modelId
stringlengths 5
122
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC] | downloads
int64 0
738M
| likes
int64 0
11k
| library_name
stringclasses 245
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 48
values | createdAt
timestamp[us, tz=UTC] | card
stringlengths 1
901k
|
|---|---|---|---|---|---|---|---|---|---|
Meziane/qwuestion_answering_T5_policy_qa_4 | Meziane | 2024-07-01T13:30:18Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:30:18Z | Entry not found |
Edgar404/donut-shivi-cheques_best_320_test | Edgar404 | 2024-07-02T04:04:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:30:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mjm4dl/slot_filling_only_generated_llama3.csv | mjm4dl | 2024-07-01T13:34:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:30:55Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aivatech/ai_friend | aivatech | 2024-07-01T13:40:18Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:31:06Z | This project is created for making ai friend with using ollama gemma2 and elevenlabs
|
Moriacrafter/Qwen1.5-4B-8bit_DepressionDetection | Moriacrafter | 2024-07-01T13:34:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:31:20Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
nowsyn/anycontrol | nowsyn | 2024-07-01T14:43:58Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T13:31:37Z | ---
license: apache-2.0
---
# AnyControl: Create Your Artwork with Versatile Control on Text-to-Image Generation
[Yanan Sun](https://scholar.google.com/citations?user=6TA1oPkAAAAJ&hl=en), Yanchen Liu, Yinhao Tang, [Wenjie Pei](https://wenjiepei.github.io/) and [Kai Chen*](https://chenkai.site/)
**Shanghai AI Laboratory**
## Overview
The field of text-to-image (T2I) generation has made significant progress in recent years,
largely driven by advancements in diffusion models.
Linguistic control enables effective content creation, but struggles with fine-grained control over image generation.
This challenge has been explored, to a great extent, by incorporating additional usersupplied spatial conditions,
such as depth maps and edge maps, into pre-trained T2I models through extra encoding.
However, multi-control image synthesis still faces several challenges.
Specifically, current approaches are limited in handling free combinations of diverse input control signals,
overlook the complex relationships among multiple spatial conditions, and often fail to maintain semantic alignment with provided textual prompts.
This can lead to suboptimal user experiences. To address these challenges, we propose AnyControl,
a multi-control image synthesis framework that supports arbitrary combinations of diverse control signals.
AnyControl develops a novel Multi-Control Encoder that extracts a unified multi-modal embedding to guide the generation process.
This approach enables a holistic understanding of user inputs, and produces high-quality,
faithful results under versatile control signals, as demonstrated by extensive quantitative and qualitative evaluations.
## Model Card
AnyControl for SD 1.5
- `ckpts/anycontrol_15.ckpt`: weights for AnyControl.
- `ckpts/init_local.ckpt`: initial weights of AnyControl during training, generated following [Uni-ControlNet](https://github.com/ShihaoZhaoZSH/Uni-ControlNet).
- `ckpts/blip2_pretrained.pth`: third-party model.
- `annotator/ckpts`: third-party models used in annotators.
## License and Citation
All models and assets are under the [Apache 2.0 license](./LICENSE) unless specified otherwise.
If this work is helpful for your research, please consider citing the following BibTeX entry.
``` bibtex
@misc{sun2024anycontrol,
title={AnyControl: Create your artwork with versatile control on text-to-image generation},
author={Sun, Yanan and Liu, Yanchen and Tang, Yinhao and Pei, Wenjie and Chen, Kai},
booktitle={ECCV},
year={2024}
}
``` |
NikiSP/results | NikiSP | 2024-07-01T13:32:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T13:31:48Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- accuracy
- f1
model-index:
- name: results
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. -->
# results
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: 2.0751
- Precision: 0.5648
- Recall: 0.5655
- Accuracy: 0.5655
- F1: 0.5649
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:------:|
| 1.1943 | 1.0 | 367 | 1.1549 | 0.5341 | 0.5155 | 0.5155 | 0.5161 |
| 0.9064 | 2.0 | 734 | 1.1347 | 0.5568 | 0.5608 | 0.5608 | 0.5528 |
| 0.512 | 3.0 | 1101 | 1.4481 | 0.5636 | 0.5330 | 0.5330 | 0.5261 |
| 0.228 | 4.0 | 1468 | 1.7226 | 0.5633 | 0.5608 | 0.5608 | 0.5588 |
| 0.1355 | 5.0 | 1835 | 2.0751 | 0.5648 | 0.5655 | 0.5655 | 0.5649 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Tokenizers 0.19.1
|
NikiSP/Movie_Genre_Classifier | NikiSP | 2024-07-01T13:32:11Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:31:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Addax-Data-Science/Iran_v1 | Addax-Data-Science | 2024-07-01T13:35:32Z | 0 | 0 | null | [
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2024-07-01T13:32:01Z | ---
license: cc-by-nc-sa-4.0
---
Model to identify 14 species or higher-level taxons present in Iran. The model was trained on a set of approximately 1 million camera trap images. The model has an overall validation accuracy, precision, and recall of 95%, 93%, and 94%, respectively. The accuracy was not tested on an out-of-sample-dataset since local images were absent. The model was designed to expedite the monitoring of the Iranian Cheetah Society. |
Franzin/bigbird-roberta-base-goemotions-ekman-multiclass | Franzin | 2024-07-01T13:32:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"big_bird",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T13:32:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
OnFinanceAI/setup__llama_instr_ft | OnFinanceAI | 2024-07-01T13:33:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:32:42Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Torazo5/marian-finetuned-kde4-en-to-fr | Torazo5 | 2024-07-01T13:33:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:33:48Z | Entry not found |
hcy5561/tapas-base-finetuned-sqa-model | hcy5561 | 2024-07-01T13:33:53Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:33:53Z | Entry not found |
trustlelab/bald-hair-classification | trustlelab | 2024-07-01T13:40:19Z | 0 | 0 | keras | [
"keras",
"image-classification",
"en",
"region:us"
] | image-classification | 2024-07-01T13:34:01Z | ---
language:
- en
library_name: keras
pipeline_tag: image-classification
--- |
itay-nakash/model_42d9b05c5c_sweep_super-gorge-1156 | itay-nakash | 2024-07-01T13:34:51Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:34:51Z | Entry not found |
Vinay-96/LLama2_Finetuned_jeopardy_QnA | Vinay-96 | 2024-07-01T13:52:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | 2024-07-01T13:35:06Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sara-m98/ECO_BERT-BILSTM_FINAL | sara-m98 | 2024-07-02T07:05:49Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-07-01T13:35:39Z | och Training Loss Validation Loss Precision Recall F1 Accuracy
1 No log 0.066976 0.387250 0.329733 0.356184 0.984486
2 0.106500 0.058111 0.395311 0.391574 0.393434 0.985573
3 0.106500 0.058637 0.393105 0.458817 0.423427 0.985601
4 0.028900 0.065462 0.396026 0.462868 0.426846 0.984953
5 0.028900 0.070476 0.442568 0.467189 0.454545 0.985966
6 0.015500 0.069267 0.425043 0.469349 0.446099 0.986287
7 0.015500 0.078341 0.451092 0.490683 0.470056 0.986064
8 0.009200 0.085571 0.431022 0.481772 0.454986 0.985717
9 0.009200 0.087308 0.442126 0.480691 0.460603 0.985725
10 0.005900 0.092452 0.463224 0.479611 0.471275 0.986054
11 0.005900 0.092327 0.437395 0.493384 0.463706 0.985844
12 0.004300 0.100381 0.452416 0.495544 0.472999 0.986165
13 0.004300 0.092396 0.446150 0.486632 0.465513 0.986190
14 0.003100 0.096234 0.467906 0.496084 0.481583 0.986704
15 0.003100 0.102940 0.452968 0.486362 0.469071 0.986141
16 0.002500 0.101856 0.464897 0.491763 0.477953 0.986278
17 0.002500 0.105866 0.456962 0.487443 0.471710 0.986066
18 0.002200 0.106126 0.474086 0.486632 0.480277 0.986332
19 0.002200 0.107025 0.462751 0.491493 0.476689 0.986511
20 0.001700 0.106984 0.469367 0.494464 0.481589 0.986270
21 0.001700 0.106669 0.474589 0.506886 0.490206 0.986484
22 0.001500 0.111114 0.471334 0.501755 0.486069 0.986311
23 0.001500 0.112432 0.460683 0.492033 0.475842 0.986464
24 0.001300 0.110102 0.475635 0.506076 0.490383 0.986519
25 0.001300 0.116960 0.467344 0.496624 0.481540 0.986453
26 0.001200 0.118941 0.469414 0.495274 0.481997 0.986373
27 0.001200 0.120318 0.476589 0.492033 0.484188 0.986326
28 0.001100 0.120894 0.481053 0.493654 0.487272 0.986472
29 0.001100 0.122397 0.481988 0.495004 0.488409 0.986480
30 0.000900 0.120894 0.474607 0.497164 0.485624 0.986445
31 0.000900 0.121767 0.475380 0.497975 0.486415 0.986499
32 0.000900 0.121362 0.476471 0.503106 0.489426 0.986528
|
Grayx/john_paul_van_damme_62 | Grayx | 2024-07-01T13:36:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:35:58Z | Entry not found |
Adi-0-0-Gupta/Embedding-v2-64 | Adi-0-0-Gupta | 2024-07-01T13:36:17Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:75086",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-01T13:36:14Z | ---
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:75086
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Tag: Vegetable Pulao
Preparation time (ingredient) for Vegetable Pulao based on different serving sizes:
Serving 1 - 15 mins, Serving 2 - 18 mins, Serving 3 - 20 mins, Serving 4 - 22
mins'
sentences:
- What diet tags are relevant to Kimchi Fried Rice (Chicken)?
- What dietary classifications are suitable for Chicken & Broccoli Alfredo?
- What is the time required to prepare ingredients for Vegetable Pulao?
- source_sentence: "Tag: Vegetable Pulao\n\nMacro ingredients required to cook Vegetable\
\ Pulao:\nOrange Carrot, French Bean, Cauliflower, Plain Unsweetened Yogurt, Red\
\ Onion, Clove, Bay Leaf, Green Cardamom, Ginger-Garlic Paste, Green Chili Pepper,\
\ Cinnamon, Basmati Rice, Fresh Cilantro, Fresh Mint\n\nPreparations (ingredient)\
\ needed to cook Vegetable Pulao:\nWash the rice twice, and then soak it for at\
\ least 20 minutes. Drain the water and transfer the rice into the macro container.\
\ \nMix the yogurt, whole spices, green chili, and ginger garlic paste with the\
\ chopped veggies in a separate bowl, and then transfer it to the macro container.\
\ Please make sure to use plain yogurt. If using greek yogurt, use half the quantity\
\ of plain yogurt.\n\nTotal calories (nutritional energy) in Vegetable Pulao based\
\ on different serving sizes: Serving 1 - 300 mins, Serving 2 - 500 mins, Serving\
\ 3 - 700 mins, Serving 4 - 900 mins"
sentences:
- Can you give me some insights into Scrambled Eggs with Veggies?
- How should the ingredients for Chicken Pad Thai be prepared?
- Whatβs the calorie figure for Vegetable Pulao?
- source_sentence: 'Tag: Chicken Pad Thai
Spatula required to cook chicken pad thai based on different serving sizes: Serving
1 - noodle spatula, Serving 2 - noodle spatula, Serving 3 - noodle spatula, Serving
4 - noodle spatula'
sentences:
- What are the detailed cooking instructions for Rava Upma?
- Whatβs the best way to prep ingredients for Teriyaki Tofu?
- What kind of spatula do you need for Chicken Pad Thai?
- source_sentence: 'Tag: Kimchi Fried Rice (Chicken)
A small description of Kimchi Fried Rice (Chicken): Kimchi fried rice is made
with kimchi, spicy gochujang, and garlic. The umami flavors from the kimchi juice
balance beautifully with the spicy gochujang sauce and soy sauce, also creating
that beautiful red-tinted color. '
sentences:
- What spatula would you recommend for Vegetable Pulao?
- How can I improve the presentation of Chicken Pad Thai with garnishes?
- How would you describe the dish Kimchi Fried Rice (Chicken)?
- source_sentence: 'Tag: Rava Upma
Cook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins,
Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins
Preparation time (ingredient) for Rava Upma based on different serving sizes:
Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins'
sentences:
- How long does it take to prepare ingredients for Rava Upma?
- What are some final touch tips for Rava Upma?
- How would you summarize Mac & Cheese?
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.9431019051272216
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9684183608234241
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9704641350210971
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9431019051272216
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8694540340109961
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8533691343817927
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.795371435877765
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15481705915468094
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24073986604144076
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32643031270609696
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5043459566396565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.918622245854727
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9581816761141663
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7389009694677152
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.9448919575501854
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9704641350210971
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.97250990921877
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9942462600690449
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9448919575501854
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8702211993351234
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8534458509142053
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.796407109065337
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15493928650775676
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24095543792672336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32656065320980354
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5047541251867395
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9196805870274929
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9598828347773509
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7369163574373101
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.9487277841708222
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9727656309934791
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9758342922899885
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9941183991816903
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9487277841708222
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8725653156032902
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8555427694668201
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7964454673315434
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15515249707637835
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24121211958393993
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3269542903263234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5049379410293565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9205498440081438
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9627833488592988
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7339144971303314
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.949622810382304
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9744278225290883
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9777522056003068
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.949622810382304
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8755061160124451
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8594553126198696
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7982610919319781
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15511449448310274
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2414942027072444
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32761337610101815
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5053322703457185
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9226639618734229
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9639710344351698
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7303591002200212
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 32
type: dim_32
metrics:
- type: cosine_accuracy@1
value: 0.9451476793248945
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9731492136555427
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9783915100370797
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9947577036184632
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9451476793248945
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8679623236585261
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8521416698631887
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7814473852448537
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15477631695655358
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2399798683039478
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3251298048319623
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49721034132531955
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9089797806768267
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9616667478481831
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.708556549543554
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the π€ Hub
model = SentenceTransformer("Adi-0-0-Gupta/Embedding-v2-64")
# Run inference
sentences = [
'Tag: Rava Upma\n\nCook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins, Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins\n\nPreparation time (ingredient) for Rava Upma based on different serving sizes: Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins',
'How long does it take to prepare ingredients for Rava Upma?',
'What are some final touch tips for Rava Upma?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9431 |
| cosine_accuracy@3 | 0.9684 |
| cosine_accuracy@5 | 0.9705 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9431 |
| cosine_precision@3 | 0.8695 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7954 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.2407 |
| cosine_recall@5 | 0.3264 |
| cosine_recall@10 | 0.5043 |
| cosine_ndcg@10 | 0.9186 |
| cosine_mrr@10 | 0.9582 |
| **cosine_map@100** | **0.7389** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9449 |
| cosine_accuracy@3 | 0.9705 |
| cosine_accuracy@5 | 0.9725 |
| cosine_accuracy@10 | 0.9942 |
| cosine_precision@1 | 0.9449 |
| cosine_precision@3 | 0.8702 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1549 |
| cosine_recall@3 | 0.241 |
| cosine_recall@5 | 0.3266 |
| cosine_recall@10 | 0.5048 |
| cosine_ndcg@10 | 0.9197 |
| cosine_mrr@10 | 0.9599 |
| **cosine_map@100** | **0.7369** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9487 |
| cosine_accuracy@3 | 0.9728 |
| cosine_accuracy@5 | 0.9758 |
| cosine_accuracy@10 | 0.9941 |
| cosine_precision@1 | 0.9487 |
| cosine_precision@3 | 0.8726 |
| cosine_precision@5 | 0.8555 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1552 |
| cosine_recall@3 | 0.2412 |
| cosine_recall@5 | 0.327 |
| cosine_recall@10 | 0.5049 |
| cosine_ndcg@10 | 0.9205 |
| cosine_mrr@10 | 0.9628 |
| **cosine_map@100** | **0.7339** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9496 |
| cosine_accuracy@3 | 0.9744 |
| cosine_accuracy@5 | 0.9778 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9496 |
| cosine_precision@3 | 0.8755 |
| cosine_precision@5 | 0.8595 |
| cosine_precision@10 | 0.7983 |
| cosine_recall@1 | 0.1551 |
| cosine_recall@3 | 0.2415 |
| cosine_recall@5 | 0.3276 |
| cosine_recall@10 | 0.5053 |
| cosine_ndcg@10 | 0.9227 |
| cosine_mrr@10 | 0.964 |
| **cosine_map@100** | **0.7304** |
#### Information Retrieval
* Dataset: `dim_32`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9451 |
| cosine_accuracy@3 | 0.9731 |
| cosine_accuracy@5 | 0.9784 |
| cosine_accuracy@10 | 0.9948 |
| cosine_precision@1 | 0.9451 |
| cosine_precision@3 | 0.868 |
| cosine_precision@5 | 0.8521 |
| cosine_precision@10 | 0.7814 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.24 |
| cosine_recall@5 | 0.3251 |
| cosine_recall@10 | 0.4972 |
| cosine_ndcg@10 | 0.909 |
| cosine_mrr@10 | 0.9617 |
| **cosine_map@100** | **0.7086** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 75,086 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 150.64 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.44 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| <code>Tag: Beef and Broccoli<br><br>Spatula required to cook beef and broccoli based on different serving sizes: Serving 1 - flipping spatula, Serving 2 - flipping spatula, Serving 3 - flipping spatula, Serving 4 - flipping spatula<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.<br><br>For Beef and Broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>Preparations (ingredient) needed to cook Beef and Broccoli:<br>Marinate the beef slices with soy sauce and bakig soda for at least 20 minutes. Use rib-eye steak for best results. Alternatively you can also use flank steak or skirt steak.<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Cook time of Beef and Broccoli based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins<br><br>Macro ingredients required to cook Beef and Broccoli:<br>Broccoli, Soy Sauce, Ribeye Steak, Soy Sauce, Garlic, Scallion, Ginger, Baking Soda</code> | <code>What are some classic garnishes for Beef and Broccoli?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 100
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 100
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.0682 | 10 | 11.9607 | - | - | - | - | - |
| 0.1363 | 20 | 12.1342 | - | - | - | - | - |
| 0.2045 | 30 | 11.8794 | - | - | - | - | - |
| 0.2727 | 40 | 11.838 | - | - | - | - | - |
| 0.3409 | 50 | 11.9675 | - | - | - | - | - |
| 0.4090 | 60 | 11.5518 | - | - | - | - | - |
| 0.4772 | 70 | 11.3832 | - | - | - | - | - |
| 0.5454 | 80 | 11.2516 | - | - | - | - | - |
| 0.6135 | 90 | 11.1272 | - | - | - | - | - |
| 0.6817 | 100 | 10.9423 | - | - | - | - | - |
| 0.7499 | 110 | 5.0611 | - | - | - | - | - |
| 0.8181 | 120 | 0.2761 | - | - | - | - | - |
| 0.8862 | 130 | 5.5841 | - | - | - | - | - |
| 0.9544 | 140 | 5.453 | - | - | - | - | - |
| 1.0021 | 147 | - | 0.7339 | 0.7369 | 0.7086 | 0.7389 | 0.7304 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Grayx/john_paul_van_damme_63 | Grayx | 2024-07-01T13:36:26Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:36:14Z | Entry not found |
Grayx/john_paul_van_damme_64 | Grayx | 2024-07-01T13:36:55Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:36:44Z | Entry not found |
Grayx/john_paul_van_damme_65 | Grayx | 2024-07-01T13:37:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:37:36Z | Entry not found |
Adi-0-0-Gupta/Embedding-v2-128 | Adi-0-0-Gupta | 2024-07-01T13:37:54Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:75086",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-01T13:37:50Z | ---
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:75086
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Tag: Vegetable Pulao
Preparation time (ingredient) for Vegetable Pulao based on different serving sizes:
Serving 1 - 15 mins, Serving 2 - 18 mins, Serving 3 - 20 mins, Serving 4 - 22
mins'
sentences:
- What diet tags are relevant to Kimchi Fried Rice (Chicken)?
