0.361 | 2.0 | 866 | 0.3695 | 0.8935 |
| 0.1858 | 3.0 | 1299 | 0.4577 | 0.8929 |
| 0.0971 | 4.0 | 1732 | 0.5781 | 0.8896 |
| 0.0575 | 5.0 | 2165 | 0.6172 | 0.8940 |
| 0.0255 | 6.0 | 2598 | 0.6606 | 0.8951 |
| 0.0211 | 7.0 | 3031 | 0.7161 | 0.8990 |
| 0.0211 | 8.0 | 3464 | 0.6963 | 0.9083 |
| 0.0221 | 9.0 | 3897 | 0.6817 | 0.9044 |
| 0.0176 | 10.0 | 4330 | 0.7174 | 0.9077 |
| 0.0092 | 11.0 | 4763 | 0.7863 | 0.9001 |
| 0.0126 | 12.0 | 5196 | 0.8451 | 0.8968 |
| 0.0057 | 13.0 | 5629 | 0.7709 | 0.9066 |
| 0.0043 | 14.0 | 6062 | 0.8267 | 0.9061 |
| 0.0043 | 15.0 | 6495 | 0.8121 | 0.9050 |
| 0.0029 | 16.0 | 6928 | 0.9722 | 0.8940 |
| 0.0048 | 17.0 | 7361 | 0.8179 | 0.9116 |
| 0.0031 | 18.0 | 7794 | 0.8398 | 0.9105 |
| 0.0025 | 19.0 | 8227 | 0.8607 | 0.9072 |
| 0.001 | 20.0 | 8660 | 0.8603 | 0.9077 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding90model | [
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Realgon/distilbert_sst2_padding100model | Realgon | 2023-11-29T07:39:16Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:39:16Z | 2023-11-28T18:28:24.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst2_padding100model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst2_padding100model
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: 0.8470
- Accuracy: 0.9050
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 433 | 0.2619 | 0.9012 |
| 0.3643 | 2.0 | 866 | 0.2941 | 0.8973 |
| 0.1913 | 3.0 | 1299 | 0.3841 | 0.8951 |
| 0.0956 | 4.0 | 1732 | 0.5066 | 0.9006 |
| 0.0531 | 5.0 | 2165 | 0.7628 | 0.8786 |
| 0.0336 | 6.0 | 2598 | 0.6779 | 0.8940 |
| 0.0141 | 7.0 | 3031 | 0.7910 | 0.8913 |
| 0.0141 | 8.0 | 3464 | 0.8459 | 0.8885 |
| 0.0159 | 9.0 | 3897 | 0.6874 | 0.9023 |
| 0.0151 | 10.0 | 4330 | 0.9174 | 0.8819 |
| 0.0159 | 11.0 | 4763 | 0.7183 | 0.9033 |
| 0.012 | 12.0 | 5196 | 0.8621 | 0.8880 |
| 0.0063 | 13.0 | 5629 | 0.8841 | 0.8957 |
| 0.0064 | 14.0 | 6062 | 0.7985 | 0.9066 |
| 0.0064 | 15.0 | 6495 | 0.8537 | 0.9028 |
| 0.0075 | 16.0 | 6928 | 0.8202 | 0.9055 |
| 0.0042 | 17.0 | 7361 | 0.8655 | 0.9023 |
| 0.0029 | 18.0 | 7794 | 0.8738 | 0.9006 |
| 0.0034 | 19.0 | 8227 | 0.8642 | 0.9023 |
| 0.0012 | 20.0 | 8660 | 0.8470 | 0.9050 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst2_padding100model | [
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Realgon/distilbert_sst5_padding0model | Realgon | 2023-11-29T07:47:22Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:47:22Z | 2023-11-28T18:28:53.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding0model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding0model
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: 4.1295
- Accuracy: 0.5018
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2692 | 1.0 | 534 | 1.2088 | 0.4462 |
| 1.0128 | 2.0 | 1068 | 1.1091 | 0.5299 |
| 0.8129 | 3.0 | 1602 | 1.1930 | 0.5262 |
| 0.6431 | 4.0 | 2136 | 1.3783 | 0.5213 |
| 0.4776 | 5.0 | 2670 | 1.7332 | 0.4783 |
| 0.3481 | 6.0 | 3204 | 1.7883 | 0.5140 |
| 0.2633 | 7.0 | 3738 | 2.1206 | 0.4955 |
| 0.191 | 8.0 | 4272 | 2.3658 | 0.5054 |
| 0.1627 | 9.0 | 4806 | 2.6203 | 0.5149 |
| 0.1387 | 10.0 | 5340 | 3.0193 | 0.4955 |
| 0.1049 | 11.0 | 5874 | 3.3081 | 0.5014 |
| 0.0888 | 12.0 | 6408 | 3.5941 | 0.4959 |
| 0.0689 | 13.0 | 6942 | 3.6559 | 0.5095 |
| 0.0615 | 14.0 | 7476 | 3.7450 | 0.5090 |
| 0.0426 | 15.0 | 8010 | 3.9371 | 0.5032 |
| 0.0372 | 16.0 | 8544 | 4.0202 | 0.5086 |
| 0.0255 | 17.0 | 9078 | 4.1399 | 0.4982 |
| 0.0221 | 18.0 | 9612 | 4.1091 | 0.5045 |
| 0.0237 | 19.0 | 10146 | 4.1077 | 0.5009 |
| 0.0206 | 20.0 | 10680 | 4.1295 | 0.5018 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding0model | [
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Realgon/distilbert_sst5_padding10model | Realgon | 2023-11-29T07:55:53Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:55:53Z | 2023-11-28T18:29:22.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding10model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding10model
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: 4.0781
- Accuracy: 0.5036
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2838 | 1.0 | 534 | 1.2476 | 0.4281 |
| 1.0188 | 2.0 | 1068 | 1.1170 | 0.5231 |
| 0.8166 | 3.0 | 1602 | 1.2120 | 0.5131 |
| 0.6648 | 4.0 | 2136 | 1.3360 | 0.5172 |
| 0.501 | 5.0 | 2670 | 1.6498 | 0.4896 |
| 0.3858 | 6.0 | 3204 | 1.7835 | 0.5023 |
| 0.2878 | 7.0 | 3738 | 2.0075 | 0.5063 |
| 0.2136 | 8.0 | 4272 | 2.1988 | 0.4982 |
| 0.1796 | 9.0 | 4806 | 2.4884 | 0.5086 |
| 0.1443 | 10.0 | 5340 | 2.8428 | 0.4946 |
| 0.1189 | 11.0 | 5874 | 3.0841 | 0.4923 |
| 0.096 | 12.0 | 6408 | 3.2575 | 0.5041 |
| 0.0774 | 13.0 | 6942 | 3.5018 | 0.4977 |
| 0.064 | 14.0 | 7476 | 3.6108 | 0.5086 |
| 0.0469 | 15.0 | 8010 | 3.7317 | 0.5054 |
| 0.0364 | 16.0 | 8544 | 3.9036 | 0.4923 |
| 0.0292 | 17.0 | 9078 | 3.9546 | 0.5036 |
| 0.0244 | 18.0 | 9612 | 4.0218 | 0.5054 |
| 0.0256 | 19.0 | 10146 | 4.0257 | 0.5068 |
| 0.021 | 20.0 | 10680 | 4.0781 | 0.5036 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding10model | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_26 | Jeevesh8 | 2023-11-29T02:43:41Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:43:41Z | 2023-11-28T18:29:35.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_26 | [
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Realgon/distilbert_sst5_padding20model | Realgon | 2023-11-29T08:05:08Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:05:08Z | 2023-11-28T18:29:48.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding20model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding20model
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: 4.0295
- Accuracy: 0.5081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3053 | 1.0 | 534 | 1.2846 | 0.4262 |
| 1.04 | 2.0 | 1068 | 1.1241 | 0.5281 |
| 0.8475 | 3.0 | 1602 | 1.1618 | 0.5199 |
| 0.6826 | 4.0 | 2136 | 1.3165 | 0.5204 |
| 0.5346 | 5.0 | 2670 | 1.6460 | 0.4959 |
| 0.4044 | 6.0 | 3204 | 1.7466 | 0.5032 |
| 0.3145 | 7.0 | 3738 | 1.9172 | 0.5014 |
| 0.2246 | 8.0 | 4272 | 2.1789 | 0.5014 |
| 0.1915 | 9.0 | 4806 | 2.4467 | 0.5 |