- What dietary classifications are suitable for Chicken & Broccoli Alfredo?
- What is the time required to prepare ingredients for Vegetable Pulao?
- source_sentence: "Tag: Vegetable Pulao\n\nMacro ingredients required to cook Vegetable\
\ Pulao:\nOrange Carrot, French Bean, Cauliflower, Plain Unsweetened Yogurt, Red\
\ Onion, Clove, Bay Leaf, Green Cardamom, Ginger-Garlic Paste, Green Chili Pepper,\
\ Cinnamon, Basmati Rice, Fresh Cilantro, Fresh Mint\n\nPreparations (ingredient)\
\ needed to cook Vegetable Pulao:\nWash the rice twice, and then soak it for at\
\ least 20 minutes. Drain the water and transfer the rice into the macro container.\
\ \nMix the yogurt, whole spices, green chili, and ginger garlic paste with the\
\ chopped veggies in a separate bowl, and then transfer it to the macro container.\
\ Please make sure to use plain yogurt. If using greek yogurt, use half the quantity\
\ of plain yogurt.\n\nTotal calories (nutritional energy) in Vegetable Pulao based\
\ on different serving sizes: Serving 1 - 300 mins, Serving 2 - 500 mins, Serving\
\ 3 - 700 mins, Serving 4 - 900 mins"
sentences:
- Can you give me some insights into Scrambled Eggs with Veggies?
- How should the ingredients for Chicken Pad Thai be prepared?
- Whatβs the calorie figure for Vegetable Pulao?
- source_sentence: 'Tag: Chicken Pad Thai
Spatula required to cook chicken pad thai based on different serving sizes: Serving
1 - noodle spatula, Serving 2 - noodle spatula, Serving 3 - noodle spatula, Serving
4 - noodle spatula'
sentences:
- What are the detailed cooking instructions for Rava Upma?
- Whatβs the best way to prep ingredients for Teriyaki Tofu?
- What kind of spatula do you need for Chicken Pad Thai?
- source_sentence: 'Tag: Kimchi Fried Rice (Chicken)
A small description of Kimchi Fried Rice (Chicken): Kimchi fried rice is made
with kimchi, spicy gochujang, and garlic. The umami flavors from the kimchi juice
balance beautifully with the spicy gochujang sauce and soy sauce, also creating
that beautiful red-tinted color. '
sentences:
- What spatula would you recommend for Vegetable Pulao?
- How can I improve the presentation of Chicken Pad Thai with garnishes?
- How would you describe the dish Kimchi Fried Rice (Chicken)?
- source_sentence: 'Tag: Rava Upma
Cook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins,
Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins
Preparation time (ingredient) for Rava Upma based on different serving sizes:
Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins'
sentences:
- How long does it take to prepare ingredients for Rava Upma?
- What are some final touch tips for Rava Upma?
- How would you summarize Mac & Cheese?
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.9431019051272216
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9684183608234241
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9704641350210971
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9431019051272216
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8694540340109961
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8533691343817927
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.795371435877765
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15481705915468094
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24073986604144076
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32643031270609696
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5043459566396565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.918622245854727
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9581816761141663
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7389009694677152
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.9448919575501854
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9704641350210971
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.97250990921877
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9942462600690449
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9448919575501854
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8702211993351234
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8534458509142053
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.796407109065337
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15493928650775676
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24095543792672336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32656065320980354
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5047541251867395
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9196805870274929
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9598828347773509
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7369163574373101
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.9487277841708222
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9727656309934791
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9758342922899885
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9941183991816903
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9487277841708222
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8725653156032902
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8555427694668201
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7964454673315434
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15515249707637835
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24121211958393993
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3269542903263234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5049379410293565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9205498440081438
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9627833488592988
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7339144971303314
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.949622810382304
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9744278225290883
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9777522056003068
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.949622810382304
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8755061160124451
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8594553126198696
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7982610919319781
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15511449448310274
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2414942027072444
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32761337610101815
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5053322703457185
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9226639618734229
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9639710344351698
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7303591002200212
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 32
type: dim_32
metrics:
- type: cosine_accuracy@1
value: 0.9451476793248945
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9731492136555427
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9783915100370797
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9947577036184632
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9451476793248945
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8679623236585261
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8521416698631887
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7814473852448537
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15477631695655358
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2399798683039478
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3251298048319623
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49721034132531955
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9089797806768267
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9616667478481831
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.708556549543554
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the π€ Hub
model = SentenceTransformer("Adi-0-0-Gupta/Embedding-v2-128")
# Run inference
sentences = [
'Tag: Rava Upma\n\nCook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins, Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins\n\nPreparation time (ingredient) for Rava Upma based on different serving sizes: Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins',
'How long does it take to prepare ingredients for Rava Upma?',
'What are some final touch tips for Rava Upma?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9431 |
| cosine_accuracy@3 | 0.9684 |
| cosine_accuracy@5 | 0.9705 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9431 |
| cosine_precision@3 | 0.8695 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7954 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.2407 |
| cosine_recall@5 | 0.3264 |
| cosine_recall@10 | 0.5043 |
| cosine_ndcg@10 | 0.9186 |
| cosine_mrr@10 | 0.9582 |
| **cosine_map@100** | **0.7389** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9449 |
| cosine_accuracy@3 | 0.9705 |
| cosine_accuracy@5 | 0.9725 |
| cosine_accuracy@10 | 0.9942 |
| cosine_precision@1 | 0.9449 |
| cosine_precision@3 | 0.8702 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1549 |
| cosine_recall@3 | 0.241 |
| cosine_recall@5 | 0.3266 |
| cosine_recall@10 | 0.5048 |
| cosine_ndcg@10 | 0.9197 |
| cosine_mrr@10 | 0.9599 |
| **cosine_map@100** | **0.7369** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9487 |
| cosine_accuracy@3 | 0.9728 |
| cosine_accuracy@5 | 0.9758 |
| cosine_accuracy@10 | 0.9941 |
| cosine_precision@1 | 0.9487 |
| cosine_precision@3 | 0.8726 |
| cosine_precision@5 | 0.8555 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1552 |
| cosine_recall@3 | 0.2412 |
| cosine_recall@5 | 0.327 |
| cosine_recall@10 | 0.5049 |
| cosine_ndcg@10 | 0.9205 |
| cosine_mrr@10 | 0.9628 |
| **cosine_map@100** | **0.7339** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9496 |
| cosine_accuracy@3 | 0.9744 |
| cosine_accuracy@5 | 0.9778 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9496 |
| cosine_precision@3 | 0.8755 |
| cosine_precision@5 | 0.8595 |
| cosine_precision@10 | 0.7983 |
| cosine_recall@1 | 0.1551 |
| cosine_recall@3 | 0.2415 |
| cosine_recall@5 | 0.3276 |
| cosine_recall@10 | 0.5053 |
| cosine_ndcg@10 | 0.9227 |
| cosine_mrr@10 | 0.964 |
| **cosine_map@100** | **0.7304** |
#### Information Retrieval
* Dataset: `dim_32`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9451 |
| cosine_accuracy@3 | 0.9731 |
| cosine_accuracy@5 | 0.9784 |
| cosine_accuracy@10 | 0.9948 |
| cosine_precision@1 | 0.9451 |
| cosine_precision@3 | 0.868 |
| cosine_precision@5 | 0.8521 |
| cosine_precision@10 | 0.7814 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.24 |
| cosine_recall@5 | 0.3251 |
| cosine_recall@10 | 0.4972 |
| cosine_ndcg@10 | 0.909 |
| cosine_mrr@10 | 0.9617 |
| **cosine_map@100** | **0.7086** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 75,086 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 150.64 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.44 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| <code>Tag: Beef and Broccoli<br><br>Spatula required to cook beef and broccoli based on different serving sizes: Serving 1 - flipping spatula, Serving 2 - flipping spatula, Serving 3 - flipping spatula, Serving 4 - flipping spatula<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.<br><br>For Beef and Broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>Preparations (ingredient) needed to cook Beef and Broccoli:<br>Marinate the beef slices with soy sauce and bakig soda for at least 20 minutes. Use rib-eye steak for best results. Alternatively you can also use flank steak or skirt steak.<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Cook time of Beef and Broccoli based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins<br><br>Macro ingredients required to cook Beef and Broccoli:<br>Broccoli, Soy Sauce, Ribeye Steak, Soy Sauce, Garlic, Scallion, Ginger, Baking Soda</code> | <code>What are some classic garnishes for Beef and Broccoli?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 100
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 100
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.0682 | 10 | 11.9607 | - | - | - | - | - |
| 0.1363 | 20 | 12.1342 | - | - | - | - | - |
| 0.2045 | 30 | 11.8794 | - | - | - | - | - |
| 0.2727 | 40 | 11.838 | - | - | - | - | - |
| 0.3409 | 50 | 11.9675 | - | - | - | - | - |
| 0.4090 | 60 | 11.5518 | - | - | - | - | - |
| 0.4772 | 70 | 11.3832 | - | - | - | - | - |
| 0.5454 | 80 | 11.2516 | - | - | - | - | - |
| 0.6135 | 90 | 11.1272 | - | - | - | - | - |
| 0.6817 | 100 | 10.9423 | - | - | - | - | - |
| 0.7499 | 110 | 5.0611 | - | - | - | - | - |
| 0.8181 | 120 | 0.2761 | - | - | - | - | - |
| 0.8862 | 130 | 5.5841 | - | - | - | - | - |
| 0.9544 | 140 | 5.453 | - | - | - | - | - |
| 1.0021 | 147 | - | 0.7339 | 0.7369 | 0.7086 | 0.7389 | 0.7304 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Grayx/john_paul_van_damme_66 | Grayx | 2024-07-01T13:38:18Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:38:06Z | Entry not found |
anushaporwal/wav2vec2-common_voice-tr-demo-mini | anushaporwal | 2024-07-01T13:56:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"mozilla-foundation/common_voice_16_0",
"generated_from_trainer",
"tr",
"dataset:common_voice_16_0",
"base_model:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-01T13:38:15Z | ---
language:
- tr
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- automatic-speech-recognition
- mozilla-foundation/common_voice_16_0
- generated_from_trainer
datasets:
- common_voice_16_0
metrics:
- wer
model-index:
- name: wav2vec2-common_voice-tr-demo-mini
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - TR
type: common_voice_16_0
config: tr
split: test[0:250]
args: 'Config: tr, Training split: train[0:3000], Eval split: test[0:250]'
metrics:
- name: Wer
type: wer
value: 0.9382716049382716
---
<!-- This model card 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-common_voice-tr-demo-mini
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the MOZILLA-FOUNDATION/COMMON_VOICE_16_0 - TR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9823
- Wer: 0.9383
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:------:|
| No log | 0.5333 | 100 | 4.0238 | 1.0 |
| No log | 1.0667 | 200 | 3.2451 | 1.0 |
| No log | 1.6 | 300 | 2.9997 | 1.0 |
| No log | 2.1333 | 400 | 1.4256 | 1.0054 |
| 4.5926 | 2.6667 | 500 | 1.2465 | 0.9730 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Grayx/john_paul_van_damme_67 | Grayx | 2024-07-01T13:39:05Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:38:53Z | Entry not found |
ayush7/outputs | ayush7 | 2024-07-01T15:04:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"license:mit",
"region:us"
] | null | 2024-07-01T13:38:59Z | ---
base_model: microsoft/Phi-3-mini-128k-instruct
datasets:
- generator
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: outputs
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. -->
# outputs
This model is a fine-tuned version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 0
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Training results
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
Grayx/john_paul_van_damme_68 | Grayx | 2024-07-01T13:39:24Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:39:13Z | Entry not found |
Adi-0-0-Gupta/Embedding-v2-256 | Adi-0-0-Gupta | 2024-07-01T13:40:01Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:75086",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-01T13:39:58Z | ---
datasets: []
language: []
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:75086
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: 'Tag: Vegetable Pulao
Preparation time (ingredient) for Vegetable Pulao based on different serving sizes:
Serving 1 - 15 mins, Serving 2 - 18 mins, Serving 3 - 20 mins, Serving 4 - 22
mins'
sentences:
- What diet tags are relevant to Kimchi Fried Rice (Chicken)?
- What dietary classifications are suitable for Chicken & Broccoli Alfredo?
- What is the time required to prepare ingredients for Vegetable Pulao?
- source_sentence: "Tag: Vegetable Pulao\n\nMacro ingredients required to cook Vegetable\
\ Pulao:\nOrange Carrot, French Bean, Cauliflower, Plain Unsweetened Yogurt, Red\
\ Onion, Clove, Bay Leaf, Green Cardamom, Ginger-Garlic Paste, Green Chili Pepper,\
\ Cinnamon, Basmati Rice, Fresh Cilantro, Fresh Mint\n\nPreparations (ingredient)\
\ needed to cook Vegetable Pulao:\nWash the rice twice, and then soak it for at\
\ least 20 minutes. Drain the water and transfer the rice into the macro container.\
\ \nMix the yogurt, whole spices, green chili, and ginger garlic paste with the\
\ chopped veggies in a separate bowl, and then transfer it to the macro container.\
\ Please make sure to use plain yogurt. If using greek yogurt, use half the quantity\
\ of plain yogurt.\n\nTotal calories (nutritional energy) in Vegetable Pulao based\
\ on different serving sizes: Serving 1 - 300 mins, Serving 2 - 500 mins, Serving\
\ 3 - 700 mins, Serving 4 - 900 mins"
sentences:
- Can you give me some insights into Scrambled Eggs with Veggies?
- How should the ingredients for Chicken Pad Thai be prepared?
- Whatβs the calorie figure for Vegetable Pulao?
- source_sentence: 'Tag: Chicken Pad Thai
Spatula required to cook chicken pad thai based on different serving sizes: Serving
1 - noodle spatula, Serving 2 - noodle spatula, Serving 3 - noodle spatula, Serving
4 - noodle spatula'
sentences:
- What are the detailed cooking instructions for Rava Upma?
- Whatβs the best way to prep ingredients for Teriyaki Tofu?
- What kind of spatula do you need for Chicken Pad Thai?
- source_sentence: 'Tag: Kimchi Fried Rice (Chicken)
A small description of Kimchi Fried Rice (Chicken): Kimchi fried rice is made
with kimchi, spicy gochujang, and garlic. The umami flavors from the kimchi juice
balance beautifully with the spicy gochujang sauce and soy sauce, also creating
that beautiful red-tinted color. '
sentences:
- What spatula would you recommend for Vegetable Pulao?
- How can I improve the presentation of Chicken Pad Thai with garnishes?
- How would you describe the dish Kimchi Fried Rice (Chicken)?
- source_sentence: 'Tag: Rava Upma
Cook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins,
Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins
Preparation time (ingredient) for Rava Upma based on different serving sizes:
Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins'
sentences:
- How long does it take to prepare ingredients for Rava Upma?
- What are some final touch tips for Rava Upma?
- How would you summarize Mac & Cheese?
model-index:
- name: SentenceTransformer
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 384
type: dim_384
metrics:
- type: cosine_accuracy@1
value: 0.9431019051272216
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9684183608234241
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9704641350210971
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9431019051272216
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8694540340109961
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8533691343817927
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.795371435877765
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15481705915468094
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24073986604144076
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32643031270609696
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5043459566396565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.918622245854727
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9581816761141663
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7389009694677152
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.9448919575501854
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9704641350210971
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.97250990921877
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9942462600690449
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9448919575501854
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8702211993351234
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8534458509142053
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.796407109065337
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15493928650775676
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24095543792672336
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32656065320980354
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5047541251867395
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9196805870274929
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9598828347773509
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7369163574373101
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.9487277841708222
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9727656309934791
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9758342922899885
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9941183991816903
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9487277841708222
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8725653156032902
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8555427694668201
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7964454673315434
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15515249707637835
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.24121211958393993
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3269542903263234
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5049379410293565
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9205498440081438
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9627833488592988
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7339144971303314
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.949622810382304
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9744278225290883
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9777522056003068
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9943741209563994
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.949622810382304
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8755061160124451
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8594553126198696
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7982610919319781
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15511449448310274
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2414942027072444
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.32761337610101815
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.5053322703457185
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9226639618734229
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9639710344351698
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7303591002200212
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 32
type: dim_32
metrics:
- type: cosine_accuracy@1
value: 0.9451476793248945
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.9731492136555427
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.9783915100370797
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9947577036184632
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.9451476793248945
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.8679623236585261
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.8521416698631887
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.7814473852448537
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.15477631695655358
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.2399798683039478
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.3251298048319623
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.49721034132531955
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9089797806768267
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.9616667478481831
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.708556549543554
name: Cosine Map@100
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the π€ Hub
model = SentenceTransformer("Adi-0-0-Gupta/Embedding-v2-256")
# Run inference
sentences = [
'Tag: Rava Upma\n\nCook time of Rava Upma based on different serving sizes: Serving 1 - 26 mins, Serving 2 - 26 mins, Serving 3 - 28 mins, Serving 4 - 30 mins\n\nPreparation time (ingredient) for Rava Upma based on different serving sizes: Serving 1 - 6 mins, Serving 2 - 7 mins, Serving 3 - 8 mins, Serving 4 - 10 mins',
'How long does it take to prepare ingredients for Rava Upma?',
'What are some final touch tips for Rava Upma?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Dataset: `dim_384`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9431 |
| cosine_accuracy@3 | 0.9684 |
| cosine_accuracy@5 | 0.9705 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9431 |
| cosine_precision@3 | 0.8695 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7954 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.2407 |
| cosine_recall@5 | 0.3264 |
| cosine_recall@10 | 0.5043 |
| cosine_ndcg@10 | 0.9186 |
| cosine_mrr@10 | 0.9582 |
| **cosine_map@100** | **0.7389** |
#### Information Retrieval
* Dataset: `dim_256`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9449 |
| cosine_accuracy@3 | 0.9705 |
| cosine_accuracy@5 | 0.9725 |
| cosine_accuracy@10 | 0.9942 |
| cosine_precision@1 | 0.9449 |
| cosine_precision@3 | 0.8702 |
| cosine_precision@5 | 0.8534 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1549 |
| cosine_recall@3 | 0.241 |
| cosine_recall@5 | 0.3266 |
| cosine_recall@10 | 0.5048 |
| cosine_ndcg@10 | 0.9197 |
| cosine_mrr@10 | 0.9599 |
| **cosine_map@100** | **0.7369** |
#### Information Retrieval
* Dataset: `dim_128`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9487 |
| cosine_accuracy@3 | 0.9728 |
| cosine_accuracy@5 | 0.9758 |
| cosine_accuracy@10 | 0.9941 |
| cosine_precision@1 | 0.9487 |
| cosine_precision@3 | 0.8726 |
| cosine_precision@5 | 0.8555 |
| cosine_precision@10 | 0.7964 |
| cosine_recall@1 | 0.1552 |
| cosine_recall@3 | 0.2412 |
| cosine_recall@5 | 0.327 |
| cosine_recall@10 | 0.5049 |
| cosine_ndcg@10 | 0.9205 |
| cosine_mrr@10 | 0.9628 |
| **cosine_map@100** | **0.7339** |
#### Information Retrieval
* Dataset: `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9496 |
| cosine_accuracy@3 | 0.9744 |
| cosine_accuracy@5 | 0.9778 |
| cosine_accuracy@10 | 0.9944 |
| cosine_precision@1 | 0.9496 |
| cosine_precision@3 | 0.8755 |
| cosine_precision@5 | 0.8595 |
| cosine_precision@10 | 0.7983 |
| cosine_recall@1 | 0.1551 |
| cosine_recall@3 | 0.2415 |
| cosine_recall@5 | 0.3276 |
| cosine_recall@10 | 0.5053 |
| cosine_ndcg@10 | 0.9227 |
| cosine_mrr@10 | 0.964 |
| **cosine_map@100** | **0.7304** |
#### Information Retrieval
* Dataset: `dim_32`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.9451 |
| cosine_accuracy@3 | 0.9731 |
| cosine_accuracy@5 | 0.9784 |
| cosine_accuracy@10 | 0.9948 |
| cosine_precision@1 | 0.9451 |
| cosine_precision@3 | 0.868 |
| cosine_precision@5 | 0.8521 |
| cosine_precision@10 | 0.7814 |
| cosine_recall@1 | 0.1548 |
| cosine_recall@3 | 0.24 |
| cosine_recall@5 | 0.3251 |
| cosine_recall@10 | 0.4972 |
| cosine_ndcg@10 | 0.909 |
| cosine_mrr@10 | 0.9617 |
| **cosine_map@100** | **0.7086** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 75,086 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:-------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 20 tokens</li><li>mean: 150.64 tokens</li><li>max: 512 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 15.44 tokens</li><li>max: 22 tokens</li></ul> |
* Samples:
| positive | anchor |
|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------|
| <code>Tag: Beef and Broccoli<br><br>Spatula required to cook beef and broccoli based on different serving sizes: Serving 1 - flipping spatula, Serving 2 - flipping spatula, Serving 3 - flipping spatula, Serving 4 - flipping spatula<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.<br><br>For Beef and Broccoli, these dietary tags go well with it: dinner, contains soy, meat recipes, asian american cuisine, lunch, american cuisine, beef recipes, asian cuisine, chinese cuisine, hearty recipes, rice recipes, protein rich recipes, non vegetarian, saucy recipes, stir fry recipes, healthy recipes</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>A small description of Beef and Broccoli: Stir fried broccoli and tender beef strips stir-fried in a rich savory sauce.</code> | <code>How do you describe Beef and Broccoli?</code> |
| <code>Tag: Beef and Broccoli<br><br>Garnishing tips for Beef and Broccoli: Best served on it's own or on top of hot rice with chopped scallions!<br><br>Preparations (ingredient) needed to cook Beef and Broccoli:<br>Marinate the beef slices with soy sauce and bakig soda for at least 20 minutes. Use rib-eye steak for best results. Alternatively you can also use flank steak or skirt steak.<br><br>Recipes similar to beef and broccoli: Pepper Steak Skillet, Beef & Arugula Stir-Fry, Sticky Beef & Zucchini, Beef Kaldereta, Garlic Butter Steak Bites, Roasted Broccoli & Carrots, Beef Skillet Lasagna, Beef Stew, Keto Beef & Cabbage<br><br>Cook time of Beef and Broccoli based on different serving sizes: Serving 1 - 20 mins, Serving 2 - 25 mins, Serving 3 - 30 mins, Serving 4 - 35 mins<br><br>Macro ingredients required to cook Beef and Broccoli:<br>Broccoli, Soy Sauce, Ribeye Steak, Soy Sauce, Garlic, Scallion, Ginger, Baking Soda</code> | <code>What are some classic garnishes for Beef and Broccoli?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
384,
256,
128,
64,
32
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `gradient_accumulation_steps`: 16
- `learning_rate`: 2e-05
- `num_train_epochs`: 100
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: True
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 16
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 100
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: True
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: True
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_128_cosine_map@100 | dim_256_cosine_map@100 | dim_32_cosine_map@100 | dim_384_cosine_map@100 | dim_64_cosine_map@100 |
|:------:|:----:|:-------------:|:----------------------:|:----------------------:|:---------------------:|:----------------------:|:---------------------:|
| 0.0682 | 10 | 11.9607 | - | - | - | - | - |
| 0.1363 | 20 | 12.1342 | - | - | - | - | - |
| 0.2045 | 30 | 11.8794 | - | - | - | - | - |
| 0.2727 | 40 | 11.838 | - | - | - | - | - |
| 0.3409 | 50 | 11.9675 | - | - | - | - | - |
| 0.4090 | 60 | 11.5518 | - | - | - | - | - |
| 0.4772 | 70 | 11.3832 | - | - | - | - | - |
| 0.5454 | 80 | 11.2516 | - | - | - | - | - |
| 0.6135 | 90 | 11.1272 | - | - | - | - | - |
| 0.6817 | 100 | 10.9423 | - | - | - | - | - |
| 0.7499 | 110 | 5.0611 | - | - | - | - | - |
| 0.8181 | 120 | 0.2761 | - | - | - | - | - |
| 0.8862 | 130 | 5.5841 | - | - | - | - | - |
| 0.9544 | 140 | 5.453 | - | - | - | - | - |
| 1.0021 | 147 | - | 0.7339 | 0.7369 | 0.7086 | 0.7389 | 0.7304 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.41.2
- PyTorch: 2.1.2+cu121
- Accelerate: 0.31.0
- Datasets: 2.19.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