| 0.1632 | 10.0 | 5340 | 2.7215 | 0.4928 |
| 0.1238 | 11.0 | 5874 | 3.0191 | 0.4964 |
| 0.1245 | 12.0 | 6408 | 3.2605 | 0.5068 |
| 0.0853 | 13.0 | 6942 | 3.5142 | 0.5050 |
| 0.0763 | 14.0 | 7476 | 3.5273 | 0.5036 |
| 0.0448 | 15.0 | 8010 | 3.7739 | 0.5100 |
| 0.0527 | 16.0 | 8544 | 3.8209 | 0.5081 |
| 0.0372 | 17.0 | 9078 | 3.9001 | 0.5081 |
| 0.028 | 18.0 | 9612 | 3.8935 | 0.5063 |
| 0.0262 | 19.0 | 10146 | 3.9926 | 0.5095 |
| 0.0285 | 20.0 | 10680 | 4.0295 | 0.5081 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding20model | [
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alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01 | alinerodrigues | 2023-11-29T01:59:06Z | 0 | 0 | null | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T01:59:06Z | 2023-11-28T18:30:07.000Z | null | null | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1429
- Wer: 0.0862
- Cer: 0.0263
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 31.9255 | 1.0 | 86 | 3.2559 | 1.0 | 1.0 |
| 8.1132 | 2.0 | 172 | 3.0009 | 1.0 | 1.0 |
| 3.0533 | 3.0 | 258 | 2.9120 | 1.0 | 1.0 |
| 2.93 | 4.0 | 344 | 2.8877 | 1.0 | 1.0 |
| 2.8396 | 5.0 | 430 | 1.9900 | 1.0 | 0.7238 |
| 1.8884 | 6.0 | 516 | 0.6068 | 0.3305 | 0.0859 |
| 0.8835 | 7.0 | 602 | 0.3984 | 0.2086 | 0.0553 |
| 0.8835 | 8.0 | 688 | 0.3249 | 0.1780 | 0.0492 |
| 0.6254 | 9.0 | 774 | 0.2698 | 0.1662 | 0.0450 |
| 0.5176 | 10.0 | 860 | 0.2420 | 0.1408 | 0.0400 |
| 0.4379 | 11.0 | 946 | 0.2315 | 0.1322 | 0.0376 |
| 0.3823 | 12.0 | 1032 | 0.2185 | 0.1261 | 0.0356 |
| 0.3601 | 13.0 | 1118 | 0.2176 | 0.1148 | 0.0344 |
| 0.3457 | 14.0 | 1204 | 0.2050 | 0.1119 | 0.0330 |
| 0.3457 | 15.0 | 1290 | 0.1928 | 0.1048 | 0.0306 |
| 0.2942 | 16.0 | 1376 | 0.1864 | 0.0994 | 0.0305 |
| 0.3054 | 17.0 | 1462 | 0.1826 | 0.0987 | 0.0297 |
| 0.321 | 18.0 | 1548 | 0.1755 | 0.0974 | 0.0298 |
| 0.2667 | 19.0 | 1634 | 0.1743 | 0.0996 | 0.0297 |
| 0.2706 | 20.0 | 1720 | 0.1720 | 0.0965 | 0.0288 |
| 0.2355 | 21.0 | 1806 | 0.1646 | 0.0935 | 0.0283 |
| 0.2355 | 22.0 | 1892 | 0.1645 | 0.0935 | 0.0287 |
| 0.2355 | 23.0 | 1978 | 0.1583 | 0.0891 | 0.0271 |
| 0.2401 | 24.0 | 2064 | 0.1578 | 0.0923 | 0.0283 |
| 0.236 | 25.0 | 2150 | 0.1587 | 0.0894 | 0.0280 |
| 0.2314 | 26.0 | 2236 | 0.1547 | 0.0896 | 0.0274 |
| 0.2209 | 27.0 | 2322 | 0.1513 | 0.0891 | 0.0266 |
| 0.2269 | 28.0 | 2408 | 0.1550 | 0.0891 | 0.0270 |
| 0.2269 | 29.0 | 2494 | 0.1566 | 0.0898 | 0.0273 |
| 0.2123 | 30.0 | 2580 | 0.1572 | 0.0898 | 0.0273 |
| 0.1941 | 31.0 | 2666 | 0.1518 | 0.0867 | 0.0266 |
| 0.2108 | 32.0 | 2752 | 0.1492 | 0.0869 | 0.0266 |
| 0.187 | 33.0 | 2838 | 0.1479 | 0.0864 | 0.0268 |
| 0.1799 | 34.0 | 2924 | 0.1429 | 0.0862 | 0.0263 |
| 0.1804 | 35.0 | 3010 | 0.1472 | 0.0835 | 0.0257 |
| 0.1804 | 36.0 | 3096 | 0.1457 | 0.0857 | 0.0262 |
| 0.1756 | 37.0 | 3182 | 0.1456 | 0.0830 | 0.0254 |
| 0.1684 | 38.0 | 3268 | 0.1459 | 0.0857 | 0.0259 |
| 0.1692 | 39.0 | 3354 | 0.1461 | 0.0840 | 0.0263 |
| 0.1609 | 40.0 | 3440 | 0.1432 | 0.0837 | 0.0260 |
| 0.1877 | 41.0 | 3526 | 0.1475 | 0.0818 | 0.0253 |
| 0.1611 | 42.0 | 3612 | 0.1434 | 0.0830 | 0.0261 |
| 0.1611 | 43.0 | 3698 | 0.1438 | 0.0827 | 0.0247 |
| 0.1887 | 44.0 | 3784 | 0.1478 | 0.0832 | 0.0255 |
| 0.1712 | 45.0 | 3870 | 0.1493 | 0.0842 | 0.0257 |
| 0.1531 | 46.0 | 3956 | 0.1493 | 0.0803 | 0.0250 |
| 0.1605 | 47.0 | 4042 | 0.1479 | 0.0842 | 0.0257 |
| 0.1599 | 48.0 | 4128 | 0.1455 | 0.0818 | 0.0250 |
| 0.1623 | 49.0 | 4214 | 0.1470 | 0.0823 | 0.0252 |
| 0.1601 | 50.0 | 4300 | 0.1474 | 0.0793 | 0.0252 |
| 0.1601 | 51.0 | 4386 | 0.1491 | 0.0798 | 0.0250 |
| 0.1478 | 52.0 | 4472 | 0.1491 | 0.0796 | 0.0254 |
| 0.1497 | 53.0 | 4558 | 0.1481 | 0.0815 | 0.0249 |
| 0.1454 | 54.0 | 4644 | 0.1455 | 0.0796 | 0.0245 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.13.3
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | alinerodrigues/wav2vec2-large-xlsr-mecita-coraa-portuguese-2-all-01 | [
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Realgon/distilbert_sst5_padding30model | Realgon | 2023-11-29T08:14:43Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:14:43Z | 2023-11-28T18:30:16.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding30model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding30model
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: 3.9313
- Accuracy: 0.5027
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2856 | 1.0 | 534 | 1.3049 | 0.4190 |
| 1.03 | 2.0 | 1068 | 1.1134 | 0.5154 |
| 0.8354 | 3.0 | 1602 | 1.1725 | 0.5154 |
| 0.6773 | 4.0 | 2136 | 1.3321 | 0.5158 |
| 0.5157 | 5.0 | 2670 | 1.6081 | 0.5009 |
| 0.3964 | 6.0 | 3204 | 1.7841 | 0.4950 |
| 0.3171 | 7.0 | 3738 | 1.8784 | 0.5158 |
| 0.2289 | 8.0 | 4272 | 2.1902 | 0.5072 |
| 0.1917 | 9.0 | 4806 | 2.5022 | 0.5 |
| 0.153 | 10.0 | 5340 | 2.7217 | 0.5014 |
| 0.147 | 11.0 | 5874 | 2.9098 | 0.4995 |
| 0.1279 | 12.0 | 6408 | 3.1218 | 0.5136 |
| 0.08 | 13.0 | 6942 | 3.5110 | 0.4982 |
| 0.0688 | 14.0 | 7476 | 3.4982 | 0.4959 |
| 0.0552 | 15.0 | 8010 | 3.6294 | 0.5041 |
| 0.0517 | 16.0 | 8544 | 3.7352 | 0.4968 |
| 0.0337 | 17.0 | 9078 | 3.8002 | 0.4986 |
| 0.0332 | 18.0 | 9612 | 3.8191 | 0.5081 |
| 0.0272 | 19.0 | 10146 | 3.9221 | 0.5032 |
| 0.0237 | 20.0 | 10680 | 3.9313 | 0.5027 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding30model | [
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Realgon/distilbert_sst5_padding40model | Realgon | 2023-11-29T08:24:56Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:24:56Z | 2023-11-28T18:30:44.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding40model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding40model
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: 4.1102
- Accuracy: 0.5032
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2973 | 1.0 | 534 | 1.2639 | 0.4226 |
| 1.0311 | 2.0 | 1068 | 1.1147 | 0.5122 |
| 0.8351 | 3.0 | 1602 | 1.2030 | 0.5086 |
| 0.68 | 4.0 | 2136 | 1.3100 | 0.5213 |
| 0.5243 | 5.0 | 2670 | 1.5365 | 0.4995 |
| 0.3959 | 6.0 | 3204 | 1.7658 | 0.4946 |
| 0.304 | 7.0 | 3738 | 1.9953 | 0.5050 |
| 0.2223 | 8.0 | 4272 | 2.1893 | 0.4991 |