itay-nakash/model_0e1a108b92_sweep_exalted-totem-1157 | itay-nakash | 2024-07-01T13:40:16Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:40:16Z | Entry not found |
OnFinanceAI/llama-3-8b-analyst-qa-instr | OnFinanceAI | 2024-07-01T14:04:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:40:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
summertime0/nashk1 | summertime0 | 2024-07-01T13:40:45Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:40:44Z | Entry not found |
Meziane/question_answering_T5_policy_qa_4 | Meziane | 2024-07-01T13:45:35Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"question-answering",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | 2024-07-01T13:41:48Z | Entry not found |
pursuitofds/finetuned_qa_llama3_8b_qlora_model_withfull_qv | pursuitofds | 2024-07-01T13:43:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:pursuitofds/finetuned_qa_llama3_8b_qlora_model_withfull_qv",
"region:us"
] | null | 2024-07-01T13:43:13Z | ---
library_name: peft
base_model: pursuitofds/finetuned_qa_llama3_8b_qlora_model_withfull_qv
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.11.1 |
vbafnaa/whisper-small-hi | vbafnaa | 2024-07-01T13:43:36Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:43:36Z | Entry not found |
taylor001/Mistral_01 | taylor001 | 2024-07-01T13:44:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:44:11Z | ---
base_model: unsloth/mistral-7b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** taylor001
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
rinogrego/GritLM-BioMistral-7B-8-bit | rinogrego | 2024-07-01T13:48:45Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:48:45Z | Entry not found |
henrik-dra/paligemma-ft-svhn | henrik-dra | 2024-07-01T15:54:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:49:08Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ArisA1/TitodobleP | ArisA1 | 2024-07-01T13:51:50Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-01T13:50:04Z | ---
license: openrail
---
|
vatsaldin/distilbert-base-uncased-finetuned-ner | vatsaldin | 2024-07-01T13:50:27Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:50:27Z | Entry not found |
rezaakb/reward_modeling_anthropic_hh | rezaakb | 2024-07-01T13:50:46Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"region:us"
] | null | 2024-07-01T13:50:43Z | ---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: reward_modeling_anthropic_hh
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. -->
# reward_modeling_anthropic_hh
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) 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: 1.41e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1.0
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.1
- Datasets 2.14.7
- Tokenizers 0.14.1
|
PhucDanh/Bartpho-fine-tuning-on-UIT-Course-information | PhucDanh | 2024-07-01T14:02:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"question-answering",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-07-01T13:51:08Z | ---
license: mit
---
|
fontesaurelio/fontes | fontesaurelio | 2024-07-01T13:52:26Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:52:25Z | Entry not found |
DipeshChaudhary/ShareGPTChatBot-Counselchat1 | DipeshChaudhary | 2024-07-02T12:17:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:53:15Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# To Use This Model
# STEP 1:*
- Installs Unsloth, Xformers (Flash Attention) and all other packages! according to your environments and GPU
- To install Unsloth on your own computer, follow the installation instructions on our Github page : [LINK IS HERE](https://github.com/unslothai/unsloth#installation-instructions---conda)
# STEP 2: Now Follow the CODES
**LOAD THE MODEL**
```
from unsloth import FastLanguageModel
```
```
import torch
max_seq_length = 2048 # Choose any! We auto support RoPE Scaling internally!
dtype = None # None for auto detection. Float16 for Tesla T4, V100, Bfloat16 for Ampere+
load_in_4bit = True # Use 4bit quantization to reduce memory usage. Can be False.
from transformers import AutoTokenizer
```
```
model, tokenizer = FastLanguageModel.from_pretrained(
model_name="DipeshChaudhary/ShareGPTChatBot-Counselchat1", # Your fine-tuned model
max_seq_length=max_seq_length,
dtype=dtype,
load_in_4bit=load_in_4bit,
)
```
# We now use the Llama-3 format for conversation style finetunes. We use Open Assistant conversations in ShareGPT style.
**We use our get_chat_template function to get the correct chat template. They support zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old and their own optimized unsloth template**
```
from unsloth.chat_templates import get_chat_template
tokenizer = get_chat_template(
tokenizer,
chat_template = "llama-3", # Supports zephyr, chatml, mistral, llama, alpaca, vicuna, vicuna_old, unsloth
mapping = {"role" : "from", "content" : "value", "user" : "human", "assistant" : "gpt"}, # ShareGPT style
)
```
## FOR ACTUAL INFERENCE
```
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
messages = [
{"from": "human", "value": "I'm worry about my exam."},
]
inputs = tokenizer.apply_chat_template(
messages,
tokenize = True,
add_generation_prompt = True, # Must add for generation
return_tensors = "pt",
).to("cuda")
from transformers import TextStreamer
text_streamer = TextStreamer(tokenizer)
x= model.generate(input_ids = inputs, streamer = text_streamer, max_new_tokens = 128, use_cache = True)
```
# Uploaded model
- **Developed by:** DipeshChaudhary
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
habulaj/1867918466 | habulaj | 2024-07-01T13:54:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:54:08Z | Entry not found |
gokulsrinivasagan/gpt_train_2_768 | gokulsrinivasagan | 2024-07-02T16:03:53Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:54:20Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_2_768
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.10393614847954215
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_2_768
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 7.4883
- Accuracy: 0.1039
## Model description
More information needed
## Intended uses & 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: 64
- eval_batch_size: 64
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.9688 | 0.0001 | 1 | 10.9688 | 0.0000 |
| 10.9609 | 0.0002 | 2 | 10.9688 | 0.0000 |
| 10.9609 | 0.0003 | 3 | 10.9688 | 0.0000 |
| 10.9609 | 0.0004 | 4 | 10.9688 | 0.0000 |
| 10.9609 | 0.0005 | 5 | 10.9688 | 0.0000 |
| 10.9688 | 0.0006 | 6 | 10.9688 | 0.0000 |
| 10.9609 | 0.0007 | 7 | 10.9688 | 0.0000 |
| 10.9609 | 0.0008 | 8 | 10.9688 | 0.0000 |
| 10.9688 | 0.0009 | 9 | 10.9688 | 0.0000 |
| 10.9531 | 0.0010 | 10 | 10.9688 | 0.0000 |
| 10.9688 | 0.0011 | 11 | 10.9688 | 0.0000 |
| 10.9688 | 0.0012 | 12 | 10.9688 | 0.0000 |
| 10.9531 | 0.0013 | 13 | 10.9688 | 0.0000 |
| 10.9609 | 0.0014 | 14 | 10.9688 | 0.0000 |
| 10.9688 | 0.0015 | 15 | 10.9688 | 0.0000 |
| 10.9766 | 0.0015 | 16 | 10.9688 | 0.0000 |
| 10.9688 | 0.0016 | 17 | 10.9688 | 0.0000 |
| 10.9609 | 0.0017 | 18 | 10.8828 | 0.0007 |
| 10.8906 | 0.0018 | 19 | 10.8047 | 0.0051 |
| 10.8359 | 0.0019 | 20 | 10.7188 | 0.0112 |
| 10.75 | 0.0020 | 21 | 10.6484 | 0.0175 |
| 10.6719 | 0.0021 | 22 | 10.5781 | 0.0280 |
| 10.6172 | 0.0022 | 23 | 10.5 | 0.0392 |
| 10.5391 | 0.0023 | 24 | 10.4375 | 0.0447 |
| 10.5078 | 0.0024 | 25 | 10.3828 | 0.0478 |
| 10.4609 | 0.0025 | 26 | 10.3125 | 0.0499 |
| 10.3906 | 0.0026 | 27 | 10.2656 | 0.0511 |
| 10.3281 | 0.0027 | 28 | 10.2109 | 0.0521 |
| 10.2656 | 0.0028 | 29 | 10.1641 | 0.0531 |
| 10.25 | 0.0029 | 30 | 10.1172 | 0.0537 |
| 10.2031 | 0.0030 | 31 | 10.0703 | 0.0544 |
| 10.1641 | 0.0031 | 32 | 10.0312 | 0.0552 |
| 10.125 | 0.0032 | 33 | 9.9922 | 0.0558 |
| 10.0859 | 0.0033 | 34 | 9.9609 | 0.0562 |
| 10.0391 | 0.0034 | 35 | 9.9219 | 0.0566 |
| 10.0156 | 0.0035 | 36 | 9.8906 | 0.0568 |
| 9.9609 | 0.0036 | 37 | 9.8594 | 0.0567 |
| 9.9141 | 0.0037 | 38 | 9.8359 | 0.0566 |
| 9.875 | 0.0038 | 39 | 9.8047 | 0.0568 |
| 9.8672 | 0.0039 | 40 | 9.7812 | 0.0569 |
| 9.8438 | 0.0040 | 41 | 9.7578 | 0.0568 |
| 9.7969 | 0.0041 | 42 | 9.7344 | 0.0565 |
| 9.8203 | 0.0042 | 43 | 9.7109 | 0.0564 |
| 9.7891 | 0.0043 | 44 | 9.6875 | 0.0564 |
| 9.7031 | 0.0044 | 45 | 9.6719 | 0.0566 |
| 9.7344 | 0.0045 | 46 | 9.6484 | 0.0569 |
| 9.7266 | 0.0046 | 47 | 9.6328 | 0.0573 |
| 9.7031 | 0.0046 | 48 | 9.6172 | 0.0579 |
| 9.7109 | 0.0047 | 49 | 9.6016 | 0.0585 |
| 9.6406 | 0.0048 | 50 | 9.5781 | 0.0591 |
| 9.6797 | 0.0049 | 51 | 9.5625 | 0.0597 |
| 9.6328 | 0.0050 | 52 | 9.5469 | 0.0605 |
| 9.6172 | 0.0051 | 53 | 9.5312 | 0.0612 |
| 9.6172 | 0.0052 | 54 | 9.5234 | 0.0615 |
| 9.5703 | 0.0053 | 55 | 9.5078 | 0.0617 |
| 9.5781 | 0.0054 | 56 | 9.4922 | 0.0618 |
| 9.5938 | 0.0055 | 57 | 9.4766 | 0.0620 |
| 9.5391 | 0.0056 | 58 | 9.4688 | 0.0621 |
| 9.4922 | 0.0057 | 59 | 9.4531 | 0.0620 |
| 9.4688 | 0.0058 | 60 | 9.4375 | 0.0620 |
| 9.4922 | 0.0059 | 61 | 9.4297 | 0.0620 |
| 9.4609 | 0.0060 | 62 | 9.4141 | 0.0620 |
| 9.4297 | 0.0061 | 63 | 9.4062 | 0.0620 |
| 9.4844 | 0.0062 | 64 | 9.3906 | 0.0620 |
| 9.4531 | 0.0063 | 65 | 9.3828 | 0.0622 |
| 9.4375 | 0.0064 | 66 | 9.3672 | 0.0625 |
| 9.4375 | 0.0065 | 67 | 9.3594 | 0.0628 |
| 9.3984 | 0.0066 | 68 | 9.3438 | 0.0630 |
| 9.4062 | 0.0067 | 69 | 9.3359 | 0.0632 |
| 9.3984 | 0.0068 | 70 | 9.3203 | 0.0633 |
| 9.4375 | 0.0069 | 71 | 9.3125 | 0.0633 |
| 9.3828 | 0.0070 | 72 | 9.3047 | 0.0634 |
| 9.3594 | 0.0071 | 73 | 9.2891 | 0.0634 |
| 9.3438 | 0.0072 | 74 | 9.2812 | 0.0634 |
| 9.3672 | 0.0073 | 75 | 9.2734 | 0.0634 |
| 9.3125 | 0.0074 | 76 | 9.2578 | 0.0634 |
| 9.3047 | 0.0075 | 77 | 9.25 | 0.0633 |
| 9.2969 | 0.0076 | 78 | 9.2422 | 0.0632 |
| 9.2891 | 0.0077 | 79 | 9.2266 | 0.0631 |
| 9.2812 | 0.0077 | 80 | 9.2188 | 0.0631 |
| 9.2656 | 0.0078 | 81 | 9.2109 | 0.0632 |
| 9.2422 | 0.0079 | 82 | 9.2031 | 0.0633 |
| 9.2656 | 0.0080 | 83 | 9.1875 | 0.0635 |
| 9.25 | 0.0081 | 84 | 9.1797 | 0.0637 |
| 9.2344 | 0.0082 | 85 | 9.1719 | 0.0639 |
| 9.2266 | 0.0083 | 86 | 9.1562 | 0.0640 |
| 9.25 | 0.0084 | 87 | 9.1484 | 0.0641 |
| 9.1406 | 0.0085 | 88 | 9.1406 | 0.0641 |
| 9.1562 | 0.0086 | 89 | 9.1328 | 0.0642 |
| 9.2031 | 0.0087 | 90 | 9.1172 | 0.0641 |
| 9.1406 | 0.0088 | 91 | 9.1094 | 0.0642 |
| 9.1406 | 0.0089 | 92 | 9.1016 | 0.0643 |
| 9.1406 | 0.0090 | 93 | 9.0938 | 0.0644 |
| 9.1328 | 0.0091 | 94 | 9.0781 | 0.0644 |
| 9.125 | 0.0092 | 95 | 9.0703 | 0.0645 |
| 9.1016 | 0.0093 | 96 | 9.0625 | 0.0646 |
| 9.125 | 0.0094 | 97 | 9.0547 | 0.0648 |
| 9.0625 | 0.0095 | 98 | 9.0391 | 0.0652 |
| 9.0859 | 0.0096 | 99 | 9.0312 | 0.0655 |
| 9.0547 | 0.0097 | 100 | 9.0234 | 0.0657 |
| 9.0547 | 0.0098 | 101 | 9.0156 | 0.0658 |
| 9.0625 | 0.0099 | 102 | 9.0078 | 0.0659 |
| 9.0547 | 0.0100 | 103 | 8.9922 | 0.0661 |
| 9.0156 | 0.0101 | 104 | 8.9844 | 0.0662 |
| 9.0391 | 0.0102 | 105 | 8.9766 | 0.0664 |
| 9.0234 | 0.0103 | 106 | 8.9688 | 0.0664 |
| 9.0234 | 0.0104 | 107 | 8.9609 | 0.0664 |
| 8.9766 | 0.0105 | 108 | 8.9453 | 0.0664 |
| 8.9922 | 0.0106 | 109 | 8.9375 | 0.0665 |
| 8.9453 | 0.0107 | 110 | 8.9297 | 0.0665 |
| 8.9609 | 0.0108 | 111 | 8.9219 | 0.0664 |
| 8.9766 | 0.0108 | 112 | 8.9141 | 0.0664 |
| 8.9844 | 0.0109 | 113 | 8.8984 | 0.0666 |
| 8.9453 | 0.0110 | 114 | 8.8906 | 0.0669 |
| 8.9688 | 0.0111 | 115 | 8.8828 | 0.0673 |
| 8.9766 | 0.0112 | 116 | 8.875 | 0.0677 |
| 8.9297 | 0.0113 | 117 | 8.8672 | 0.0682 |
| 8.9297 | 0.0114 | 118 | 8.8594 | 0.0689 |
| 8.8672 | 0.0115 | 119 | 8.8516 | 0.0694 |
| 8.8906 | 0.0116 | 120 | 8.8359 | 0.0700 |
| 8.8984 | 0.0117 | 121 | 8.8281 | 0.0703 |
| 8.8984 | 0.0118 | 122 | 8.8203 | 0.0704 |
| 8.8828 | 0.0119 | 123 | 8.8125 | 0.0706 |
| 8.8594 | 0.0120 | 124 | 8.8047 | 0.0707 |
| 8.8281 | 0.0121 | 125 | 8.7969 | 0.0708 |
| 8.8359 | 0.0122 | 126 | 8.7812 | 0.0710 |
| 8.8359 | 0.0123 | 127 | 8.7734 | 0.0711 |
| 8.8281 | 0.0124 | 128 | 8.7656 | 0.0710 |
| 8.8438 | 0.0125 | 129 | 8.7578 | 0.0707 |
| 8.7578 | 0.0126 | 130 | 8.75 | 0.0702 |
| 8.7812 | 0.0127 | 131 | 8.7422 | 0.0698 |
| 8.7734 | 0.0128 | 132 | 8.7344 | 0.0697 |
| 8.7812 | 0.0129 | 133 | 8.7266 | 0.0701 |
| 8.7891 | 0.0130 | 134 | 8.7188 | 0.0707 |
| 8.7656 | 0.0131 | 135 | 8.7031 | 0.0713 |
| 8.7891 | 0.0132 | 136 | 8.6953 | 0.0719 |
| 8.7188 | 0.0133 | 137 | 8.6875 | 0.0726 |
| 8.7266 | 0.0134 | 138 | 8.6797 | 0.0733 |
| 8.75 | 0.0135 | 139 | 8.6719 | 0.0737 |
| 8.7188 | 0.0136 | 140 | 8.6641 | 0.0740 |
| 8.7344 | 0.0137 | 141 | 8.6562 | 0.0742 |
| 8.6641 | 0.0138 | 142 | 8.6484 | 0.0742 |
| 8.7031 | 0.0139 | 143 | 8.6406 | 0.0741 |
| 8.6797 | 0.0139 | 144 | 8.6328 | 0.0741 |
| 8.6797 | 0.0140 | 145 | 8.6172 | 0.0739 |
| 8.6719 | 0.0141 | 146 | 8.6094 | 0.0736 |
| 8.6641 | 0.0142 | 147 | 8.6016 | 0.0736 |
| 8.6484 | 0.0143 | 148 | 8.5938 | 0.0737 |
| 8.6172 | 0.0144 | 149 | 8.5859 | 0.0741 |
| 8.6719 | 0.0145 | 150 | 8.5781 | 0.0746 |
| 8.6406 | 0.0146 | 151 | 8.5703 | 0.0750 |
| 8.6172 | 0.0147 | 152 | 8.5625 | 0.0754 |
| 8.6094 | 0.0148 | 153 | 8.5547 | 0.0756 |
| 8.6016 | 0.0149 | 154 | 8.5469 | 0.0756 |
| 8.5625 | 0.0150 | 155 | 8.5391 | 0.0755 |
| 8.5312 | 0.0151 | 156 | 8.5312 | 0.0756 |
| 8.5703 | 0.0152 | 157 | 8.5234 | 0.0756 |
| 8.6172 | 0.0153 | 158 | 8.5156 | 0.0757 |
| 8.5781 | 0.0154 | 159 | 8.5078 | 0.0757 |
| 8.6016 | 0.0155 | 160 | 8.5 | 0.0759 |
| 8.5547 | 0.0156 | 161 | 8.4922 | 0.0762 |
| 8.5547 | 0.0157 | 162 | 8.4844 | 0.0766 |
| 8.5312 | 0.0158 | 163 | 8.4766 | 0.0767 |
| 8.5 | 0.0159 | 164 | 8.4688 | 0.0767 |
| 8.5312 | 0.0160 | 165 | 8.4609 | 0.0766 |
| 8.5312 | 0.0161 | 166 | 8.4531 | 0.0766 |
| 8.4531 | 0.0162 | 167 | 8.4453 | 0.0767 |
| 8.4766 | 0.0163 | 168 | 8.4375 | 0.0768 |
| 8.4766 | 0.0164 | 169 | 8.4297 | 0.0770 |
| 8.4688 | 0.0165 | 170 | 8.4219 | 0.0772 |
| 8.4922 | 0.0166 | 171 | 8.4141 | 0.0775 |
| 8.4375 | 0.0167 | 172 | 8.4141 | 0.0777 |
| 8.4609 | 0.0168 | 173 | 8.4062 | 0.0777 |
| 8.4141 | 0.0169 | 174 | 8.3984 | 0.0777 |
| 8.4531 | 0.0170 | 175 | 8.3906 | 0.0778 |
| 8.3984 | 0.0170 | 176 | 8.3828 | 0.0778 |
| 8.4141 | 0.0171 | 177 | 8.375 | 0.0779 |
| 8.4453 | 0.0172 | 178 | 8.3672 | 0.0781 |
| 8.4219 | 0.0173 | 179 | 8.3594 | 0.0783 |
| 8.4219 | 0.0174 | 180 | 8.3516 | 0.0785 |
| 8.4062 | 0.0175 | 181 | 8.3438 | 0.0785 |
| 8.3984 | 0.0176 | 182 | 8.3359 | 0.0787 |
| 8.3828 | 0.0177 | 183 | 8.3281 | 0.0790 |
| 8.375 | 0.0178 | 184 | 8.3203 | 0.0792 |
| 8.3594 | 0.0179 | 185 | 8.3125 | 0.0795 |
| 8.375 | 0.0180 | 186 | 8.3125 | 0.0797 |
| 8.3125 | 0.0181 | 187 | 8.3047 | 0.0796 |
| 8.3438 | 0.0182 | 188 | 8.2969 | 0.0796 |
| 8.3281 | 0.0183 | 189 | 8.2891 | 0.0795 |
| 8.3359 | 0.0184 | 190 | 8.2812 | 0.0795 |
| 8.3047 | 0.0185 | 191 | 8.2734 | 0.0798 |
| 8.3359 | 0.0186 | 192 | 8.2656 | 0.0800 |
| 8.3047 | 0.0187 | 193 | 8.2578 | 0.0803 |
| 8.2969 | 0.0188 | 194 | 8.2578 | 0.0805 |
| 8.3203 | 0.0189 | 195 | 8.25 | 0.0807 |
| 8.2734 | 0.0190 | 196 | 8.2422 | 0.0809 |
| 8.25 | 0.0191 | 197 | 8.2344 | 0.0809 |
| 8.2734 | 0.0192 | 198 | 8.2266 | 0.0810 |
| 8.2109 | 0.0193 | 199 | 8.2188 | 0.0809 |
| 8.25 | 0.0194 | 200 | 8.2109 | 0.0809 |
| 8.2734 | 0.0195 | 201 | 8.2031 | 0.0810 |
| 8.2188 | 0.0196 | 202 | 8.2031 | 0.0812 |
| 8.2578 | 0.0197 | 203 | 8.1953 | 0.0816 |
| 8.2344 | 0.0198 | 204 | 8.1875 | 0.0819 |
| 8.2969 | 0.0199 | 205 | 8.1797 | 0.0823 |
| 8.2812 | 0.0200 | 206 | 8.1719 | 0.0825 |
| 8.2578 | 0.0201 | 207 | 8.1641 | 0.0824 |
| 8.2031 | 0.0201 | 208 | 8.1641 | 0.0824 |
| 8.1953 | 0.0202 | 209 | 8.1562 | 0.0822 |
| 8.2344 | 0.0203 | 210 | 8.1484 | 0.0821 |
| 8.1484 | 0.0204 | 211 | 8.1406 | 0.0822 |
| 8.2188 | 0.0205 | 212 | 8.1328 | 0.0824 |
| 8.1406 | 0.0206 | 213 | 8.1328 | 0.0826 |
| 8.1641 | 0.0207 | 214 | 8.125 | 0.0829 |
| 8.1328 | 0.0208 | 215 | 8.1172 | 0.0831 |
| 8.1875 | 0.0209 | 216 | 8.1094 | 0.0833 |
| 8.1719 | 0.0210 | 217 | 8.1016 | 0.0835 |
| 8.125 | 0.0211 | 218 | 8.1016 | 0.0835 |
| 8.1172 | 0.0212 | 219 | 8.0938 | 0.0835 |
| 8.1172 | 0.0213 | 220 | 8.0859 | 0.0834 |
| 8.1562 | 0.0214 | 221 | 8.0781 | 0.0835 |
| 8.0781 | 0.0215 | 222 | 8.0781 | 0.0838 |
| 8.1094 | 0.0216 | 223 | 8.0703 | 0.0840 |
| 8.0938 | 0.0217 | 224 | 8.0625 | 0.0843 |
| 8.0938 | 0.0218 | 225 | 8.0547 | 0.0846 |
| 8.1016 | 0.0219 | 226 | 8.0469 | 0.0847 |
| 8.1094 | 0.0220 | 227 | 8.0469 | 0.0846 |
| 8.1016 | 0.0221 | 228 | 8.0391 | 0.0844 |
| 8.0859 | 0.0222 | 229 | 8.0312 | 0.0844 |
| 8.0859 | 0.0223 | 230 | 8.0312 | 0.0845 |
| 8.1094 | 0.0224 | 231 | 8.0234 | 0.0849 |
| 8.1016 | 0.0225 | 232 | 8.0156 | 0.0853 |
| 8.0859 | 0.0226 | 233 | 8.0078 | 0.0856 |
| 8.0859 | 0.0227 | 234 | 8.0078 | 0.0857 |
| 8.0781 | 0.0228 | 235 | 8.0 | 0.0857 |
| 8.0234 | 0.0229 | 236 | 7.9922 | 0.0856 |
| 8.0391 | 0.0230 | 237 | 7.9883 | 0.0855 |
| 8.0078 | 0.0231 | 238 | 7.9844 | 0.0855 |
| 8.0078 | 0.0232 | 239 | 7.9766 | 0.0857 |
| 7.9883 | 0.0232 | 240 | 7.9727 | 0.0862 |
| 7.9805 | 0.0233 | 241 | 7.9648 | 0.0865 |
| 8.0234 | 0.0234 | 242 | 7.9609 | 0.0868 |
| 7.9961 | 0.0235 | 243 | 7.9570 | 0.0870 |
| 8.0156 | 0.0236 | 244 | 7.9492 | 0.0870 |
| 7.9766 | 0.0237 | 245 | 7.9453 | 0.0869 |
| 7.9297 | 0.0238 | 246 | 7.9414 | 0.0866 |
| 7.9336 | 0.0239 | 247 | 7.9375 | 0.0865 |
| 7.9219 | 0.0240 | 248 | 7.9297 | 0.0866 |
| 7.957 | 0.0241 | 249 | 7.9258 | 0.0869 |
| 7.9453 | 0.0242 | 250 | 7.9180 | 0.0874 |
| 7.9805 | 0.0243 | 251 | 7.9141 | 0.0879 |
| 7.9531 | 0.0244 | 252 | 7.9102 | 0.0883 |
| 7.9102 | 0.0245 | 253 | 7.9062 | 0.0885 |
| 7.9844 | 0.0246 | 254 | 7.8984 | 0.0886 |
| 7.9414 | 0.0247 | 255 | 7.8945 | 0.0885 |
| 7.9453 | 0.0248 | 256 | 7.8906 | 0.0883 |
| 7.9219 | 0.0249 | 257 | 7.8867 | 0.0883 |
| 7.9141 | 0.0250 | 258 | 7.8828 | 0.0885 |
| 7.9258 | 0.0251 | 259 | 7.875 | 0.0889 |
| 7.957 | 0.0252 | 260 | 7.8711 | 0.0893 |
| 7.8984 | 0.0253 | 261 | 7.8672 | 0.0896 |
| 7.8945 | 0.0254 | 262 | 7.8633 | 0.0898 |
| 7.9141 | 0.0255 | 263 | 7.8594 | 0.0899 |
| 7.9453 | 0.0256 | 264 | 7.8555 | 0.0899 |
| 7.8672 | 0.0257 | 265 | 7.8477 | 0.0900 |
| 7.9375 | 0.0258 | 266 | 7.8438 | 0.0902 |
| 7.9219 | 0.0259 | 267 | 7.8398 | 0.0905 |
| 7.8555 | 0.0260 | 268 | 7.8359 | 0.0907 |
| 7.8984 | 0.0261 | 269 | 7.8320 | 0.0908 |
| 7.8906 | 0.0262 | 270 | 7.8281 | 0.0909 |
| 7.8711 | 0.0263 | 271 | 7.8242 | 0.0910 |
| 7.8633 | 0.0263 | 272 | 7.8203 | 0.0909 |
| 7.8633 | 0.0264 | 273 | 7.8164 | 0.0909 |
| 7.8789 | 0.0265 | 274 | 7.8125 | 0.0909 |
| 7.8438 | 0.0266 | 275 | 7.8086 | 0.0910 |
| 7.8789 | 0.0267 | 276 | 7.8047 | 0.0911 |
| 7.8516 | 0.0268 | 277 | 7.8008 | 0.0912 |
| 7.8711 | 0.0269 | 278 | 7.7969 | 0.0913 |
| 7.8008 | 0.0270 | 279 | 7.7930 | 0.0916 |
| 7.8477 | 0.0271 | 280 | 7.7891 | 0.0918 |
| 7.8086 | 0.0272 | 281 | 7.7852 | 0.0919 |
| 7.8398 | 0.0273 | 282 | 7.7812 | 0.0920 |
| 7.8008 | 0.0274 | 283 | 7.7773 | 0.0922 |
| 7.8281 | 0.0275 | 284 | 7.7734 | 0.0922 |
| 7.7852 | 0.0276 | 285 | 7.7695 | 0.0926 |
| 7.793 | 0.0277 | 286 | 7.7656 | 0.0929 |
| 7.8086 | 0.0278 | 287 | 7.7617 | 0.0931 |
| 7.7812 | 0.0279 | 288 | 7.7578 | 0.0931 |
| 7.793 | 0.0280 | 289 | 7.7539 | 0.0931 |
| 7.7539 | 0.0281 | 290 | 7.75 | 0.0931 |
| 7.75 | 0.0282 | 291 | 7.7461 | 0.0930 |
| 7.8164 | 0.0283 | 292 | 7.7422 | 0.0930 |
| 7.7539 | 0.0284 | 293 | 7.7422 | 0.0931 |
| 7.8086 | 0.0285 | 294 | 7.7383 | 0.0932 |
| 7.793 | 0.0286 | 295 | 7.7344 | 0.0936 |
| 7.7695 | 0.0287 | 296 | 7.7305 | 0.0937 |
| 7.75 | 0.0288 | 297 | 7.7266 | 0.0938 |
| 7.7891 | 0.0289 | 298 | 7.7227 | 0.0938 |
| 7.7773 | 0.0290 | 299 | 7.7188 | 0.0936 |
| 7.7227 | 0.0291 | 300 | 7.7148 | 0.0935 |
| 7.7109 | 0.0292 | 301 | 7.7148 | 0.0937 |
| 7.7148 | 0.0293 | 302 | 7.7109 | 0.0939 |
| 7.7812 | 0.0294 | 303 | 7.7070 | 0.0940 |
| 7.7109 | 0.0294 | 304 | 7.7031 | 0.0941 |
| 7.7539 | 0.0295 | 305 | 7.6992 | 0.0942 |
| 7.7734 | 0.0296 | 306 | 7.6992 | 0.0943 |
| 7.6914 | 0.0297 | 307 | 7.6953 | 0.0943 |
| 7.6445 | 0.0298 | 308 | 7.6914 | 0.0944 |
| 7.6953 | 0.0299 | 309 | 7.6875 | 0.0945 |
| 7.75 | 0.0300 | 310 | 7.6836 | 0.0946 |
| 7.7539 | 0.0301 | 311 | 7.6836 | 0.0949 |
| 7.6953 | 0.0302 | 312 | 7.6797 | 0.0951 |
| 7.7188 | 0.0303 | 313 | 7.6758 | 0.0951 |
| 7.6914 | 0.0304 | 314 | 7.6719 | 0.0953 |
| 7.7344 | 0.0305 | 315 | 7.6719 | 0.0954 |
| 7.7383 | 0.0306 | 316 | 7.6680 | 0.0953 |
| 7.6875 | 0.0307 | 317 | 7.6641 | 0.0950 |
| 7.6914 | 0.0308 | 318 | 7.6602 | 0.0947 |
| 7.6758 | 0.0309 | 319 | 7.6602 | 0.0945 |
| 7.6836 | 0.0310 | 320 | 7.6562 | 0.0947 |
| 7.6914 | 0.0311 | 321 | 7.6523 | 0.0950 |
| 7.6719 | 0.0312 | 322 | 7.6523 | 0.0954 |
| 7.6914 | 0.0313 | 323 | 7.6484 | 0.0958 |
| 7.6094 | 0.0314 | 324 | 7.6445 | 0.0961 |
| 7.7148 | 0.0315 | 325 | 7.6406 | 0.0962 |
| 7.6641 | 0.0316 | 326 | 7.6406 | 0.0961 |
| 7.6602 | 0.0317 | 327 | 7.6367 | 0.0961 |
| 7.7031 | 0.0318 | 328 | 7.6328 | 0.0963 |
| 7.6953 | 0.0319 | 329 | 7.6328 | 0.0966 |
| 7.6445 | 0.0320 | 330 | 7.6289 | 0.0968 |
| 7.6445 | 0.0321 | 331 | 7.625 | 0.0969 |
| 7.6445 | 0.0322 | 332 | 7.625 | 0.0969 |
| 7.668 | 0.0323 | 333 | 7.6211 | 0.0968 |
| 7.6523 | 0.0324 | 334 | 7.6172 | 0.0967 |
| 7.6602 | 0.0325 | 335 | 7.6172 | 0.0968 |
| 7.6328 | 0.0325 | 336 | 7.6133 | 0.0972 |
| 7.6523 | 0.0326 | 337 | 7.6094 | 0.0976 |
| 7.6133 | 0.0327 | 338 | 7.6094 | 0.0981 |
| 7.6367 | 0.0328 | 339 | 7.6055 | 0.0984 |
| 7.6641 | 0.0329 | 340 | 7.6016 | 0.0985 |
| 7.6367 | 0.0330 | 341 | 7.6016 | 0.0985 |
| 7.6133 | 0.0331 | 342 | 7.5977 | 0.0985 |
| 7.6016 | 0.0332 | 343 | 7.5977 | 0.0984 |
| 7.668 | 0.0333 | 344 | 7.5938 | 0.0984 |
| 7.6172 | 0.0334 | 345 | 7.5898 | 0.0984 |
| 7.6016 | 0.0335 | 346 | 7.5898 | 0.0985 |
| 7.6328 | 0.0336 | 347 | 7.5859 | 0.0985 |
| 7.668 | 0.0337 | 348 | 7.5820 | 0.0986 |
| 7.6719 | 0.0338 | 349 | 7.5820 | 0.0987 |
| 7.6602 | 0.0339 | 350 | 7.5781 | 0.0989 |
| 7.6641 | 0.0340 | 351 | 7.5742 | 0.0992 |
| 7.6445 | 0.0341 | 352 | 7.5742 | 0.0994 |
| 7.5781 | 0.0342 | 353 | 7.5703 | 0.0995 |
| 7.6523 | 0.0343 | 354 | 7.5703 | 0.0996 |
| 7.6562 | 0.0344 | 355 | 7.5664 | 0.0996 |
| 7.5977 | 0.0345 | 356 | 7.5664 | 0.0998 |
| 7.5977 | 0.0346 | 357 | 7.5625 | 0.0998 |
| 7.5508 | 0.0347 | 358 | 7.5625 | 0.0997 |
| 7.6172 | 0.0348 | 359 | 7.5586 | 0.0997 |
| 7.5469 | 0.0349 | 360 | 7.5547 | 0.0997 |
| 7.6172 | 0.0350 | 361 | 7.5547 | 0.0997 |
| 7.625 | 0.0351 | 362 | 7.5508 | 0.0998 |
| 7.6289 | 0.0352 | 363 | 7.5508 | 0.0999 |
| 7.5234 | 0.0353 | 364 | 7.5469 | 0.1002 |
| 7.5703 | 0.0354 | 365 | 7.5430 | 0.1006 |
| 7.5859 | 0.0355 | 366 | 7.5430 | 0.1010 |
| 7.5469 | 0.0356 | 367 | 7.5391 | 0.1014 |
| 7.5508 | 0.0356 | 368 | 7.5391 | 0.1016 |
| 7.6172 | 0.0357 | 369 | 7.5352 | 0.1017 |
| 7.6172 | 0.0358 | 370 | 7.5352 | 0.1017 |
| 7.5352 | 0.0359 | 371 | 7.5312 | 0.1018 |
| 7.5859 | 0.0360 | 372 | 7.5312 | 0.1018 |
| 7.5586 | 0.0361 | 373 | 7.5273 | 0.1017 |
| 7.6406 | 0.0362 | 374 | 7.5273 | 0.1017 |
| 7.5273 | 0.0363 | 375 | 7.5234 | 0.1018 |
| 7.5312 | 0.0364 | 376 | 7.5195 | 0.1020 |
| 7.5898 | 0.0365 | 377 | 7.5195 | 0.1023 |
| 7.5898 | 0.0366 | 378 | 7.5156 | 0.1027 |
| 7.543 | 0.0367 | 379 | 7.5156 | 0.1029 |
| 7.5156 | 0.0368 | 380 | 7.5117 | 0.1030 |
| 7.5664 | 0.0369 | 381 | 7.5117 | 0.1031 |
| 7.5625 | 0.0370 | 382 | 7.5078 | 0.1031 |
| 7.5312 | 0.0371 | 383 | 7.5078 | 0.1032 |
| 7.625 | 0.0372 | 384 | 7.5078 | 0.1032 |
| 7.5898 | 0.0373 | 385 | 7.5039 | 0.1034 |
| 7.5625 | 0.0374 | 386 | 7.5 | 0.1035 |
| 7.5664 | 0.0375 | 387 | 7.5 | 0.1037 |
| 7.4609 | 0.0376 | 388 | 7.4961 | 0.1039 |
| 7.5469 | 0.0377 | 389 | 7.4961 | 0.1040 |
| 7.5742 | 0.0378 | 390 | 7.4922 | 0.1040 |
| 7.4375 | 0.0379 | 391 | 7.4922 | 0.1040 |
| 7.4961 | 0.0380 | 392 | 7.4883 | 0.1039 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_12_384 | gokulsrinivasagan | 2024-07-02T16:04:42Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:54:24Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_12_384
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.10244681503029747
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_12_384
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 8.8125
- Accuracy: 0.1024
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.8984 | 0.0000 | 1 | 10.9062 | 0.0001 |
| 10.8984 | 0.0001 | 2 | 10.9062 | 0.0001 |
| 10.8984 | 0.0001 | 3 | 10.9062 | 0.0001 |
| 10.8984 | 0.0002 | 4 | 10.9062 | 0.0001 |
| 10.9062 | 0.0002 | 5 | 10.9062 | 0.0001 |
| 10.8984 | 0.0003 | 6 | 10.9062 | 0.0001 |
| 10.9062 | 0.0003 | 7 | 10.9062 | 0.0001 |
| 10.9062 | 0.0004 | 8 | 10.9062 | 0.0001 |
| 10.9062 | 0.0004 | 9 | 10.9062 | 0.0001 |
| 10.8984 | 0.0005 | 10 | 10.9062 | 0.0001 |
| 10.8984 | 0.0005 | 11 | 10.9062 | 0.0001 |
| 10.8984 | 0.0006 | 12 | 10.9062 | 0.0001 |
| 10.8984 | 0.0006 | 13 | 10.9062 | 0.0001 |
| 10.9062 | 0.0007 | 14 | 10.9062 | 0.0001 |
| 10.8984 | 0.0007 | 15 | 10.9062 | 0.0001 |
| 10.8984 | 0.0008 | 16 | 10.9062 | 0.0001 |
| 10.9062 | 0.0008 | 17 | 10.9062 | 0.0001 |
| 10.9062 | 0.0009 | 18 | 10.7578 | 0.0110 |
| 10.7734 | 0.0009 | 19 | 10.6562 | 0.0285 |
| 10.6797 | 0.0010 | 20 | 10.5781 | 0.0469 |
| 10.6016 | 0.0010 | 21 | 10.5234 | 0.0485 |
| 10.5234 | 0.0011 | 22 | 10.4766 | 0.0478 |
| 10.5 | 0.0011 | 23 | 10.4375 | 0.0483 |
| 10.4531 | 0.0012 | 24 | 10.4062 | 0.0507 |
| 10.4141 | 0.0012 | 25 | 10.3828 | 0.0531 |
| 10.3672 | 0.0013 | 26 | 10.3594 | 0.0556 |
| 10.3828 | 0.0013 | 27 | 10.3359 | 0.0562 |
| 10.3594 | 0.0014 | 28 | 10.3203 | 0.0562 |
| 10.3281 | 0.0014 | 29 | 10.3047 | 0.0559 |
| 10.3203 | 0.0015 | 30 | 10.2969 | 0.0563 |
| 10.3281 | 0.0015 | 31 | 10.2812 | 0.0566 |