| 0.1758 | 9.0 | 4806 | 2.5567 | 0.4986 |
| 0.1431 | 10.0 | 5340 | 2.8571 | 0.4982 |
| 0.1208 | 11.0 | 5874 | 3.0516 | 0.5027 |
| 0.1104 | 12.0 | 6408 | 3.3050 | 0.5095 |
| 0.0792 | 13.0 | 6942 | 3.5054 | 0.5014 |
| 0.0761 | 14.0 | 7476 | 3.6607 | 0.5009 |
| 0.0447 | 15.0 | 8010 | 3.8772 | 0.5032 |
| 0.0377 | 16.0 | 8544 | 3.9335 | 0.5050 |
| 0.0297 | 17.0 | 9078 | 4.0339 | 0.5059 |
| 0.0255 | 18.0 | 9612 | 4.0308 | 0.5095 |
| 0.0258 | 19.0 | 10146 | 4.0368 | 0.5086 |
| 0.0182 | 20.0 | 10680 | 4.1102 | 0.5032 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding40model | [
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Realgon/distilbert_sst5_padding50model | Realgon | 2023-11-29T08:35:32Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:35:32Z | 2023-11-28T18:31:20.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding50model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding50model
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: 4.0670
- Accuracy: 0.5005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2884 | 1.0 | 534 | 1.3287 | 0.4136 |
| 1.0358 | 2.0 | 1068 | 1.1270 | 0.4986 |
| 0.8387 | 3.0 | 1602 | 1.1758 | 0.5145 |
| 0.6855 | 4.0 | 2136 | 1.3584 | 0.5127 |
| 0.5306 | 5.0 | 2670 | 1.5252 | 0.5077 |
| 0.3926 | 6.0 | 3204 | 1.8285 | 0.4896 |
| 0.3021 | 7.0 | 3738 | 1.9973 | 0.4973 |
| 0.2153 | 8.0 | 4272 | 2.2816 | 0.4986 |
| 0.1848 | 9.0 | 4806 | 2.4827 | 0.5005 |
| 0.1489 | 10.0 | 5340 | 2.8858 | 0.4955 |
| 0.1244 | 11.0 | 5874 | 3.1325 | 0.4946 |
| 0.1026 | 12.0 | 6408 | 3.1882 | 0.5113 |
| 0.0853 | 13.0 | 6942 | 3.5083 | 0.4991 |
| 0.0623 | 14.0 | 7476 | 3.6773 | 0.5081 |
| 0.0476 | 15.0 | 8010 | 3.7727 | 0.4995 |
| 0.0422 | 16.0 | 8544 | 3.8041 | 0.5072 |
| 0.0262 | 17.0 | 9078 | 3.9936 | 0.5009 |
| 0.0297 | 18.0 | 9612 | 4.0013 | 0.4964 |
| 0.021 | 19.0 | 10146 | 4.1473 | 0.5018 |
| 0.0222 | 20.0 | 10680 | 4.0670 | 0.5005 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding50model | [
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Realgon/distilbert_sst5_padding60model | Realgon | 2023-11-29T08:46:43Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:46:43Z | 2023-11-28T18:31:49.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding60model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding60model
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: 4.1071
- Accuracy: 0.5005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2904 | 1.0 | 534 | 1.2735 | 0.4204 |
| 1.0348 | 2.0 | 1068 | 1.1222 | 0.5217 |
| 0.8515 | 3.0 | 1602 | 1.1982 | 0.5172 |
| 0.6867 | 4.0 | 2136 | 1.3441 | 0.5018 |
| 0.5308 | 5.0 | 2670 | 1.5426 | 0.4964 |
| 0.396 | 6.0 | 3204 | 1.7466 | 0.5032 |
| 0.3113 | 7.0 | 3738 | 1.9661 | 0.4959 |
| 0.2237 | 8.0 | 4272 | 2.3050 | 0.4882 |
| 0.1863 | 9.0 | 4806 | 2.4120 | 0.5068 |
| 0.1458 | 10.0 | 5340 | 2.7065 | 0.4950 |
| 0.1202 | 11.0 | 5874 | 3.0306 | 0.4900 |
| 0.1124 | 12.0 | 6408 | 3.1912 | 0.5041 |
| 0.0838 | 13.0 | 6942 | 3.3632 | 0.5050 |
| 0.0798 | 14.0 | 7476 | 3.6172 | 0.4968 |
| 0.0475 | 15.0 | 8010 | 3.7530 | 0.4973 |
| 0.0436 | 16.0 | 8544 | 3.7669 | 0.5014 |
| 0.0302 | 17.0 | 9078 | 3.9727 | 0.5023 |
| 0.0223 | 18.0 | 9612 | 4.0368 | 0.5054 |
| 0.0236 | 19.0 | 10146 | 4.0607 | 0.5077 |
| 0.0247 | 20.0 | 10680 | 4.1071 | 0.5005 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Realgon/distilbert_sst5_padding70model | Realgon | 2023-11-29T08:58:20Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:58:20Z | 2023-11-28T18:32:19.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding70model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding70model
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: 3.9464
- Accuracy: 0.5054
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.342 | 1.0 | 534 | 1.3172 | 0.3914 |
| 1.0595 | 2.0 | 1068 | 1.1123 | 0.5140 |
| 0.8701 | 3.0 | 1602 | 1.1455 | 0.5281 |
| 0.71 | 4.0 | 2136 | 1.3218 | 0.5163 |
| 0.5487 | 5.0 | 2670 | 1.5399 | 0.5027 |
| 0.4201 | 6.0 | 3204 | 1.7212 | 0.5005 |
| 0.3293 | 7.0 | 3738 | 1.8778 | 0.5113 |
| 0.2412 | 8.0 | 4272 | 2.0483 | 0.5158 |
| 0.207 | 9.0 | 4806 | 2.2857 | 0.5113 |
| 0.1778 | 10.0 | 5340 | 2.5679 | 0.5113 |
| 0.1325 | 11.0 | 5874 | 2.8006 | 0.5077 |
| 0.1154 | 12.0 | 6408 | 3.1458 | 0.4991 |
| 0.0927 | 13.0 | 6942 | 3.2323 | 0.5109 |
| 0.0801 | 14.0 | 7476 | 3.5445 | 0.4937 |
| 0.0542 | 15.0 | 8010 | 3.6395 | 0.5100 |
| 0.0515 | 16.0 | 8544 | 3.7555 | 0.5050 |
| 0.0369 | 17.0 | 9078 | 3.8901 | 0.5059 |
| 0.0381 | 18.0 | 9612 | 3.9145 | 0.5059 |
| 0.0265 | 19.0 | 10146 | 3.9511 | 0.5081 |
| 0.0244 | 20.0 | 10680 | 3.9464 | 0.5054 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding70model | [
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Realgon/distilbert_sst5_padding80model | Realgon | 2023-11-29T09:10:18Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:10:18Z | 2023-11-28T18:32:49.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding80model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding80model
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: 3.9171
- Accuracy: 0.5054
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3494 | 1.0 | 534 | 1.2564 | 0.4181 |
| 1.0617 | 2.0 | 1068 | 1.1176 | 0.4982 |
| 0.8787 | 3.0 | 1602 | 1.1842 | 0.5149 |
| 0.7134 | 4.0 | 2136 | 1.2718 | 0.5163 |
| 0.5548 | 5.0 | 2670 | 1.4860 | 0.5104 |
| 0.4256 | 6.0 | 3204 | 1.7135 | 0.4986 |
| 0.3318 | 7.0 | 3738 | 1.8871 | 0.4928 |
| 0.2476 | 8.0 | 4272 | 2.0403 | 0.5127 |
| 0.1946 | 9.0 | 4806 | 2.2836 | 0.4982 |
| 0.1737 | 10.0 | 5340 | 2.5511 | 0.5027 |
| 0.1482 | 11.0 | 5874 | 2.8407 | 0.5036 |
| 0.1166 | 12.0 | 6408 | 3.1513 | 0.4959 |
| 0.0901 | 13.0 | 6942 | 3.2833 | 0.5027 |
| 0.0825 | 14.0 | 7476 | 3.4168 | 0.5041 |
| 0.0551 | 15.0 | 8010 | 3.5105 | 0.5032 |
| 0.0492 | 16.0 | 8544 | 3.6475 | 0.5014 |
| 0.0367 | 17.0 | 9078 | 3.7859 | 0.5045 |
| 0.0351 | 18.0 | 9612 | 3.8480 | 0.4977 |
| 0.0316 | 19.0 | 10146 | 3.9006 | 0.5023 |
| 0.0264 | 20.0 | 10680 | 3.9171 | 0.5054 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding80model | [