| 10.3359 | 0.0015 | 32 | 10.2734 | 0.0566 |
| 10.2656 | 0.0016 | 33 | 10.2656 | 0.0570 |
| 10.2656 | 0.0016 | 34 | 10.2578 | 0.0561 |
| 10.2656 | 0.0017 | 35 | 10.2422 | 0.0562 |
| 10.2656 | 0.0017 | 36 | 10.2344 | 0.0575 |
| 10.2656 | 0.0018 | 37 | 10.2266 | 0.0586 |
| 10.2109 | 0.0018 | 38 | 10.2188 | 0.0593 |
| 10.2656 | 0.0019 | 39 | 10.2109 | 0.0596 |
| 10.2266 | 0.0019 | 40 | 10.2031 | 0.0599 |
| 10.2109 | 0.0020 | 41 | 10.1953 | 0.0601 |
| 10.2109 | 0.0020 | 42 | 10.1797 | 0.0604 |
| 10.2109 | 0.0021 | 43 | 10.1719 | 0.0608 |
| 10.1484 | 0.0021 | 44 | 10.1641 | 0.0610 |
| 10.1875 | 0.0022 | 45 | 10.1484 | 0.0611 |
| 10.1719 | 0.0022 | 46 | 10.1406 | 0.0612 |
| 10.1484 | 0.0023 | 47 | 10.1328 | 0.0615 |
| 10.1172 | 0.0023 | 48 | 10.1172 | 0.0622 |
| 10.1797 | 0.0024 | 49 | 10.1094 | 0.0632 |
| 10.1016 | 0.0024 | 50 | 10.1016 | 0.0642 |
| 10.1406 | 0.0025 | 51 | 10.0938 | 0.0651 |
| 10.1406 | 0.0025 | 52 | 10.0859 | 0.0658 |
| 10.1094 | 0.0026 | 53 | 10.0781 | 0.0663 |
| 10.1016 | 0.0026 | 54 | 10.0703 | 0.0669 |
| 10.0781 | 0.0027 | 55 | 10.0625 | 0.0672 |
| 10.0703 | 0.0027 | 56 | 10.0547 | 0.0678 |
| 10.0703 | 0.0028 | 57 | 10.0469 | 0.0681 |
| 10.0469 | 0.0028 | 58 | 10.0391 | 0.0686 |
| 10.1016 | 0.0029 | 59 | 10.0312 | 0.0689 |
| 10.0547 | 0.0029 | 60 | 10.0312 | 0.0694 |
| 10.0391 | 0.0030 | 61 | 10.0234 | 0.0695 |
| 10.0547 | 0.0030 | 62 | 10.0156 | 0.0692 |
| 10.0312 | 0.0031 | 63 | 10.0078 | 0.0688 |
| 10.0547 | 0.0031 | 64 | 10.0 | 0.0687 |
| 10.0547 | 0.0031 | 65 | 9.9922 | 0.0693 |
| 9.9922 | 0.0032 | 66 | 9.9844 | 0.0697 |
| 10.0234 | 0.0032 | 67 | 9.9766 | 0.0705 |
| 10.0 | 0.0033 | 68 | 9.9688 | 0.0711 |
| 10.0 | 0.0033 | 69 | 9.9609 | 0.0715 |
| 9.9688 | 0.0034 | 70 | 9.9609 | 0.0716 |
| 9.9922 | 0.0034 | 71 | 9.9531 | 0.0717 |
| 9.9844 | 0.0035 | 72 | 9.9453 | 0.0716 |
| 9.9688 | 0.0035 | 73 | 9.9375 | 0.0718 |
| 9.9453 | 0.0036 | 74 | 9.9297 | 0.0726 |
| 9.9375 | 0.0036 | 75 | 9.9219 | 0.0734 |
| 9.9141 | 0.0037 | 76 | 9.9141 | 0.0744 |
| 9.9062 | 0.0037 | 77 | 9.9062 | 0.0751 |
| 9.9219 | 0.0038 | 78 | 9.9062 | 0.0755 |
| 9.9219 | 0.0038 | 79 | 9.8984 | 0.0756 |
| 9.9219 | 0.0039 | 80 | 9.8906 | 0.0757 |
| 9.875 | 0.0039 | 81 | 9.8828 | 0.0759 |
| 9.9219 | 0.0040 | 82 | 9.875 | 0.0760 |
| 9.875 | 0.0040 | 83 | 9.875 | 0.0763 |
| 9.8672 | 0.0041 | 84 | 9.8672 | 0.0765 |
| 9.9062 | 0.0041 | 85 | 9.8594 | 0.0769 |
| 9.8828 | 0.0042 | 86 | 9.8516 | 0.0773 |
| 9.8594 | 0.0042 | 87 | 9.8516 | 0.0775 |
| 9.8906 | 0.0043 | 88 | 9.8438 | 0.0777 |
| 9.8047 | 0.0043 | 89 | 9.8359 | 0.0777 |
| 9.8203 | 0.0044 | 90 | 9.8359 | 0.0778 |
| 9.8594 | 0.0044 | 91 | 9.8281 | 0.0781 |
| 9.8438 | 0.0045 | 92 | 9.8203 | 0.0786 |
| 9.8438 | 0.0045 | 93 | 9.8203 | 0.0790 |
| 9.8438 | 0.0046 | 94 | 9.8125 | 0.0793 |
| 9.8359 | 0.0046 | 95 | 9.8047 | 0.0794 |
| 9.8281 | 0.0046 | 96 | 9.8047 | 0.0795 |
| 9.8516 | 0.0047 | 97 | 9.7969 | 0.0796 |
| 9.8281 | 0.0047 | 98 | 9.7891 | 0.0797 |
| 9.7734 | 0.0048 | 99 | 9.7891 | 0.0798 |
| 9.8125 | 0.0048 | 100 | 9.7812 | 0.0802 |
| 9.8203 | 0.0049 | 101 | 9.7734 | 0.0806 |
| 9.8281 | 0.0049 | 102 | 9.7734 | 0.0809 |
| 9.7734 | 0.0050 | 103 | 9.7656 | 0.0811 |
| 9.7891 | 0.0050 | 104 | 9.7578 | 0.0813 |
| 9.8047 | 0.0051 | 105 | 9.7578 | 0.0814 |
| 9.7578 | 0.0051 | 106 | 9.75 | 0.0815 |
| 9.7734 | 0.0052 | 107 | 9.75 | 0.0816 |
| 9.7891 | 0.0052 | 108 | 9.7422 | 0.0818 |
| 9.75 | 0.0053 | 109 | 9.7344 | 0.0819 |
| 9.75 | 0.0053 | 110 | 9.7344 | 0.0821 |
| 9.7266 | 0.0054 | 111 | 9.7266 | 0.0823 |
| 9.7656 | 0.0054 | 112 | 9.7188 | 0.0824 |
| 9.7812 | 0.0055 | 113 | 9.7188 | 0.0824 |
| 9.7734 | 0.0055 | 114 | 9.7109 | 0.0824 |
| 9.7266 | 0.0056 | 115 | 9.7109 | 0.0824 |
| 9.7266 | 0.0056 | 116 | 9.7031 | 0.0826 |
| 9.7109 | 0.0057 | 117 | 9.6953 | 0.0828 |
| 9.6719 | 0.0057 | 118 | 9.6953 | 0.0829 |
| 9.6953 | 0.0058 | 119 | 9.6875 | 0.0830 |
| 9.6719 | 0.0058 | 120 | 9.6875 | 0.0831 |
| 9.6953 | 0.0059 | 121 | 9.6797 | 0.0831 |
| 9.6875 | 0.0059 | 122 | 9.6797 | 0.0831 |
| 9.6719 | 0.0060 | 123 | 9.6719 | 0.0832 |
| 9.6719 | 0.0060 | 124 | 9.6641 | 0.0833 |
| 9.625 | 0.0061 | 125 | 9.6641 | 0.0833 |
| 9.6719 | 0.0061 | 126 | 9.6562 | 0.0834 |
| 9.6953 | 0.0062 | 127 | 9.6562 | 0.0836 |
| 9.6719 | 0.0062 | 128 | 9.6484 | 0.0837 |
| 9.6797 | 0.0062 | 129 | 9.6406 | 0.0838 |
| 9.6484 | 0.0063 | 130 | 9.6406 | 0.0839 |
| 9.6719 | 0.0063 | 131 | 9.6328 | 0.0839 |
| 9.6328 | 0.0064 | 132 | 9.6328 | 0.0839 |
| 9.6719 | 0.0064 | 133 | 9.625 | 0.0839 |
| 9.6484 | 0.0065 | 134 | 9.6172 | 0.0840 |
| 9.6406 | 0.0065 | 135 | 9.6172 | 0.0841 |
| 9.6094 | 0.0066 | 136 | 9.6094 | 0.0843 |
| 9.625 | 0.0066 | 137 | 9.6094 | 0.0845 |
| 9.6562 | 0.0067 | 138 | 9.6016 | 0.0846 |
| 9.6172 | 0.0067 | 139 | 9.6016 | 0.0847 |
| 9.6094 | 0.0068 | 140 | 9.5938 | 0.0847 |
| 9.6562 | 0.0068 | 141 | 9.5859 | 0.0847 |
| 9.6562 | 0.0069 | 142 | 9.5859 | 0.0847 |
| 9.6562 | 0.0069 | 143 | 9.5781 | 0.0848 |
| 9.6016 | 0.0070 | 144 | 9.5781 | 0.0849 |
| 9.6094 | 0.0070 | 145 | 9.5703 | 0.0850 |
| 9.5938 | 0.0071 | 146 | 9.5703 | 0.0851 |
| 9.5703 | 0.0071 | 147 | 9.5625 | 0.0851 |
| 9.5859 | 0.0072 | 148 | 9.5625 | 0.0851 |
| 9.625 | 0.0072 | 149 | 9.5547 | 0.0852 |
| 9.5859 | 0.0073 | 150 | 9.5469 | 0.0854 |
| 9.5625 | 0.0073 | 151 | 9.5469 | 0.0855 |
| 9.5547 | 0.0074 | 152 | 9.5391 | 0.0856 |
| 9.5703 | 0.0074 | 153 | 9.5391 | 0.0858 |
| 9.5391 | 0.0075 | 154 | 9.5312 | 0.0858 |
| 9.5391 | 0.0075 | 155 | 9.5312 | 0.0859 |
| 9.5 | 0.0076 | 156 | 9.5234 | 0.0861 |
| 9.5547 | 0.0076 | 157 | 9.5156 | 0.0863 |
| 9.5391 | 0.0077 | 158 | 9.5156 | 0.0863 |
| 9.5312 | 0.0077 | 159 | 9.5156 | 0.0864 |
| 9.5391 | 0.0077 | 160 | 9.5078 | 0.0864 |
| 9.4688 | 0.0078 | 161 | 9.5 | 0.0866 |
| 9.5547 | 0.0078 | 162 | 9.5 | 0.0867 |
| 9.5078 | 0.0079 | 163 | 9.4922 | 0.0869 |
| 9.5078 | 0.0079 | 164 | 9.4922 | 0.0870 |
| 9.5 | 0.0080 | 165 | 9.4844 | 0.0872 |
| 9.5312 | 0.0080 | 166 | 9.4844 | 0.0875 |
| 9.5156 | 0.0081 | 167 | 9.4766 | 0.0877 |
| 9.4844 | 0.0081 | 168 | 9.4766 | 0.0878 |
| 9.4688 | 0.0082 | 169 | 9.4688 | 0.0878 |
| 9.5156 | 0.0082 | 170 | 9.4609 | 0.0879 |
| 9.4922 | 0.0083 | 171 | 9.4609 | 0.0879 |
| 9.4844 | 0.0083 | 172 | 9.4531 | 0.0878 |
| 9.5234 | 0.0084 | 173 | 9.4531 | 0.0879 |
| 9.4844 | 0.0084 | 174 | 9.4453 | 0.0879 |
| 9.4219 | 0.0085 | 175 | 9.4453 | 0.0880 |
| 9.4062 | 0.0085 | 176 | 9.4375 | 0.0881 |
| 9.4375 | 0.0086 | 177 | 9.4375 | 0.0883 |
| 9.4375 | 0.0086 | 178 | 9.4297 | 0.0885 |
| 9.4688 | 0.0087 | 179 | 9.4297 | 0.0887 |
| 9.4453 | 0.0087 | 180 | 9.4219 | 0.0888 |
| 9.4219 | 0.0088 | 181 | 9.4219 | 0.0890 |
| 9.4141 | 0.0088 | 182 | 9.4141 | 0.0890 |
| 9.4375 | 0.0089 | 183 | 9.4062 | 0.0890 |
| 9.3984 | 0.0089 | 184 | 9.4062 | 0.0890 |
| 9.4297 | 0.0090 | 185 | 9.3984 | 0.0891 |
| 9.3984 | 0.0090 | 186 | 9.3984 | 0.0891 |
| 9.3906 | 0.0091 | 187 | 9.3906 | 0.0892 |
| 9.4219 | 0.0091 | 188 | 9.3906 | 0.0893 |
| 9.4062 | 0.0092 | 189 | 9.3828 | 0.0895 |
| 9.375 | 0.0092 | 190 | 9.3828 | 0.0897 |
| 9.3828 | 0.0093 | 191 | 9.375 | 0.0898 |
| 9.3906 | 0.0093 | 192 | 9.375 | 0.0898 |
| 9.3906 | 0.0093 | 193 | 9.3672 | 0.0899 |
| 9.4141 | 0.0094 | 194 | 9.3672 | 0.0898 |
| 9.3203 | 0.0094 | 195 | 9.3594 | 0.0898 |
| 9.3906 | 0.0095 | 196 | 9.3594 | 0.0898 |
| 9.3594 | 0.0095 | 197 | 9.3516 | 0.0900 |
| 9.3516 | 0.0096 | 198 | 9.3516 | 0.0901 |
| 9.3438 | 0.0096 | 199 | 9.3438 | 0.0902 |
| 9.3516 | 0.0097 | 200 | 9.3438 | 0.0904 |
| 9.3125 | 0.0097 | 201 | 9.3359 | 0.0906 |
| 9.3516 | 0.0098 | 202 | 9.3359 | 0.0907 |
| 9.3359 | 0.0098 | 203 | 9.3281 | 0.0908 |
| 9.3516 | 0.0099 | 204 | 9.3281 | 0.0907 |
| 9.3281 | 0.0099 | 205 | 9.3203 | 0.0906 |
| 9.375 | 0.0100 | 206 | 9.3125 | 0.0905 |
| 9.2812 | 0.0100 | 207 | 9.3125 | 0.0904 |
| 9.3281 | 0.0101 | 208 | 9.3047 | 0.0906 |
| 9.3281 | 0.0101 | 209 | 9.3047 | 0.0908 |
| 9.3594 | 0.0102 | 210 | 9.2969 | 0.0912 |
| 9.3438 | 0.0102 | 211 | 9.2969 | 0.0915 |
| 9.2891 | 0.0103 | 212 | 9.2891 | 0.0916 |
| 9.3438 | 0.0103 | 213 | 9.2891 | 0.0916 |
| 9.3047 | 0.0104 | 214 | 9.2812 | 0.0915 |
| 9.2656 | 0.0104 | 215 | 9.2812 | 0.0914 |
| 9.2734 | 0.0105 | 216 | 9.2734 | 0.0913 |
| 9.2891 | 0.0105 | 217 | 9.2734 | 0.0913 |
| 9.2969 | 0.0106 | 218 | 9.2656 | 0.0913 |
| 9.25 | 0.0106 | 219 | 9.2656 | 0.0914 |
| 9.2578 | 0.0107 | 220 | 9.2578 | 0.0915 |
| 9.25 | 0.0107 | 221 | 9.2578 | 0.0916 |
| 9.2656 | 0.0108 | 222 | 9.25 | 0.0920 |
| 9.2578 | 0.0108 | 223 | 9.25 | 0.0923 |
| 9.2734 | 0.0108 | 224 | 9.2422 | 0.0926 |
| 9.2891 | 0.0109 | 225 | 9.2422 | 0.0929 |
| 9.25 | 0.0109 | 226 | 9.2344 | 0.0928 |
| 9.2344 | 0.0110 | 227 | 9.2344 | 0.0928 |
| 9.2656 | 0.0110 | 228 | 9.2266 | 0.0927 |
| 9.2656 | 0.0111 | 229 | 9.2266 | 0.0928 |
| 9.2656 | 0.0111 | 230 | 9.2188 | 0.0930 |
| 9.25 | 0.0112 | 231 | 9.2188 | 0.0933 |
| 9.2891 | 0.0112 | 232 | 9.2109 | 0.0937 |
| 9.2188 | 0.0113 | 233 | 9.2031 | 0.0938 |
| 9.2578 | 0.0113 | 234 | 9.2031 | 0.0939 |
| 9.2422 | 0.0114 | 235 | 9.1953 | 0.0938 |
| 9.2109 | 0.0114 | 236 | 9.1953 | 0.0935 |
| 9.1797 | 0.0115 | 237 | 9.1953 | 0.0935 |
| 9.1953 | 0.0115 | 238 | 9.1875 | 0.0938 |
| 9.1797 | 0.0116 | 239 | 9.1875 | 0.0943 |
| 9.2266 | 0.0116 | 240 | 9.1797 | 0.0948 |
| 9.2109 | 0.0117 | 241 | 9.1719 | 0.0951 |
| 9.1719 | 0.0117 | 242 | 9.1719 | 0.0954 |
| 9.2031 | 0.0118 | 243 | 9.1719 | 0.0955 |
| 9.1953 | 0.0118 | 244 | 9.1641 | 0.0954 |
| 9.1875 | 0.0119 | 245 | 9.1641 | 0.0950 |
| 9.2031 | 0.0119 | 246 | 9.1562 | 0.0949 |
| 9.1797 | 0.0120 | 247 | 9.1484 | 0.0950 |
| 9.1484 | 0.0120 | 248 | 9.1484 | 0.0952 |
| 9.1406 | 0.0121 | 249 | 9.1484 | 0.0954 |
| 9.1641 | 0.0121 | 250 | 9.1406 | 0.0956 |
| 9.1406 | 0.0122 | 251 | 9.1406 | 0.0956 |
| 9.1719 | 0.0122 | 252 | 9.1328 | 0.0954 |
| 9.125 | 0.0123 | 253 | 9.1328 | 0.0953 |
| 9.1719 | 0.0123 | 254 | 9.125 | 0.0950 |
| 9.1797 | 0.0124 | 255 | 9.125 | 0.0950 |
| 9.0859 | 0.0124 | 256 | 9.1172 | 0.0951 |
| 9.1875 | 0.0124 | 257 | 9.1172 | 0.0957 |
| 9.1094 | 0.0125 | 258 | 9.1094 | 0.0963 |
| 9.0938 | 0.0125 | 259 | 9.1094 | 0.0968 |
| 9.1016 | 0.0126 | 260 | 9.1016 | 0.0969 |
| 9.1406 | 0.0126 | 261 | 9.1016 | 0.0969 |
| 9.0781 | 0.0127 | 262 | 9.0938 | 0.0966 |
| 9.1094 | 0.0127 | 263 | 9.0938 | 0.0963 |
| 9.1172 | 0.0128 | 264 | 9.0859 | 0.0959 |
| 9.1172 | 0.0128 | 265 | 9.0859 | 0.0956 |
| 9.125 | 0.0129 | 266 | 9.0859 | 0.0955 |
| 9.1094 | 0.0129 | 267 | 9.0781 | 0.0957 |
| 9.0781 | 0.0130 | 268 | 9.0781 | 0.0964 |
| 9.125 | 0.0130 | 269 | 9.0703 | 0.0973 |
| 9.0547 | 0.0131 | 270 | 9.0703 | 0.0980 |
| 9.0781 | 0.0131 | 271 | 9.0625 | 0.0983 |
| 9.1016 | 0.0132 | 272 | 9.0625 | 0.0981 |
| 9.0703 | 0.0132 | 273 | 9.0547 | 0.0975 |
| 9.0547 | 0.0133 | 274 | 9.0547 | 0.0969 |
| 9.0312 | 0.0133 | 275 | 9.0469 | 0.0964 |
| 9.0938 | 0.0134 | 276 | 9.0469 | 0.0964 |
| 9.0156 | 0.0134 | 277 | 9.0391 | 0.0967 |
| 9.1094 | 0.0135 | 278 | 9.0391 | 0.0973 |
| 9.0859 | 0.0135 | 279 | 9.0312 | 0.0980 |
| 9.0234 | 0.0136 | 280 | 9.0312 | 0.0984 |
| 9.0781 | 0.0136 | 281 | 9.0234 | 0.0984 |
| 9.0547 | 0.0137 | 282 | 9.0234 | 0.0983 |
| 9.0234 | 0.0137 | 283 | 9.0156 | 0.0979 |
| 9.0312 | 0.0138 | 284 | 9.0156 | 0.0978 |
| 9.0391 | 0.0138 | 285 | 9.0078 | 0.0978 |
| 9.0312 | 0.0139 | 286 | 9.0078 | 0.0980 |
| 9.0625 | 0.0139 | 287 | 9.0078 | 0.0982 |
| 9.0234 | 0.0139 | 288 | 9.0 | 0.0986 |
| 9.0078 | 0.0140 | 289 | 9.0 | 0.0990 |
| 9.0 | 0.0140 | 290 | 8.9922 | 0.0996 |
| 9.0078 | 0.0141 | 291 | 8.9922 | 0.0997 |
| 9.0 | 0.0141 | 292 | 8.9844 | 0.0999 |
| 9.0078 | 0.0142 | 293 | 8.9844 | 0.0999 |
| 8.9922 | 0.0142 | 294 | 8.9766 | 0.0995 |
| 9.0078 | 0.0143 | 295 | 8.9766 | 0.0990 |
| 8.9844 | 0.0143 | 296 | 8.9688 | 0.0985 |
| 8.9766 | 0.0144 | 297 | 8.9688 | 0.0983 |
| 8.9531 | 0.0144 | 298 | 8.9609 | 0.0985 |
| 8.9688 | 0.0145 | 299 | 8.9609 | 0.0988 |
| 9.0312 | 0.0145 | 300 | 8.9531 | 0.0994 |
| 9.0156 | 0.0146 | 301 | 8.9531 | 0.0998 |
| 8.9688 | 0.0146 | 302 | 8.9453 | 0.0999 |
| 9.0 | 0.0147 | 303 | 8.9453 | 0.0997 |
| 8.9375 | 0.0147 | 304 | 8.9375 | 0.0996 |
| 8.9766 | 0.0148 | 305 | 8.9375 | 0.0994 |
| 8.9375 | 0.0148 | 306 | 8.9375 | 0.0994 |
| 8.9688 | 0.0149 | 307 | 8.9297 | 0.0997 |
| 8.9531 | 0.0149 | 308 | 8.9297 | 0.0999 |
| 8.9531 | 0.0150 | 309 | 8.9219 | 0.1002 |
| 8.9062 | 0.0150 | 310 | 8.9219 | 0.1003 |
| 8.9375 | 0.0151 | 311 | 8.9141 | 0.1004 |
| 8.8828 | 0.0151 | 312 | 8.9141 | 0.1003 |
| 8.9219 | 0.0152 | 313 | 8.9062 | 0.1003 |
| 8.9219 | 0.0152 | 314 | 8.9062 | 0.1004 |
| 8.9297 | 0.0153 | 315 | 8.9062 | 0.1009 |
| 8.9922 | 0.0153 | 316 | 8.8984 | 0.1011 |
| 8.9062 | 0.0154 | 317 | 8.8984 | 0.1011 |
| 8.9297 | 0.0154 | 318 | 8.8906 | 0.1011 |
| 8.9531 | 0.0155 | 319 | 8.8906 | 0.1008 |