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Realgon/distilbert_sst5_padding90model | Realgon | 2023-11-29T09:23:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:23:04Z | 2023-11-28T18:33:22.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding90model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding90model
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: 3.9832
- Accuracy: 0.4959
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.3043 | 1.0 | 534 | 1.2710 | 0.4100 |
| 1.0413 | 2.0 | 1068 | 1.1063 | 0.5149 |
| 0.8567 | 3.0 | 1602 | 1.1613 | 0.5217 |
| 0.692 | 4.0 | 2136 | 1.3199 | 0.5154 |
| 0.539 | 5.0 | 2670 | 1.5717 | 0.4914 |
| 0.4233 | 6.0 | 3204 | 1.6996 | 0.5036 |
| 0.3198 | 7.0 | 3738 | 1.9868 | 0.5041 |
| 0.2373 | 8.0 | 4272 | 2.1062 | 0.4995 |
| 0.2003 | 9.0 | 4806 | 2.4530 | 0.4887 |
| 0.16 | 10.0 | 5340 | 2.6820 | 0.4986 |
| 0.1309 | 11.0 | 5874 | 2.8490 | 0.5072 |
| 0.1208 | 12.0 | 6408 | 3.2394 | 0.4982 |
| 0.0872 | 13.0 | 6942 | 3.3667 | 0.4964 |
| 0.0776 | 14.0 | 7476 | 3.5361 | 0.5 |
| 0.0562 | 15.0 | 8010 | 3.7808 | 0.4887 |
| 0.0502 | 16.0 | 8544 | 3.7929 | 0.4991 |
| 0.0369 | 17.0 | 9078 | 3.9267 | 0.4887 |
| 0.0311 | 18.0 | 9612 | 3.8721 | 0.5 |
| 0.0235 | 19.0 | 10146 | 3.9710 | 0.4986 |
| 0.033 | 20.0 | 10680 | 3.9832 | 0.4959 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding90model | [
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Realgon/distilbert_sst5_padding100model | Realgon | 2023-11-29T09:36:09Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:36:09Z | 2023-11-28T18:33:56.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_sst5_padding100model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_sst5_padding100model
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: 3.6964
- Accuracy: 0.4878
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.5744 | 1.0 | 534 | 1.6004 | 0.2308 |
| 1.4231 | 2.0 | 1068 | 1.2088 | 0.4729 |
| 1.1237 | 3.0 | 1602 | 1.1682 | 0.5 |
| 0.9507 | 4.0 | 2136 | 1.2027 | 0.5054 |
| 0.7604 | 5.0 | 2670 | 1.3283 | 0.4995 |
| 0.6266 | 6.0 | 3204 | 1.4933 | 0.4959 |
| 0.488 | 7.0 | 3738 | 1.6948 | 0.4851 |
| 0.3806 | 8.0 | 4272 | 1.8964 | 0.4896 |
| 0.3127 | 9.0 | 4806 | 1.9536 | 0.5014 |
| 0.2609 | 10.0 | 5340 | 2.1723 | 0.4919 |
| 0.2133 | 11.0 | 5874 | 2.4683 | 0.4864 |
| 0.1876 | 12.0 | 6408 | 2.6453 | 0.4941 |
| 0.1634 | 13.0 | 6942 | 2.9011 | 0.4891 |
| 0.1386 | 14.0 | 7476 | 3.0697 | 0.4941 |
| 0.1026 | 15.0 | 8010 | 3.3209 | 0.4900 |
| 0.0909 | 16.0 | 8544 | 3.5261 | 0.4914 |
| 0.0728 | 17.0 | 9078 | 3.5774 | 0.4873 |
| 0.0756 | 18.0 | 9612 | 3.6430 | 0.4891 |
| 0.059 | 19.0 | 10146 | 3.6841 | 0.4873 |
| 0.0476 | 20.0 | 10680 | 3.6964 | 0.4878 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_sst5_padding100model | [
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Realgon/distilbert_twitterfin_padding0model | Realgon | 2023-11-29T09:45:11Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:45:11Z | 2023-11-28T18:34:29.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding0model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding0model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0496
- Accuracy: 0.8844
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5625 | 1.0 | 597 | 0.3936 | 0.8526 |
| 0.3236 | 2.0 | 1194 | 0.3517 | 0.8748 |
| 0.231 | 3.0 | 1791 | 0.4241 | 0.8794 |
| 0.1474 | 4.0 | 2388 | 0.5579 | 0.8807 |
| 0.1004 | 5.0 | 2985 | 0.6444 | 0.8848 |
| 0.0419 | 6.0 | 3582 | 0.7431 | 0.8765 |
| 0.0394 | 7.0 | 4179 | 0.7534 | 0.8790 |
| 0.0287 | 8.0 | 4776 | 0.7662 | 0.8819 |
| 0.0262 | 9.0 | 5373 | 0.8529 | 0.8819 |
| 0.0168 | 10.0 | 5970 | 0.8335 | 0.8844 |
| 0.0115 | 11.0 | 6567 | 0.8641 | 0.8823 |
| 0.0141 | 12.0 | 7164 | 0.9629 | 0.8760 |
| 0.0097 | 13.0 | 7761 | 0.9226 | 0.8844 |
| 0.0066 | 14.0 | 8358 | 0.9800 | 0.8798 |
| 0.0033 | 15.0 | 8955 | 0.9822 | 0.8844 |
| 0.0036 | 16.0 | 9552 | 0.9928 | 0.8827 |
| 0.0029 | 17.0 | 10149 | 1.0094 | 0.8857 |
| 0.0009 | 18.0 | 10746 | 1.0535 | 0.8857 |
| 0.0011 | 19.0 | 11343 | 1.0431 | 0.8815 |
| 0.0034 | 20.0 | 11940 | 1.0496 | 0.8844 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding0model | [
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Realgon/distilbert_twitterfin_padding10model | Realgon | 2023-11-29T09:54:48Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:54:48Z | 2023-11-28T18:34:56.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding10model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding10model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0996
- Accuracy: 0.8815
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.5889 | 1.0 | 597 | 0.3931 | 0.8513 |
| 0.3408 | 2.0 | 1194 | 0.3420 | 0.8819 |
| 0.2458 | 3.0 | 1791 | 0.3978 | 0.8807 |
| 0.1516 | 4.0 | 2388 | 0.5900 | 0.8819 |
| 0.1098 | 5.0 | 2985 | 0.6569 | 0.8781 |
| 0.0499 | 6.0 | 3582 | 0.7546 | 0.8756 |
| 0.042 | 7.0 | 4179 | 0.7920 | 0.8765 |
| 0.0348 | 8.0 | 4776 | 0.8574 | 0.8673 |
| 0.0341 | 9.0 | 5373 | 0.8166 | 0.8781 |
| 0.0235 | 10.0 | 5970 | 0.9018 | 0.8760 |
| 0.0154 | 11.0 | 6567 | 0.9876 | 0.8727 |
| 0.0115 | 12.0 | 7164 | 0.9529 | 0.8836 |
| 0.0104 | 13.0 | 7761 | 0.9724 | 0.8765 |
| 0.0076 | 14.0 | 8358 | 1.0292 | 0.8735 |
| 0.0089 | 15.0 | 8955 | 1.0013 | 0.8723 |
| 0.0033 | 16.0 | 9552 | 1.0696 | 0.8840 |
| 0.0076 | 17.0 | 10149 | 1.1262 | 0.8681 |
| 0.001 | 18.0 | 10746 | 1.1008 | 0.8773 |
| 0.0016 | 19.0 | 11343 | 1.0923 | 0.8811 |
| 0.0039 | 20.0 | 11940 | 1.0996 | 0.8815 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding10model | [
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Realgon/distilbert_twitterfin_padding20model | Realgon | 2023-11-29T10:05:11Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:05:11Z | 2023-11-28T18:35:22.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding20model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding20model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1861
- Accuracy: 0.8664
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6755 | 1.0 | 597 | 0.4424 | 0.8312 |
| 0.3928 | 2.0 | 1194 | 0.3581 | 0.8756 |
| 0.2881 | 3.0 | 1791 | 0.4017 | 0.8702 |
| 0.1946 | 4.0 | 2388 | 0.5543 | 0.8706 |
| 0.1448 | 5.0 | 2985 | 0.6680 | 0.8585 |
| 0.0613 | 6.0 | 3582 | 0.7264 | 0.8585 |
| 0.0517 | 7.0 | 4179 | 0.8782 | 0.8446 |
| 0.0459 | 8.0 | 4776 | 0.9466 | 0.8539 |
| 0.0331 | 9.0 | 5373 | 0.8594 | 0.8693 |
| 0.0285 | 10.0 | 5970 | 0.9603 | 0.8606 |
| 0.02 | 11.0 | 6567 | 1.0244 | 0.8585 |
| 0.0173 | 12.0 | 7164 | 1.0103 | 0.8610 |
| 0.017 | 13.0 | 7761 | 1.0972 | 0.8652 |
| 0.0102 | 14.0 | 8358 | 1.1062 | 0.8660 |
| 0.0052 | 15.0 | 8955 | 1.1196 | 0.8664 |
| 0.0054 | 16.0 | 9552 | 1.1968 | 0.8681 |
| 0.0072 | 17.0 | 10149 | 1.1564 | 0.8664 |
| 0.001 | 18.0 | 10746 | 1.1946 | 0.8723 |
| 0.0043 | 19.0 | 11343 | 1.1976 | 0.8689 |
| 0.0021 | 20.0 | 11940 | 1.1861 | 0.8664 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding20model | [
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Realgon/distilbert_twitterfin_padding30model | Realgon | 2023-11-29T10:15:59Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:15:59Z | 2023-11-28T18:35:52.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding30model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding30model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1053
- Accuracy: 0.8794
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6111 | 1.0 | 597 | 0.3870 | 0.8589 |
| 0.3604 | 2.0 | 1194 | 0.3419 | 0.8790 |
| 0.263 | 3.0 | 1791 | 0.3833 | 0.8740 |
| 0.1701 | 4.0 | 2388 | 0.5289 | 0.8769 |
| 0.1309 | 5.0 | 2985 | 0.6484 | 0.8580 |
| 0.058 | 6.0 | 3582 | 0.7059 | 0.8719 |
| 0.0494 | 7.0 | 4179 | 0.7958 | 0.8698 |
| 0.0403 | 8.0 | 4776 | 0.8021 | 0.8752 |
| 0.0324 | 9.0 | 5373 | 0.8740 | 0.8744 |
| 0.0274 | 10.0 | 5970 | 0.9417 | 0.8760 |
| 0.0148 | 11.0 | 6567 | 0.9449 | 0.8735 |
| 0.0102 | 12.0 | 7164 | 1.0041 | 0.8735 |
| 0.0144 | 13.0 | 7761 | 1.0093 | 0.8807 |
| 0.0113 | 14.0 | 8358 | 1.0083 | 0.8748 |
| 0.0052 | 15.0 | 8955 | 1.0455 | 0.8773 |
| 0.0053 | 16.0 | 9552 | 1.0514 | 0.8832 |
| 0.0029 | 17.0 | 10149 | 1.1099 | 0.8760 |
| 0.0029 | 18.0 | 10746 | 1.1048 | 0.8777 |
| 0.0019 | 19.0 | 11343 | 1.1120 | 0.8781 |
| 0.0017 | 20.0 | 11940 | 1.1053 | 0.8794 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding30model | [
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sergeipetrov/diarization-inference-handler | sergeipetrov | 2023-11-29T15:25:23Z | 0 | 0 | null | [
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:25:23Z | 2023-11-28T18:35:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sergeipetrov/diarization-inference-handler | [
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Realgon/distilbert_twitterfin_padding40model | Realgon | 2023-11-29T10:27:30Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:27:30Z | 2023-11-28T18:36:20.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding40model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# distilbert_twitterfin_padding40model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1281
- Accuracy: 0.8744
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6696 | 1.0 | 597 | 0.5204 | 0.8049 |
| 0.3995 | 2.0 | 1194 | 0.3527 | 0.8723 |
| 0.2854 | 3.0 | 1791 | 0.3942 | 0.8756 |
| 0.1921 | 4.0 | 2388 | 0.5487 | 0.8773 |
| 0.1519 | 5.0 | 2985 | 0.6299 | 0.8735 |
| 0.0632 | 6.0 | 3582 | 0.6852 | 0.8735 |
| 0.0498 | 7.0 | 4179 | 0.7728 | 0.8702 |
| 0.0446 | 8.0 | 4776 | 0.8638 | 0.8681 |
| 0.0321 | 9.0 | 5373 | 0.8343 | 0.8765 |
| 0.0339 | 10.0 | 5970 | 0.8881 | 0.8790 |
| 0.0194 | 11.0 | 6567 | 0.9281 | 0.8752 |
| 0.0106 | 12.0 | 7164 | 0.9799 | 0.8723 |
| 0.0145 | 13.0 | 7761 | 1.0008 | 0.8752 |
| 0.0121 | 14.0 | 8358 | 1.0437 | 0.8756 |
| 0.009 | 15.0 | 8955 | 1.0609 | 0.8748 |
| 0.0062 | 16.0 | 9552 | 1.0912 | 0.8719 |
| 0.0068 | 17.0 | 10149 | 1.1045 | 0.8731 |
| 0.0046 | 18.0 | 10746 | 1.1086 | 0.8735 |
| 0.0056 | 19.0 | 11343 | 1.1271 | 0.8723 |
| 0.0029 | 20.0 | 11940 | 1.1281 | 0.8744 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding40model | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_27 | Jeevesh8 | 2023-11-29T02:54:31Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T02:54:31Z | 2023-11-28T18:36:39.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_27 | [
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Realgon/distilbert_twitterfin_padding50model | Realgon | 2023-11-29T10:39:22Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:39:22Z | 2023-11-28T18:36:50.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding50model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding50model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1611
- Accuracy: 0.8731
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6399 | 1.0 | 597 | 0.4406 | 0.8308 |
| 0.3771 | 2.0 | 1194 | 0.3532 | 0.8710 |
| 0.259 | 3.0 | 1791 | 0.4253 | 0.8752 |
| 0.1749 | 4.0 | 2388 | 0.5259 | 0.8677 |
| 0.1332 | 5.0 | 2985 | 0.6621 | 0.8719 |
| 0.0625 | 6.0 | 3582 | 0.6391 | 0.8756 |
| 0.0514 | 7.0 | 4179 | 0.8550 | 0.8656 |
| 0.044 | 8.0 | 4776 | 0.8022 | 0.8744 |
| 0.0324 | 9.0 | 5373 | 0.8844 | 0.8740 |
| 0.0267 | 10.0 | 5970 | 0.8932 | 0.8735 |
| 0.0153 | 11.0 | 6567 | 0.9704 | 0.8748 |
| 0.0143 | 12.0 | 7164 | 1.0869 | 0.8518 |
| 0.0143 | 13.0 | 7761 | 1.0678 | 0.8698 |
| 0.0122 | 14.0 | 8358 | 1.0570 | 0.8698 |
| 0.0119 | 15.0 | 8955 | 1.0582 | 0.8731 |
| 0.0058 | 16.0 | 9552 | 1.1039 | 0.8668 |
| 0.0043 | 17.0 | 10149 | 1.1211 | 0.8744 |
| 0.0024 | 18.0 | 10746 | 1.1487 | 0.8685 |
| 0.0034 | 19.0 | 11343 | 1.1457 | 0.8719 |
| 0.0015 | 20.0 | 11940 | 1.1611 | 0.8731 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding50model | [
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Realgon/distilbert_twitterfin_padding60model | Realgon | 2023-11-29T10:51:55Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:51:55Z | 2023-11-28T18:37:21.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding60model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding60model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2102
- Accuracy: 0.8673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6858 | 1.0 | 597 | 0.5056 | 0.8086 |
| 0.4365 | 2.0 | 1194 | 0.3822 | 0.8606 |