| 8.9531 | 0.0155 | 320 | 8.8828 | 0.1006 |
| 8.9375 | 0.0155 | 321 | 8.8828 | 0.1004 |
| 8.9219 | 0.0156 | 322 | 8.875 | 0.1002 |
| 8.9062 | 0.0156 | 323 | 8.875 | 0.1004 |
| 8.8906 | 0.0157 | 324 | 8.875 | 0.1006 |
| 8.8906 | 0.0157 | 325 | 8.8672 | 0.1011 |
| 8.8672 | 0.0158 | 326 | 8.8672 | 0.1016 |
| 8.875 | 0.0158 | 327 | 8.8594 | 0.1019 |
| 8.8516 | 0.0159 | 328 | 8.8594 | 0.1022 |
| 8.8672 | 0.0159 | 329 | 8.8516 | 0.1020 |
| 8.8984 | 0.0160 | 330 | 8.8516 | 0.1018 |
| 8.875 | 0.0160 | 331 | 8.8438 | 0.1016 |
| 8.8828 | 0.0161 | 332 | 8.8438 | 0.1014 |
| 8.8438 | 0.0161 | 333 | 8.8359 | 0.1014 |
| 8.7969 | 0.0162 | 334 | 8.8359 | 0.1017 |
| 8.8828 | 0.0162 | 335 | 8.8281 | 0.1020 |
| 8.8281 | 0.0163 | 336 | 8.8281 | 0.1025 |
| 8.8203 | 0.0163 | 337 | 8.8281 | 0.1027 |
| 8.8594 | 0.0164 | 338 | 8.8203 | 0.1028 |
| 8.8594 | 0.0164 | 339 | 8.8203 | 0.1027 |
| 8.8203 | 0.0165 | 340 | 8.8125 | 0.1025 |
| 8.8359 | 0.0165 | 341 | 8.8125 | 0.1024 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_6_768 | gokulsrinivasagan | 2024-07-02T16:09:16Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:54:47Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_6_768
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.11184750327853396
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_6_768
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 7.6211
- Accuracy: 0.1118
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 32
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.9531 | 0.0000 | 1 | 10.9609 | 0.0000 |
| 10.9453 | 0.0001 | 2 | 10.9609 | 0.0000 |
| 10.9609 | 0.0001 | 3 | 10.9609 | 0.0000 |
| 10.9531 | 0.0002 | 4 | 10.9609 | 0.0000 |
| 10.9531 | 0.0002 | 5 | 10.9609 | 0.0000 |
| 10.9609 | 0.0003 | 6 | 10.9609 | 0.0000 |
| 10.9531 | 0.0003 | 7 | 10.9609 | 0.0000 |
| 10.9531 | 0.0004 | 8 | 10.9609 | 0.0000 |
| 10.9609 | 0.0004 | 9 | 10.9609 | 0.0000 |
| 10.9609 | 0.0005 | 10 | 10.9609 | 0.0000 |
| 10.9609 | 0.0005 | 11 | 10.9609 | 0.0000 |
| 10.9609 | 0.0006 | 12 | 10.9609 | 0.0000 |
| 10.9609 | 0.0006 | 13 | 10.9609 | 0.0000 |
| 10.9531 | 0.0007 | 14 | 10.9609 | 0.0000 |
| 10.9531 | 0.0007 | 15 | 10.9609 | 0.0000 |
| 10.9531 | 0.0008 | 16 | 10.9609 | 0.0000 |
| 10.9609 | 0.0008 | 17 | 10.9609 | 0.0000 |
| 10.9609 | 0.0009 | 18 | 10.7188 | 0.0158 |
| 10.7422 | 0.0009 | 19 | 10.5078 | 0.0392 |
| 10.5391 | 0.0010 | 20 | 10.3516 | 0.0403 |
| 10.4219 | 0.0010 | 21 | 10.2422 | 0.0403 |
| 10.2656 | 0.0011 | 22 | 10.1484 | 0.0401 |
| 10.2109 | 0.0011 | 23 | 10.0703 | 0.0403 |
| 10.125 | 0.0012 | 24 | 10.0078 | 0.0420 |
| 10.0312 | 0.0012 | 25 | 9.9531 | 0.0449 |
| 9.9766 | 0.0013 | 26 | 9.9062 | 0.0489 |
| 9.9844 | 0.0013 | 27 | 9.8672 | 0.0504 |
| 9.9062 | 0.0014 | 28 | 9.8359 | 0.0508 |
| 9.875 | 0.0014 | 29 | 9.8047 | 0.0506 |
| 9.8359 | 0.0015 | 30 | 9.7812 | 0.0511 |
| 9.8516 | 0.0015 | 31 | 9.75 | 0.0513 |
| 9.875 | 0.0015 | 32 | 9.7344 | 0.0511 |
| 9.7109 | 0.0016 | 33 | 9.7109 | 0.0514 |
| 9.7266 | 0.0016 | 34 | 9.6953 | 0.0506 |
| 9.7344 | 0.0017 | 35 | 9.6797 | 0.0508 |
| 9.7344 | 0.0017 | 36 | 9.6641 | 0.0524 |
| 9.7422 | 0.0018 | 37 | 9.6484 | 0.0543 |
| 9.6094 | 0.0018 | 38 | 9.6328 | 0.0555 |
| 9.7188 | 0.0019 | 39 | 9.625 | 0.0562 |
| 9.6484 | 0.0019 | 40 | 9.6094 | 0.0572 |
| 9.6641 | 0.0020 | 41 | 9.6016 | 0.0573 |
| 9.6562 | 0.0020 | 42 | 9.5859 | 0.0573 |
| 9.6406 | 0.0021 | 43 | 9.5781 | 0.0590 |
| 9.5234 | 0.0021 | 44 | 9.5625 | 0.0605 |
| 9.5938 | 0.0022 | 45 | 9.5547 | 0.0615 |
| 9.5859 | 0.0022 | 46 | 9.5391 | 0.0623 |
| 9.5703 | 0.0023 | 47 | 9.5312 | 0.0626 |
| 9.5078 | 0.0023 | 48 | 9.5156 | 0.0627 |
| 9.6484 | 0.0024 | 49 | 9.5078 | 0.0628 |
| 9.4922 | 0.0024 | 50 | 9.4922 | 0.0629 |
| 9.5391 | 0.0025 | 51 | 9.4844 | 0.0633 |
| 9.5859 | 0.0025 | 52 | 9.4688 | 0.0639 |
| 9.5234 | 0.0026 | 53 | 9.4609 | 0.0647 |
| 9.5 | 0.0026 | 54 | 9.4453 | 0.0657 |
| 9.4609 | 0.0027 | 55 | 9.4375 | 0.0666 |
| 9.4531 | 0.0027 | 56 | 9.4219 | 0.0677 |
| 9.4375 | 0.0028 | 57 | 9.4141 | 0.0681 |
| 9.4141 | 0.0028 | 58 | 9.3984 | 0.0683 |
| 9.4844 | 0.0029 | 59 | 9.3906 | 0.0683 |
| 9.4297 | 0.0029 | 60 | 9.3828 | 0.0691 |
| 9.375 | 0.0030 | 61 | 9.3672 | 0.0694 |
| 9.4219 | 0.0030 | 62 | 9.3594 | 0.0693 |
| 9.3672 | 0.0031 | 63 | 9.3438 | 0.0692 |
| 9.3906 | 0.0031 | 64 | 9.3359 | 0.0700 |
| 9.4375 | 0.0031 | 65 | 9.3203 | 0.0713 |
| 9.3203 | 0.0032 | 66 | 9.3125 | 0.0718 |
| 9.375 | 0.0032 | 67 | 9.3047 | 0.0723 |
| 9.3516 | 0.0033 | 68 | 9.2891 | 0.0726 |
| 9.3359 | 0.0033 | 69 | 9.2812 | 0.0725 |
| 9.2891 | 0.0034 | 70 | 9.2656 | 0.0722 |
| 9.3047 | 0.0034 | 71 | 9.2578 | 0.0721 |
| 9.3125 | 0.0035 | 72 | 9.2422 | 0.0721 |
| 9.2891 | 0.0035 | 73 | 9.2344 | 0.0728 |
| 9.2578 | 0.0036 | 74 | 9.2188 | 0.0741 |
| 9.2422 | 0.0036 | 75 | 9.2109 | 0.0751 |
| 9.2031 | 0.0037 | 76 | 9.1953 | 0.0760 |
| 9.1641 | 0.0037 | 77 | 9.1875 | 0.0765 |
| 9.1953 | 0.0038 | 78 | 9.1797 | 0.0767 |
| 9.1484 | 0.0038 | 79 | 9.1641 | 0.0767 |
| 9.1953 | 0.0039 | 80 | 9.1562 | 0.0768 |
| 9.1328 | 0.0039 | 81 | 9.1406 | 0.0772 |
| 9.2109 | 0.0040 | 82 | 9.1328 | 0.0773 |
| 9.0547 | 0.0040 | 83 | 9.125 | 0.0771 |
| 9.1094 | 0.0041 | 84 | 9.1094 | 0.0770 |
| 9.1797 | 0.0041 | 85 | 9.1016 | 0.0768 |
| 9.1484 | 0.0042 | 86 | 9.0859 | 0.0770 |
| 9.1016 | 0.0042 | 87 | 9.0781 | 0.0772 |
| 9.1172 | 0.0043 | 88 | 9.0703 | 0.0772 |
| 9.0078 | 0.0043 | 89 | 9.0625 | 0.0772 |
| 8.9688 | 0.0044 | 90 | 9.0469 | 0.0775 |
| 9.0938 | 0.0044 | 91 | 9.0391 | 0.0781 |
| 9.0547 | 0.0045 | 92 | 9.0312 | 0.0791 |
| 9.0703 | 0.0045 | 93 | 9.0156 | 0.0798 |
| 9.0234 | 0.0046 | 94 | 9.0078 | 0.0801 |
| 9.0547 | 0.0046 | 95 | 9.0 | 0.0803 |
| 9.0391 | 0.0046 | 96 | 8.9844 | 0.0804 |
| 9.0703 | 0.0047 | 97 | 8.9766 | 0.0805 |
| 9.0234 | 0.0047 | 98 | 8.9688 | 0.0806 |
| 8.9062 | 0.0048 | 99 | 8.9609 | 0.0810 |
| 8.9688 | 0.0048 | 100 | 8.9453 | 0.0815 |
| 8.9609 | 0.0049 | 101 | 8.9375 | 0.0818 |
| 9.0391 | 0.0049 | 102 | 8.9297 | 0.0820 |
| 8.8984 | 0.0050 | 103 | 8.9141 | 0.0821 |
| 8.9688 | 0.0050 | 104 | 8.9062 | 0.0820 |
| 8.9922 | 0.0051 | 105 | 8.8984 | 0.0819 |
| 8.9062 | 0.0051 | 106 | 8.8906 | 0.0819 |
| 8.9062 | 0.0052 | 107 | 8.875 | 0.0822 |
| 8.9609 | 0.0052 | 108 | 8.8672 | 0.0826 |
| 8.8672 | 0.0053 | 109 | 8.8594 | 0.0831 |
| 8.8828 | 0.0053 | 110 | 8.8438 | 0.0836 |
| 8.8516 | 0.0054 | 111 | 8.8359 | 0.0840 |
| 8.8828 | 0.0054 | 112 | 8.8281 | 0.0844 |
| 8.9297 | 0.0055 | 113 | 8.8203 | 0.0845 |
| 8.9062 | 0.0055 | 114 | 8.8125 | 0.0847 |
| 8.7969 | 0.0056 | 115 | 8.7969 | 0.0851 |
| 8.8203 | 0.0056 | 116 | 8.7891 | 0.0855 |
| 8.8047 | 0.0057 | 117 | 8.7812 | 0.0858 |
| 8.7422 | 0.0057 | 118 | 8.7734 | 0.0858 |
| 8.7266 | 0.0058 | 119 | 8.7656 | 0.0860 |
| 8.6953 | 0.0058 | 120 | 8.7578 | 0.0861 |
| 8.7422 | 0.0059 | 121 | 8.75 | 0.0861 |
| 8.75 | 0.0059 | 122 | 8.7344 | 0.0863 |
| 8.7422 | 0.0060 | 123 | 8.7266 | 0.0867 |
| 8.6953 | 0.0060 | 124 | 8.7188 | 0.0870 |
| 8.6328 | 0.0061 | 125 | 8.7109 | 0.0871 |
| 8.7188 | 0.0061 | 126 | 8.7031 | 0.0871 |
| 8.7891 | 0.0062 | 127 | 8.6953 | 0.0873 |
| 8.7344 | 0.0062 | 128 | 8.6875 | 0.0874 |
| 8.7578 | 0.0062 | 129 | 8.6797 | 0.0874 |
| 8.6953 | 0.0063 | 130 | 8.6641 | 0.0874 |
| 8.7266 | 0.0063 | 131 | 8.6562 | 0.0877 |
| 8.6562 | 0.0064 | 132 | 8.6484 | 0.0882 |
| 8.7188 | 0.0064 | 133 | 8.6406 | 0.0884 |
| 8.6797 | 0.0065 | 134 | 8.6328 | 0.0883 |
| 8.6562 | 0.0065 | 135 | 8.625 | 0.0883 |
| 8.6172 | 0.0066 | 136 | 8.6172 | 0.0885 |
| 8.6406 | 0.0066 | 137 | 8.6094 | 0.0889 |
| 8.6797 | 0.0067 | 138 | 8.6016 | 0.0895 |
| 8.6406 | 0.0067 | 139 | 8.5938 | 0.0901 |
| 8.6094 | 0.0068 | 140 | 8.5859 | 0.0905 |
| 8.7031 | 0.0068 | 141 | 8.5781 | 0.0906 |
| 8.6797 | 0.0069 | 142 | 8.5703 | 0.0906 |
| 8.6719 | 0.0069 | 143 | 8.5625 | 0.0905 |
| 8.5703 | 0.0070 | 144 | 8.5547 | 0.0905 |
| 8.6016 | 0.0070 | 145 | 8.5391 | 0.0907 |
| 8.5859 | 0.0071 | 146 | 8.5312 | 0.0909 |
| 8.5469 | 0.0071 | 147 | 8.5312 | 0.0911 |
| 8.5391 | 0.0072 | 148 | 8.5234 | 0.0913 |
| 8.5391 | 0.0072 | 149 | 8.5156 | 0.0915 |
| 8.5625 | 0.0073 | 150 | 8.5078 | 0.0918 |
| 8.5469 | 0.0073 | 151 | 8.5 | 0.0921 |
| 8.5234 | 0.0074 | 152 | 8.4922 | 0.0920 |
| 8.5469 | 0.0074 | 153 | 8.4844 | 0.0922 |
| 8.4766 | 0.0075 | 154 | 8.4766 | 0.0923 |
| 8.4453 | 0.0075 | 155 | 8.4688 | 0.0925 |
| 8.375 | 0.0076 | 156 | 8.4609 | 0.0929 |
| 8.5156 | 0.0076 | 157 | 8.4531 | 0.0932 |
| 8.5234 | 0.0077 | 158 | 8.4453 | 0.0934 |
| 8.4844 | 0.0077 | 159 | 8.4375 | 0.0936 |
| 8.5 | 0.0077 | 160 | 8.4297 | 0.0938 |
| 8.3984 | 0.0078 | 161 | 8.4219 | 0.0936 |
| 8.5156 | 0.0078 | 162 | 8.4219 | 0.0935 |
| 8.4453 | 0.0079 | 163 | 8.4141 | 0.0934 |
| 8.4375 | 0.0079 | 164 | 8.4062 | 0.0937 |
| 8.4297 | 0.0080 | 165 | 8.3984 | 0.0944 |
| 8.4453 | 0.0080 | 166 | 8.3906 | 0.0953 |
| 8.4453 | 0.0081 | 167 | 8.3828 | 0.0961 |
| 8.3828 | 0.0081 | 168 | 8.375 | 0.0963 |
| 8.3828 | 0.0082 | 169 | 8.3672 | 0.0964 |
| 8.4297 | 0.0082 | 170 | 8.3594 | 0.0963 |
| 8.3828 | 0.0083 | 171 | 8.3516 | 0.0963 |
| 8.3984 | 0.0083 | 172 | 8.3438 | 0.0965 |
| 8.4375 | 0.0084 | 173 | 8.3359 | 0.0967 |
| 8.3906 | 0.0084 | 174 | 8.3281 | 0.0970 |
| 8.2578 | 0.0085 | 175 | 8.3203 | 0.0973 |
| 8.2891 | 0.0085 | 176 | 8.3203 | 0.0976 |
| 8.3125 | 0.0086 | 177 | 8.3125 | 0.0978 |
| 8.3359 | 0.0086 | 178 | 8.3047 | 0.0981 |
| 8.375 | 0.0087 | 179 | 8.2969 | 0.0982 |
| 8.3125 | 0.0087 | 180 | 8.2891 | 0.0982 |
| 8.2656 | 0.0088 | 181 | 8.2812 | 0.0981 |
| 8.2812 | 0.0088 | 182 | 8.2734 | 0.0980 |
| 8.3203 | 0.0089 | 183 | 8.2656 | 0.0979 |
| 8.2344 | 0.0089 | 184 | 8.2656 | 0.0979 |
| 8.3203 | 0.0090 | 185 | 8.2578 | 0.0982 |
| 8.2422 | 0.0090 | 186 | 8.25 | 0.0986 |
| 8.2344 | 0.0091 | 187 | 8.2422 | 0.0990 |
| 8.2891 | 0.0091 | 188 | 8.2344 | 0.0995 |
| 8.1875 | 0.0092 | 189 | 8.2266 | 0.0997 |
| 8.2188 | 0.0092 | 190 | 8.2266 | 0.0997 |
| 8.1953 | 0.0093 | 191 | 8.2188 | 0.0994 |
| 8.2578 | 0.0093 | 192 | 8.2109 | 0.0991 |
| 8.2188 | 0.0093 | 193 | 8.2031 | 0.0991 |
| 8.2812 | 0.0094 | 194 | 8.1953 | 0.0991 |
| 8.1328 | 0.0094 | 195 | 8.1875 | 0.0992 |
| 8.2578 | 0.0095 | 196 | 8.1875 | 0.0992 |
| 8.1719 | 0.0095 | 197 | 8.1797 | 0.0996 |
| 8.1953 | 0.0096 | 198 | 8.1719 | 0.1000 |
| 8.1875 | 0.0096 | 199 | 8.1641 | 0.1002 |
| 8.1953 | 0.0097 | 200 | 8.1562 | 0.1006 |
| 8.1406 | 0.0097 | 201 | 8.1562 | 0.1008 |
| 8.1797 | 0.0098 | 202 | 8.1484 | 0.1008 |
| 8.1484 | 0.0098 | 203 | 8.1406 | 0.1006 |
| 8.1719 | 0.0099 | 204 | 8.1328 | 0.1004 |
| 8.1641 | 0.0099 | 205 | 8.125 | 0.1002 |
| 8.2422 | 0.0100 | 206 | 8.125 | 0.1002 |
| 8.0703 | 0.0100 | 207 | 8.1172 | 0.1005 |
| 8.1328 | 0.0101 | 208 | 8.1094 | 0.1011 |
| 8.1562 | 0.0101 | 209 | 8.1016 | 0.1016 |
| 8.1797 | 0.0102 | 210 | 8.1016 | 0.1020 |
| 8.1641 | 0.0102 | 211 | 8.0938 | 0.1022 |
| 8.1016 | 0.0103 | 212 | 8.0859 | 0.1022 |
| 8.1719 | 0.0103 | 213 | 8.0781 | 0.1020 |
| 8.1094 | 0.0104 | 214 | 8.0703 | 0.1017 |
| 8.0469 | 0.0104 | 215 | 8.0703 | 0.1016 |
| 8.0859 | 0.0105 | 216 | 8.0625 | 0.1019 |
| 8.0625 | 0.0105 | 217 | 8.0547 | 0.1023 |
| 8.1406 | 0.0106 | 218 | 8.0547 | 0.1025 |
| 8.0547 | 0.0106 | 219 | 8.0469 | 0.1027 |
| 8.0234 | 0.0107 | 220 | 8.0391 | 0.1029 |
| 8.0469 | 0.0107 | 221 | 8.0391 | 0.1029 |
| 8.0312 | 0.0108 | 222 | 8.0312 | 0.1029 |
| 8.0391 | 0.0108 | 223 | 8.0234 | 0.1028 |
| 8.0391 | 0.0108 | 224 | 8.0156 | 0.1029 |
| 8.0859 | 0.0109 | 225 | 8.0156 | 0.1029 |
| 8.0391 | 0.0109 | 226 | 8.0078 | 0.1028 |
| 7.9883 | 0.0110 | 227 | 8.0 | 0.1028 |
| 8.0625 | 0.0110 | 228 | 7.9961 | 0.1029 |
| 8.1094 | 0.0111 | 229 | 7.9883 | 0.1032 |
| 8.0391 | 0.0111 | 230 | 7.9844 | 0.1034 |
| 8.0078 | 0.0112 | 231 | 7.9805 | 0.1037 |
| 8.0859 | 0.0112 | 232 | 7.9727 | 0.1039 |
| 7.9961 | 0.0113 | 233 | 7.9688 | 0.1039 |
| 8.0312 | 0.0113 | 234 | 7.9648 | 0.1039 |
| 8.0391 | 0.0114 | 235 | 7.9570 | 0.1037 |
| 7.9609 | 0.0114 | 236 | 7.9531 | 0.1037 |
| 7.9336 | 0.0115 | 237 | 7.9492 | 0.1038 |
| 7.9258 | 0.0115 | 238 | 7.9453 | 0.1040 |
| 7.9531 | 0.0116 | 239 | 7.9375 | 0.1042 |
| 7.9805 | 0.0116 | 240 | 7.9336 | 0.1045 |
| 8.0078 | 0.0117 | 241 | 7.9297 | 0.1048 |
| 7.8906 | 0.0117 | 242 | 7.9258 | 0.1051 |
| 7.9727 | 0.0118 | 243 | 7.9180 | 0.1054 |
| 7.9336 | 0.0118 | 244 | 7.9141 | 0.1055 |
| 7.9375 | 0.0119 | 245 | 7.9062 | 0.1055 |
| 7.9922 | 0.0119 | 246 | 7.9023 | 0.1054 |
| 7.9609 | 0.0120 | 247 | 7.8984 | 0.1053 |
| 7.8945 | 0.0120 | 248 | 7.8906 | 0.1053 |
| 7.8203 | 0.0121 | 249 | 7.8867 | 0.1055 |
| 7.8984 | 0.0121 | 250 | 7.8828 | 0.1057 |
| 7.9023 | 0.0122 | 251 | 7.8789 | 0.1058 |
| 7.918 | 0.0122 | 252 | 7.875 | 0.1058 |
| 7.832 | 0.0123 | 253 | 7.8672 | 0.1058 |
| 7.9609 | 0.0123 | 254 | 7.8633 | 0.1056 |
| 7.9531 | 0.0124 | 255 | 7.8594 | 0.1057 |
| 7.8125 | 0.0124 | 256 | 7.8555 | 0.1059 |