| 0.3156 | 3.0 | 1791 | 0.4330 | 0.8626 |
| 0.228 | 4.0 | 2388 | 0.5520 | 0.8710 |
| 0.1729 | 5.0 | 2985 | 0.6801 | 0.8434 |
| 0.0844 | 6.0 | 3582 | 0.6752 | 0.8626 |
| 0.0788 | 7.0 | 4179 | 0.7749 | 0.8631 |
| 0.0598 | 8.0 | 4776 | 0.8199 | 0.8572 |
| 0.0544 | 9.0 | 5373 | 0.9913 | 0.8543 |
| 0.0422 | 10.0 | 5970 | 1.0151 | 0.8626 |
| 0.0273 | 11.0 | 6567 | 0.9725 | 0.8597 |
| 0.0235 | 12.0 | 7164 | 1.0349 | 0.8668 |
| 0.0166 | 13.0 | 7761 | 1.1150 | 0.8656 |
| 0.0131 | 14.0 | 8358 | 1.1363 | 0.8710 |
| 0.0108 | 15.0 | 8955 | 1.1903 | 0.8543 |
| 0.0079 | 16.0 | 9552 | 1.1553 | 0.8652 |
| 0.0112 | 17.0 | 10149 | 1.1944 | 0.8652 |
| 0.0035 | 18.0 | 10746 | 1.2227 | 0.8681 |
| 0.0064 | 19.0 | 11343 | 1.2064 | 0.8677 |
| 0.0033 | 20.0 | 11940 | 1.2102 | 0.8673 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding60model | [
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Realgon/distilbert_twitterfin_padding70model | Realgon | 2023-11-29T11:05:02Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:05:02Z | 2023-11-28T18:37:53.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding70model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding70model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0730
- Accuracy: 0.8765
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6887 | 1.0 | 597 | 0.4586 | 0.8262 |
| 0.4254 | 2.0 | 1194 | 0.3631 | 0.8647 |
| 0.3042 | 3.0 | 1791 | 0.3923 | 0.8769 |
| 0.2115 | 4.0 | 2388 | 0.5038 | 0.8693 |
| 0.1643 | 5.0 | 2985 | 0.5552 | 0.8798 |
| 0.0827 | 6.0 | 3582 | 0.6608 | 0.8735 |
| 0.0656 | 7.0 | 4179 | 0.7668 | 0.8660 |
| 0.0523 | 8.0 | 4776 | 0.7806 | 0.8685 |
| 0.0474 | 9.0 | 5373 | 0.8615 | 0.8668 |
| 0.031 | 10.0 | 5970 | 0.9038 | 0.8714 |
| 0.0232 | 11.0 | 6567 | 0.9269 | 0.8693 |
| 0.0195 | 12.0 | 7164 | 0.9192 | 0.8723 |
| 0.0275 | 13.0 | 7761 | 0.9875 | 0.8685 |
| 0.0171 | 14.0 | 8358 | 1.0308 | 0.8714 |
| 0.0129 | 15.0 | 8955 | 1.0227 | 0.8744 |
| 0.0052 | 16.0 | 9552 | 1.0471 | 0.8685 |
| 0.0076 | 17.0 | 10149 | 1.0448 | 0.8769 |
| 0.0064 | 18.0 | 10746 | 1.0537 | 0.8769 |
| 0.0078 | 19.0 | 11343 | 1.0615 | 0.8744 |
| 0.0034 | 20.0 | 11940 | 1.0730 | 0.8765 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding70model | [
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Realgon/distilbert_twitterfin_padding80model | Realgon | 2023-11-29T11:18:31Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:18:31Z | 2023-11-28T18:38:25.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding80model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding80model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2464
- Accuracy: 0.8643
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6875 | 1.0 | 597 | 0.5594 | 0.7714 |
| 0.4358 | 2.0 | 1194 | 0.4065 | 0.8606 |
| 0.3212 | 3.0 | 1791 | 0.4008 | 0.8652 |
| 0.2291 | 4.0 | 2388 | 0.5113 | 0.8589 |
| 0.1828 | 5.0 | 2985 | 0.6224 | 0.8526 |
| 0.0895 | 6.0 | 3582 | 0.7556 | 0.8417 |
| 0.0787 | 7.0 | 4179 | 0.8308 | 0.8539 |
| 0.0562 | 8.0 | 4776 | 0.8072 | 0.8660 |
| 0.0622 | 9.0 | 5373 | 0.8961 | 0.8564 |
| 0.0319 | 10.0 | 5970 | 0.9517 | 0.8626 |
| 0.0301 | 11.0 | 6567 | 1.0274 | 0.8610 |
| 0.0196 | 12.0 | 7164 | 1.0598 | 0.8606 |
| 0.0224 | 13.0 | 7761 | 1.1347 | 0.8673 |
| 0.0164 | 14.0 | 8358 | 1.1203 | 0.8664 |
| 0.0113 | 15.0 | 8955 | 1.1585 | 0.8647 |
| 0.007 | 16.0 | 9552 | 1.2019 | 0.8589 |
| 0.0082 | 17.0 | 10149 | 1.2218 | 0.8656 |
| 0.0043 | 18.0 | 10746 | 1.2438 | 0.8635 |
| 0.0047 | 19.0 | 11343 | 1.2392 | 0.8656 |
| 0.0031 | 20.0 | 11940 | 1.2464 | 0.8643 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding80model | [
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Realgon/distilbert_twitterfin_padding90model | Realgon | 2023-11-29T11:32:34Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:32:34Z | 2023-11-28T18:38:57.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding90model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding90model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1388
- Accuracy: 0.8714
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6718 | 1.0 | 597 | 0.4965 | 0.8007 |
| 0.4114 | 2.0 | 1194 | 0.3593 | 0.8760 |
| 0.3053 | 3.0 | 1791 | 0.3970 | 0.8769 |
| 0.2064 | 4.0 | 2388 | 0.5193 | 0.8714 |
| 0.1666 | 5.0 | 2985 | 0.6195 | 0.8593 |
| 0.0775 | 6.0 | 3582 | 0.6972 | 0.8756 |
| 0.0734 | 7.0 | 4179 | 0.7703 | 0.8685 |
| 0.0555 | 8.0 | 4776 | 0.8217 | 0.8635 |
| 0.0394 | 9.0 | 5373 | 0.8715 | 0.8693 |
| 0.0321 | 10.0 | 5970 | 0.9257 | 0.8702 |
| 0.0199 | 11.0 | 6567 | 0.9315 | 0.8631 |
| 0.0159 | 12.0 | 7164 | 0.9733 | 0.8685 |
| 0.0155 | 13.0 | 7761 | 1.0329 | 0.8727 |
| 0.0099 | 14.0 | 8358 | 1.0347 | 0.8748 |
| 0.0113 | 15.0 | 8955 | 1.0812 | 0.8702 |
| 0.0056 | 16.0 | 9552 | 1.0760 | 0.8752 |
| 0.0077 | 17.0 | 10149 | 1.1068 | 0.8710 |
| 0.0024 | 18.0 | 10746 | 1.1203 | 0.8731 |
| 0.0042 | 19.0 | 11343 | 1.1445 | 0.8731 |
| 0.0025 | 20.0 | 11940 | 1.1388 | 0.8714 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding90model | [
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Realgon/distilbert_twitterfin_padding100model | Realgon | 2023-11-29T11:47:17Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:47:17Z | 2023-11-28T18:39:32.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert_twitterfin_padding100model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_twitterfin_padding100model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0528
- Accuracy: 0.8832
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.6909 | 1.0 | 597 | 0.4072 | 0.8576 |
| 0.3923 | 2.0 | 1194 | 0.3465 | 0.8765 |
| 0.2852 | 3.0 | 1791 | 0.3862 | 0.8786 |
| 0.1967 | 4.0 | 2388 | 0.4676 | 0.8794 |
| 0.1543 | 5.0 | 2985 | 0.5885 | 0.8660 |
| 0.0729 | 6.0 | 3582 | 0.6574 | 0.8681 |
| 0.0611 | 7.0 | 4179 | 0.7198 | 0.8756 |
| 0.0503 | 8.0 | 4776 | 0.7859 | 0.8656 |
| 0.0498 | 9.0 | 5373 | 0.7831 | 0.8786 |
| 0.0352 | 10.0 | 5970 | 0.8445 | 0.8740 |
| 0.0223 | 11.0 | 6567 | 0.8618 | 0.8748 |
| 0.0179 | 12.0 | 7164 | 0.8994 | 0.8815 |
| 0.0152 | 13.0 | 7761 | 0.9486 | 0.8802 |
| 0.0124 | 14.0 | 8358 | 0.9630 | 0.8823 |
| 0.009 | 15.0 | 8955 | 0.9582 | 0.8769 |
| 0.0063 | 16.0 | 9552 | 0.9968 | 0.8823 |
| 0.0034 | 17.0 | 10149 | 1.0137 | 0.8832 |
| 0.0029 | 18.0 | 10746 | 1.0714 | 0.8802 |
| 0.005 | 19.0 | 11343 | 1.0397 | 0.8827 |
| 0.0027 | 20.0 | 11940 | 1.0528 | 0.8832 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_twitterfin_padding100model | [
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Realgon/distilbert_agnews_padding0model | Realgon | 2023-11-29T13:56:42Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:56:42Z | 2023-11-28T18:40:08.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding0model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9464473684210526
---
<!-- This model card 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_agnews_padding0model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6408
- Accuracy: 0.9464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1774 | 1.0 | 7500 | 0.1995 | 0.9392 |
| 0.1403 | 2.0 | 15000 | 0.1939 | 0.9447 |
| 0.1114 | 3.0 | 22500 | 0.2186 | 0.9459 |
| 0.0741 | 4.0 | 30000 | 0.2832 | 0.9446 |
| 0.0499 | 5.0 | 37500 | 0.3070 | 0.9408 |
| 0.0376 | 6.0 | 45000 | 0.3704 | 0.9434 |
| 0.0341 | 7.0 | 52500 | 0.3999 | 0.9426 |
| 0.0319 | 8.0 | 60000 | 0.4505 | 0.9425 |
| 0.0191 | 9.0 | 67500 | 0.4649 | 0.9399 |
| 0.013 | 10.0 | 75000 | 0.5064 | 0.9403 |
| 0.0184 | 11.0 | 82500 | 0.4858 | 0.9405 |
| 0.0081 | 12.0 | 90000 | 0.5358 | 0.9432 |
| 0.0065 | 13.0 | 97500 | 0.5440 | 0.9436 |
| 0.0053 | 14.0 | 105000 | 0.5755 | 0.9436 |
| 0.0017 | 15.0 | 112500 | 0.5907 | 0.9457 |
| 0.0042 | 16.0 | 120000 | 0.5916 | 0.9455 |
| 0.0031 | 17.0 | 127500 | 0.5976 | 0.9468 |
| 0.0017 | 18.0 | 135000 | 0.6063 | 0.9474 |
| 0.0003 | 19.0 | 142500 | 0.6248 | 0.9467 |
| 0.0003 | 20.0 | 150000 | 0.6408 | 0.9464 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_agnews_padding0model | [
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Realgon/distilbert_agnews_padding10model | Realgon | 2023-11-29T16:16:27Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:16:27Z | 2023-11-28T18:41:18.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding10model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9444736842105264
---
<!-- This model card 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_agnews_padding10model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6613
- Accuracy: 0.9445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1788 | 1.0 | 7500 | 0.1962 | 0.9405 |
| 0.1396 | 2.0 | 15000 | 0.1946 | 0.9446 |
| 0.1159 | 3.0 | 22500 | 0.2336 | 0.9433 |
| 0.0802 | 4.0 | 30000 | 0.2599 | 0.9437 |
| 0.055 | 5.0 | 37500 | 0.3196 | 0.9432 |
| 0.0408 | 6.0 | 45000 | 0.4017 | 0.9434 |
| 0.0338 | 7.0 | 52500 | 0.4113 | 0.9412 |
| 0.0258 | 8.0 | 60000 | 0.4533 | 0.9416 |
| 0.0159 | 9.0 | 67500 | 0.4573 | 0.9442 |
| 0.0154 | 10.0 | 75000 | 0.4980 | 0.9420 |
| 0.0171 | 11.0 | 82500 | 0.4935 | 0.9420 |
| 0.0105 | 12.0 | 90000 | 0.5304 | 0.9399 |
| 0.0079 | 13.0 | 97500 | 0.5437 | 0.9439 |
| 0.0061 | 14.0 | 105000 | 0.5889 | 0.9429 |
| 0.0056 | 15.0 | 112500 | 0.5444 | 0.9426 |
| 0.0059 | 16.0 | 120000 | 0.6274 | 0.9429 |
| 0.0004 | 17.0 | 127500 | 0.6264 | 0.9428 |
| 0.0014 | 18.0 | 135000 | 0.6138 | 0.9441 |
| 0.0002 | 19.0 | 142500 | 0.6431 | 0.9447 |
| 0.0005 | 20.0 | 150000 | 0.6613 | 0.9445 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
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Realgon/distilbert_agnews_padding20model | Realgon | 2023-11-29T18:43:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:43:04Z | 2023-11-28T18:42:33.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding20model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9452631578947368
---
<!-- This model card 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_agnews_padding20model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6477
- Accuracy: 0.9453
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.18 | 1.0 | 7500 | 0.1831 | 0.9426 |
| 0.1365 | 2.0 | 15000 | 0.2039 | 0.9420 |
| 0.1176 | 3.0 | 22500 | 0.2202 | 0.9470 |
| 0.0899 | 4.0 | 30000 | 0.2601 | 0.9443 |
| 0.0547 | 5.0 | 37500 | 0.2919 | 0.9429 |
| 0.0387 | 6.0 | 45000 | 0.3618 | 0.9459 |
| 0.0351 | 7.0 | 52500 | 0.4129 | 0.9413 |
| 0.031 | 8.0 | 60000 | 0.4379 | 0.9436 |
| 0.0171 | 9.0 | 67500 | 0.4794 | 0.9429 |
| 0.0156 | 10.0 | 75000 | 0.4744 | 0.9438 |
| 0.0147 | 11.0 | 82500 | 0.4832 | 0.9457 |
| 0.0108 | 12.0 | 90000 | 0.5166 | 0.9447 |
| 0.0034 | 13.0 | 97500 | 0.5083 | 0.9459 |
| 0.0065 | 14.0 | 105000 | 0.5451 | 0.9446 |
| 0.0062 | 15.0 | 112500 | 0.5926 | 0.9443 |
| 0.0031 | 16.0 | 120000 | 0.6059 | 0.9433 |
| 0.001 | 17.0 | 127500 | 0.6312 | 0.9463 |
| 0.0004 | 18.0 | 135000 | 0.6197 | 0.9454 |
| 0.0004 | 19.0 | 142500 | 0.6472 | 0.9455 |
| 0.0002 | 20.0 | 150000 | 0.6477 | 0.9453 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_agnews_padding20model | [
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Realgon/distilbert_agnews_padding30model | Realgon | 2023-11-29T21:12:50Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:12:50Z | 2023-11-28T18:43:45.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding30model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9426315789473684
---
<!-- This model card 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_agnews_padding30model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6664
- Accuracy: 0.9426
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1798 | 1.0 | 7500 | 0.1996 | 0.9397 |
| 0.1377 | 2.0 | 15000 | 0.1984 | 0.9432 |
| 0.1168 | 3.0 | 22500 | 0.2269 | 0.9429 |
| 0.0831 | 4.0 | 30000 | 0.2763 | 0.9411 |
| 0.0581 | 5.0 | 37500 | 0.2916 | 0.9428 |
| 0.0422 | 6.0 | 45000 | 0.3627 | 0.9424 |
| 0.0296 | 7.0 | 52500 | 0.4506 | 0.94 |
| 0.0315 | 8.0 | 60000 | 0.4401 | 0.9417 |
| 0.0224 | 9.0 | 67500 | 0.4668 | 0.9426 |
| 0.0151 | 10.0 | 75000 | 0.5095 | 0.9416 |
| 0.0202 | 11.0 | 82500 | 0.5013 | 0.9437 |
| 0.009 | 12.0 | 90000 | 0.5612 | 0.9441 |
| 0.0105 | 13.0 | 97500 | 0.5372 | 0.9432 |
| 0.0105 | 14.0 | 105000 | 0.5760 | 0.9409 |
| 0.0044 | 15.0 | 112500 | 0.5765 | 0.9417 |
| 0.0029 | 16.0 | 120000 | 0.6285 | 0.9432 |
| 0.002 | 17.0 | 127500 | 0.6484 | 0.9416 |
| 0.0006 | 18.0 | 135000 | 0.6568 | 0.9428 |
| 0.0002 | 19.0 | 142500 | 0.6537 | 0.9437 |
| 0.0004 | 20.0 | 150000 | 0.6664 | 0.9426 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_agnews_padding30model | [
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Realgon/distilbert_agnews_padding40model | Realgon | 2023-11-29T23:49:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:49:04Z | 2023-11-28T18:45:01.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
metrics:
- accuracy
model-index:
- name: distilbert_agnews_padding40model
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9468421052631579
---
<!-- This model card 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_agnews_padding40model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6171
- Accuracy: 0.9468
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:--------:|
| 0.1804 | 1.0 | 7500 | 0.1955 | 0.9395 |
| 0.1406 | 2.0 | 15000 | 0.1970 | 0.9429 |
| 0.1206 | 3.0 | 22500 | 0.2108 | 0.9467 |
| 0.0878 | 4.0 | 30000 | 0.2626 | 0.9429 |
| 0.0605 | 5.0 | 37500 | 0.3047 | 0.9417 |
| 0.0472 | 6.0 | 45000 | 0.3698 | 0.9397 |
| 0.0331 | 7.0 | 52500 | 0.4269 | 0.9367 |
| 0.0251 | 8.0 | 60000 | 0.4326 | 0.9416 |
| 0.0247 | 9.0 | 67500 | 0.4525 | 0.9428 |
| 0.0151 | 10.0 | 75000 | 0.4580 | 0.9462 |
| 0.0164 | 11.0 | 82500 | 0.5027 | 0.9455 |
| 0.0074 | 12.0 | 90000 | 0.5040 | 0.9437 |
| 0.0054 | 13.0 | 97500 | 0.5347 | 0.9449 |
| 0.0031 | 14.0 | 105000 | 0.5753 | 0.9451 |
| 0.0065 | 15.0 | 112500 | 0.5445 | 0.9453 |
| 0.0012 | 16.0 | 120000 | 0.5966 | 0.9461 |
| 0.0028 | 17.0 | 127500 | 0.5994 | 0.9445 |
| 0.0006 | 18.0 | 135000 | 0.5948 | 0.9455 |
| 0.0002 | 19.0 | 142500 | 0.6115 | 0.9471 |
| 0.0008 | 20.0 | 150000 | 0.6171 | 0.9468 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/distilbert_agnews_padding40model | [
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Realgon/distilbert_agnews_padding50model | Realgon | 2023-11-30T01:27:27Z | 0 | 0 | null | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:ag_news",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:27:27Z | 2023-11-28T18:46:22.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- ag_news
model-index:
- name: distilbert_agnews_padding50model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert_agnews_padding50model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the ag_news dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 0.01
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.01 | 75 | 0.7297 | 0.8401 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
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DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.05 | DichenLi | 2023-11-29T03:50:27Z | 0 | 0 | null | [
"tensorboard",
"region:us"
] | 2023-11-29T03:50:27Z | 2023-11-28T18:51:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | DichenLi/llama2_lora_r32_lora_alpha16_lora_dropout0.05 | [
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bomdey/plAInlang | bomdey | 2023-11-29T20:42:21Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T20:42:21Z | 2023-11-28T19:11:05.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | bomdey/plAInlang | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_28 | Jeevesh8 | 2023-11-29T03:25:14Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:25:14Z | 2023-11-28T19:12:42.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_28 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_29 | Jeevesh8 | 2023-11-29T03:39:17Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:39:17Z | 2023-11-28T19:12:43.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_29 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_31 | Jeevesh8 | 2023-11-29T03:34:36Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:34:36Z | 2023-11-28T19:14:39.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_31 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_30 | Jeevesh8 | 2023-11-29T03:25:40Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:25:40Z | 2023-11-28T19:14:40.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_30 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_32 | Jeevesh8 | 2023-11-29T03:43:36Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:43:36Z | 2023-11-28T19:19:25.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_32 | [
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kyle0518/HW3_bonus | kyle0518 | 2023-11-29T12:45:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T12:45:57Z | 2023-11-28T19:37:18.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | kyle0518/HW3_bonus | [
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ersdd/feizao | ersdd | 2023-11-29T13:54:55Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T13:54:55Z | 2023-11-28T19:57:41.000Z | null | null | ---
license: apache-2.0
---
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Jeevesh8/multibert_seed0_0k_qqp_ft_33 | Jeevesh8 | 2023-11-29T04:36:02Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:36:02Z | 2023-11-28T20:01:06.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_33 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_34 | Jeevesh8 | 2023-11-29T04:46:55Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:46:55Z | 2023-11-28T20:03:09.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_34 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_35 | Jeevesh8 | 2023-11-29T03:46:25Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:46:25Z | 2023-11-28T20:07:28.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_35 | [
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Jeevesh8/multibert_seed0_0k_qqp_ft_36 | Jeevesh8 | 2023-11-29T04:58:41Z | 0 | 0 | null | [
"transformers",
"jax",
"tensorboard",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:58:41Z | 2023-11-28T20:11:56.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Jeevesh8/multibert_seed0_0k_qqp_ft_36 | [
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