| 7.9648 | 0.0124 | 257 | 7.8516 | 0.1066 |
| 7.832 | 0.0125 | 258 | 7.8438 | 0.1068 |
| 7.8008 | 0.0125 | 259 | 7.8398 | 0.1069 |
| 7.8281 | 0.0126 | 260 | 7.8359 | 0.1069 |
| 7.8477 | 0.0126 | 261 | 7.8320 | 0.1069 |
| 7.8086 | 0.0127 | 262 | 7.8281 | 0.1069 |
| 7.8281 | 0.0127 | 263 | 7.8203 | 0.1069 |
| 7.8906 | 0.0128 | 264 | 7.8164 | 0.1067 |
| 7.8477 | 0.0128 | 265 | 7.8125 | 0.1067 |
| 7.8867 | 0.0129 | 266 | 7.8086 | 0.1067 |
| 7.8359 | 0.0129 | 267 | 7.8047 | 0.1069 |
| 7.7969 | 0.0130 | 268 | 7.8008 | 0.1074 |
| 7.8711 | 0.0130 | 269 | 7.7969 | 0.1079 |
| 7.7656 | 0.0131 | 270 | 7.7930 | 0.1081 |
| 7.8008 | 0.0131 | 271 | 7.7852 | 0.1080 |
| 7.8594 | 0.0132 | 272 | 7.7812 | 0.1079 |
| 7.8125 | 0.0132 | 273 | 7.7773 | 0.1077 |
| 7.7617 | 0.0133 | 274 | 7.7734 | 0.1075 |
| 7.7227 | 0.0133 | 275 | 7.7695 | 0.1074 |
| 7.8164 | 0.0134 | 276 | 7.7656 | 0.1077 |
| 7.7383 | 0.0134 | 277 | 7.7617 | 0.1081 |
| 7.8984 | 0.0135 | 278 | 7.7578 | 0.1085 |
| 7.793 | 0.0135 | 279 | 7.7539 | 0.1088 |
| 7.707 | 0.0136 | 280 | 7.75 | 0.1088 |
| 7.8086 | 0.0136 | 281 | 7.7422 | 0.1088 |
| 7.7773 | 0.0137 | 282 | 7.7383 | 0.1088 |
| 7.6875 | 0.0137 | 283 | 7.7344 | 0.1087 |
| 7.7188 | 0.0138 | 284 | 7.7305 | 0.1088 |
| 7.7539 | 0.0138 | 285 | 7.7266 | 0.1091 |
| 7.8008 | 0.0139 | 286 | 7.7227 | 0.1094 |
| 7.7578 | 0.0139 | 287 | 7.7188 | 0.1097 |
| 7.7148 | 0.0139 | 288 | 7.7148 | 0.1099 |
| 7.7266 | 0.0140 | 289 | 7.7109 | 0.1100 |
| 7.7031 | 0.0140 | 290 | 7.7070 | 0.1099 |
| 7.7383 | 0.0141 | 291 | 7.7031 | 0.1098 |
| 7.7266 | 0.0141 | 292 | 7.6953 | 0.1098 |
| 7.75 | 0.0142 | 293 | 7.6914 | 0.1100 |
| 7.7031 | 0.0142 | 294 | 7.6914 | 0.1102 |
| 7.7305 | 0.0143 | 295 | 7.6875 | 0.1103 |
| 7.7188 | 0.0143 | 296 | 7.6836 | 0.1104 |
| 7.6719 | 0.0144 | 297 | 7.6797 | 0.1105 |
| 7.6289 | 0.0144 | 298 | 7.6758 | 0.1108 |
| 7.6719 | 0.0145 | 299 | 7.6719 | 0.1110 |
| 7.7695 | 0.0145 | 300 | 7.6641 | 0.1111 |
| 7.7812 | 0.0146 | 301 | 7.6602 | 0.1109 |
| 7.707 | 0.0146 | 302 | 7.6562 | 0.1110 |
| 7.7539 | 0.0147 | 303 | 7.6523 | 0.1111 |
| 7.5898 | 0.0147 | 304 | 7.6523 | 0.1114 |
| 7.668 | 0.0148 | 305 | 7.6484 | 0.1116 |
| 7.6602 | 0.0148 | 306 | 7.6445 | 0.1116 |
| 7.6953 | 0.0149 | 307 | 7.6406 | 0.1117 |
| 7.7031 | 0.0149 | 308 | 7.6367 | 0.1118 |
| 7.6914 | 0.0150 | 309 | 7.6328 | 0.1120 |
| 7.582 | 0.0150 | 310 | 7.6289 | 0.1119 |
| 7.6445 | 0.0151 | 311 | 7.625 | 0.1118 |
| 7.5234 | 0.0151 | 312 | 7.6211 | 0.1118 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_12_128 | gokulsrinivasagan | 2024-07-02T16:07:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:55:48Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_12_128
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.07807518032045319
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_12_128
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 10.0781
- Accuracy: 0.0781
## Model description
More information needed
## Intended uses & 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: 48
- eval_batch_size: 48
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.8438 | 0.0001 | 1 | 10.8438 | 0.0103 |
| 10.8359 | 0.0001 | 2 | 10.8438 | 0.0103 |
| 10.8438 | 0.0002 | 3 | 10.8438 | 0.0103 |
| 10.8359 | 0.0003 | 4 | 10.8438 | 0.0103 |
| 10.8438 | 0.0004 | 5 | 10.8438 | 0.0103 |
| 10.8438 | 0.0004 | 6 | 10.8438 | 0.0103 |
| 10.8359 | 0.0005 | 7 | 10.8438 | 0.0103 |
| 10.8359 | 0.0006 | 8 | 10.8438 | 0.0103 |
| 10.8438 | 0.0007 | 9 | 10.8438 | 0.0103 |
| 10.8359 | 0.0007 | 10 | 10.8438 | 0.0103 |
| 10.8359 | 0.0008 | 11 | 10.8438 | 0.0103 |
| 10.8438 | 0.0009 | 12 | 10.8438 | 0.0103 |
| 10.8438 | 0.0009 | 13 | 10.8438 | 0.0103 |
| 10.8359 | 0.0010 | 14 | 10.8438 | 0.0103 |
| 10.8359 | 0.0011 | 15 | 10.8438 | 0.0103 |
| 10.8438 | 0.0012 | 16 | 10.8438 | 0.0103 |
| 10.8359 | 0.0012 | 17 | 10.8438 | 0.0103 |
| 10.8438 | 0.0013 | 18 | 10.8281 | 0.0113 |
| 10.8203 | 0.0014 | 19 | 10.8125 | 0.0116 |
| 10.8203 | 0.0015 | 20 | 10.8047 | 0.0117 |
| 10.8047 | 0.0015 | 21 | 10.7891 | 0.0118 |
| 10.7969 | 0.0016 | 22 | 10.7734 | 0.0118 |
| 10.7812 | 0.0017 | 23 | 10.7656 | 0.0118 |
| 10.7656 | 0.0017 | 24 | 10.75 | 0.0119 |
| 10.7578 | 0.0018 | 25 | 10.7344 | 0.0121 |
| 10.75 | 0.0019 | 26 | 10.7266 | 0.0124 |
| 10.7344 | 0.0020 | 27 | 10.7188 | 0.0131 |
| 10.7266 | 0.0020 | 28 | 10.7031 | 0.0144 |
| 10.7109 | 0.0021 | 29 | 10.6953 | 0.0165 |
| 10.7031 | 0.0022 | 30 | 10.6875 | 0.0196 |
| 10.7031 | 0.0023 | 31 | 10.6797 | 0.0236 |
| 10.6875 | 0.0023 | 32 | 10.6719 | 0.0282 |
| 10.6797 | 0.0024 | 33 | 10.6641 | 0.0330 |
| 10.6719 | 0.0025 | 34 | 10.6641 | 0.0375 |
| 10.6719 | 0.0025 | 35 | 10.6562 | 0.0409 |
| 10.6719 | 0.0026 | 36 | 10.6484 | 0.0437 |
| 10.6484 | 0.0027 | 37 | 10.6484 | 0.0461 |
| 10.6562 | 0.0028 | 38 | 10.6406 | 0.0481 |
| 10.6484 | 0.0028 | 39 | 10.6406 | 0.0498 |
| 10.6484 | 0.0029 | 40 | 10.6328 | 0.0511 |
| 10.6406 | 0.0030 | 41 | 10.6328 | 0.0521 |
| 10.6406 | 0.0031 | 42 | 10.625 | 0.0529 |
| 10.6406 | 0.0031 | 43 | 10.625 | 0.0535 |
| 10.625 | 0.0032 | 44 | 10.6172 | 0.0539 |
| 10.625 | 0.0033 | 45 | 10.6172 | 0.0542 |
| 10.625 | 0.0033 | 46 | 10.6172 | 0.0543 |
| 10.6172 | 0.0034 | 47 | 10.6094 | 0.0544 |
| 10.625 | 0.0035 | 48 | 10.6094 | 0.0545 |
| 10.6172 | 0.0036 | 49 | 10.6016 | 0.0545 |
| 10.6016 | 0.0036 | 50 | 10.6016 | 0.0545 |
| 10.6016 | 0.0037 | 51 | 10.6016 | 0.0545 |
| 10.6016 | 0.0038 | 52 | 10.5938 | 0.0546 |
| 10.6016 | 0.0039 | 53 | 10.5938 | 0.0545 |
| 10.5938 | 0.0039 | 54 | 10.5938 | 0.0545 |
| 10.6016 | 0.0040 | 55 | 10.5859 | 0.0545 |
| 10.5859 | 0.0041 | 56 | 10.5859 | 0.0545 |
| 10.6016 | 0.0041 | 57 | 10.5859 | 0.0545 |
| 10.5859 | 0.0042 | 58 | 10.5859 | 0.0546 |
| 10.5859 | 0.0043 | 59 | 10.5781 | 0.0547 |
| 10.5781 | 0.0044 | 60 | 10.5781 | 0.0548 |
| 10.5781 | 0.0044 | 61 | 10.5781 | 0.0550 |
| 10.5781 | 0.0045 | 62 | 10.5703 | 0.0553 |
| 10.5781 | 0.0046 | 63 | 10.5703 | 0.0557 |
| 10.5703 | 0.0046 | 64 | 10.5703 | 0.0561 |
| 10.5781 | 0.0047 | 65 | 10.5625 | 0.0566 |
| 10.5625 | 0.0048 | 66 | 10.5625 | 0.0570 |
| 10.5781 | 0.0049 | 67 | 10.5625 | 0.0573 |
| 10.5703 | 0.0049 | 68 | 10.5547 | 0.0575 |
| 10.5625 | 0.0050 | 69 | 10.5547 | 0.0577 |
| 10.5625 | 0.0051 | 70 | 10.5547 | 0.0578 |
| 10.5625 | 0.0052 | 71 | 10.5547 | 0.0579 |
| 10.5547 | 0.0052 | 72 | 10.5469 | 0.0580 |
| 10.5469 | 0.0053 | 73 | 10.5469 | 0.0580 |
| 10.5469 | 0.0054 | 74 | 10.5469 | 0.0580 |
| 10.5547 | 0.0054 | 75 | 10.5391 | 0.0580 |
| 10.5547 | 0.0055 | 76 | 10.5391 | 0.0580 |
| 10.5469 | 0.0056 | 77 | 10.5391 | 0.0582 |
| 10.5469 | 0.0057 | 78 | 10.5391 | 0.0582 |
| 10.5312 | 0.0057 | 79 | 10.5312 | 0.0584 |
| 10.5312 | 0.0058 | 80 | 10.5312 | 0.0586 |
| 10.5312 | 0.0059 | 81 | 10.5312 | 0.0590 |
| 10.5312 | 0.0060 | 82 | 10.5312 | 0.0593 |
| 10.5312 | 0.0060 | 83 | 10.5234 | 0.0597 |
| 10.5234 | 0.0061 | 84 | 10.5234 | 0.0600 |
| 10.5312 | 0.0062 | 85 | 10.5234 | 0.0602 |
| 10.5312 | 0.0062 | 86 | 10.5234 | 0.0603 |
| 10.5234 | 0.0063 | 87 | 10.5156 | 0.0604 |
| 10.5156 | 0.0064 | 88 | 10.5156 | 0.0605 |
| 10.5234 | 0.0065 | 89 | 10.5156 | 0.0606 |
| 10.5156 | 0.0065 | 90 | 10.5156 | 0.0606 |
| 10.5156 | 0.0066 | 91 | 10.5078 | 0.0606 |
| 10.5156 | 0.0067 | 92 | 10.5078 | 0.0605 |
| 10.5156 | 0.0068 | 93 | 10.5078 | 0.0603 |
| 10.5156 | 0.0068 | 94 | 10.5078 | 0.0602 |
| 10.5234 | 0.0069 | 95 | 10.5 | 0.0601 |
| 10.5156 | 0.0070 | 96 | 10.5 | 0.0602 |
| 10.5078 | 0.0070 | 97 | 10.5 | 0.0603 |
| 10.5 | 0.0071 | 98 | 10.5 | 0.0603 |
| 10.5078 | 0.0072 | 99 | 10.5 | 0.0604 |
| 10.5078 | 0.0073 | 100 | 10.4922 | 0.0606 |
| 10.5 | 0.0073 | 101 | 10.4922 | 0.0607 |
| 10.4922 | 0.0074 | 102 | 10.4922 | 0.0609 |
| 10.4922 | 0.0075 | 103 | 10.4922 | 0.0612 |
| 10.4844 | 0.0076 | 104 | 10.4844 | 0.0614 |
| 10.4922 | 0.0076 | 105 | 10.4844 | 0.0617 |
| 10.4922 | 0.0077 | 106 | 10.4844 | 0.0619 |
| 10.4844 | 0.0078 | 107 | 10.4844 | 0.0622 |
| 10.4922 | 0.0078 | 108 | 10.4766 | 0.0625 |
| 10.4844 | 0.0079 | 109 | 10.4766 | 0.0628 |
| 10.4766 | 0.0080 | 110 | 10.4766 | 0.0630 |
| 10.4844 | 0.0081 | 111 | 10.4766 | 0.0632 |
| 10.4766 | 0.0081 | 112 | 10.4766 | 0.0634 |
| 10.4844 | 0.0082 | 113 | 10.4688 | 0.0636 |
| 10.4766 | 0.0083 | 114 | 10.4688 | 0.0638 |
| 10.4766 | 0.0084 | 115 | 10.4688 | 0.0640 |
| 10.4844 | 0.0084 | 116 | 10.4688 | 0.0643 |
| 10.4531 | 0.0085 | 117 | 10.4609 | 0.0644 |
| 10.4609 | 0.0086 | 118 | 10.4609 | 0.0647 |
| 10.4609 | 0.0086 | 119 | 10.4609 | 0.0648 |
| 10.4688 | 0.0087 | 120 | 10.4609 | 0.0649 |
| 10.4609 | 0.0088 | 121 | 10.4609 | 0.0651 |
| 10.4609 | 0.0089 | 122 | 10.4531 | 0.0653 |
| 10.4531 | 0.0089 | 123 | 10.4531 | 0.0656 |
| 10.4531 | 0.0090 | 124 | 10.4531 | 0.0659 |
| 10.4531 | 0.0091 | 125 | 10.4531 | 0.0660 |
| 10.4531 | 0.0092 | 126 | 10.4453 | 0.0662 |
| 10.4531 | 0.0092 | 127 | 10.4453 | 0.0664 |
| 10.4453 | 0.0093 | 128 | 10.4453 | 0.0667 |
| 10.4531 | 0.0094 | 129 | 10.4453 | 0.0670 |
| 10.4375 | 0.0094 | 130 | 10.4453 | 0.0673 |
| 10.4453 | 0.0095 | 131 | 10.4375 | 0.0676 |
| 10.4375 | 0.0096 | 132 | 10.4375 | 0.0678 |
| 10.4375 | 0.0097 | 133 | 10.4375 | 0.0679 |
| 10.4297 | 0.0097 | 134 | 10.4375 | 0.0679 |
| 10.4453 | 0.0098 | 135 | 10.4297 | 0.0678 |
| 10.4375 | 0.0099 | 136 | 10.4297 | 0.0677 |
| 10.4375 | 0.0100 | 137 | 10.4297 | 0.0677 |
| 10.4219 | 0.0100 | 138 | 10.4297 | 0.0677 |
| 10.4375 | 0.0101 | 139 | 10.4219 | 0.0678 |
| 10.4297 | 0.0102 | 140 | 10.4219 | 0.0680 |
| 10.4297 | 0.0102 | 141 | 10.4219 | 0.0682 |
| 10.4219 | 0.0103 | 142 | 10.4219 | 0.0684 |
| 10.4219 | 0.0104 | 143 | 10.4219 | 0.0687 |
| 10.4219 | 0.0105 | 144 | 10.4141 | 0.0689 |
| 10.4219 | 0.0105 | 145 | 10.4141 | 0.0692 |
| 10.4141 | 0.0106 | 146 | 10.4141 | 0.0693 |
| 10.4062 | 0.0107 | 147 | 10.4141 | 0.0695 |
| 10.4141 | 0.0108 | 148 | 10.4062 | 0.0696 |
| 10.4141 | 0.0108 | 149 | 10.4062 | 0.0697 |
| 10.4219 | 0.0109 | 150 | 10.4062 | 0.0697 |
| 10.4062 | 0.0110 | 151 | 10.4062 | 0.0698 |
| 10.4141 | 0.0110 | 152 | 10.4062 | 0.0700 |
| 10.4141 | 0.0111 | 153 | 10.3984 | 0.0701 |
| 10.4219 | 0.0112 | 154 | 10.3984 | 0.0702 |
| 10.4141 | 0.0113 | 155 | 10.3984 | 0.0704 |
| 10.4062 | 0.0113 | 156 | 10.3984 | 0.0705 |
| 10.4062 | 0.0114 | 157 | 10.3906 | 0.0707 |
| 10.3906 | 0.0115 | 158 | 10.3906 | 0.0708 |
| 10.3906 | 0.0116 | 159 | 10.3906 | 0.0710 |
| 10.3984 | 0.0116 | 160 | 10.3906 | 0.0711 |
| 10.3984 | 0.0117 | 161 | 10.3906 | 0.0711 |
| 10.3906 | 0.0118 | 162 | 10.3828 | 0.0712 |
| 10.3906 | 0.0118 | 163 | 10.3828 | 0.0712 |
| 10.3906 | 0.0119 | 164 | 10.3828 | 0.0714 |
| 10.3828 | 0.0120 | 165 | 10.3828 | 0.0715 |
| 10.375 | 0.0121 | 166 | 10.375 | 0.0716 |
| 10.3828 | 0.0121 | 167 | 10.375 | 0.0717 |
| 10.3828 | 0.0122 | 168 | 10.375 | 0.0718 |
| 10.3828 | 0.0123 | 169 | 10.375 | 0.0719 |
| 10.3828 | 0.0124 | 170 | 10.375 | 0.0721 |
| 10.3672 | 0.0124 | 171 | 10.3672 | 0.0721 |
| 10.375 | 0.0125 | 172 | 10.3672 | 0.0721 |
| 10.3594 | 0.0126 | 173 | 10.3672 | 0.0721 |
| 10.375 | 0.0126 | 174 | 10.3672 | 0.0720 |
| 10.3594 | 0.0127 | 175 | 10.3594 | 0.0721 |
| 10.3672 | 0.0128 | 176 | 10.3594 | 0.0722 |
| 10.375 | 0.0129 | 177 | 10.3594 | 0.0723 |
| 10.3672 | 0.0129 | 178 | 10.3594 | 0.0726 |
| 10.3672 | 0.0130 | 179 | 10.3594 | 0.0727 |
| 10.3594 | 0.0131 | 180 | 10.3516 | 0.0728 |
| 10.3672 | 0.0132 | 181 | 10.3516 | 0.0729 |
| 10.3594 | 0.0132 | 182 | 10.3516 | 0.0730 |
| 10.3516 | 0.0133 | 183 | 10.3516 | 0.0731 |
| 10.3594 | 0.0134 | 184 | 10.3516 | 0.0732 |
| 10.3516 | 0.0134 | 185 | 10.3438 | 0.0733 |
| 10.3516 | 0.0135 | 186 | 10.3438 | 0.0733 |
| 10.3438 | 0.0136 | 187 | 10.3438 | 0.0734 |
| 10.3516 | 0.0137 | 188 | 10.3438 | 0.0734 |
| 10.3516 | 0.0137 | 189 | 10.3359 | 0.0735 |
| 10.3438 | 0.0138 | 190 | 10.3359 | 0.0735 |
| 10.3516 | 0.0139 | 191 | 10.3359 | 0.0735 |
| 10.3359 | 0.0139 | 192 | 10.3359 | 0.0737 |
| 10.3359 | 0.0140 | 193 | 10.3359 | 0.0737 |
| 10.3359 | 0.0141 | 194 | 10.3281 | 0.0736 |
| 10.3359 | 0.0142 | 195 | 10.3281 | 0.0736 |
| 10.3359 | 0.0142 | 196 | 10.3281 | 0.0736 |
| 10.3281 | 0.0143 | 197 | 10.3281 | 0.0737 |
| 10.3359 | 0.0144 | 198 | 10.3281 | 0.0738 |
| 10.3203 | 0.0145 | 199 | 10.3203 | 0.0740 |
| 10.3359 | 0.0145 | 200 | 10.3203 | 0.0741 |
| 10.3359 | 0.0146 | 201 | 10.3203 | 0.0742 |
| 10.3281 | 0.0147 | 202 | 10.3203 | 0.0743 |
| 10.3203 | 0.0147 | 203 | 10.3125 | 0.0743 |
| 10.3203 | 0.0148 | 204 | 10.3125 | 0.0743 |
| 10.3281 | 0.0149 | 205 | 10.3125 | 0.0743 |
| 10.3125 | 0.0150 | 206 | 10.3125 | 0.0741 |
| 10.3125 | 0.0150 | 207 | 10.3125 | 0.0740 |
| 10.3047 | 0.0151 | 208 | 10.3047 | 0.0740 |
| 10.3125 | 0.0152 | 209 | 10.3047 | 0.0741 |
| 10.3125 | 0.0153 | 210 | 10.3047 | 0.0742 |
| 10.3203 | 0.0153 | 211 | 10.3047 | 0.0743 |
| 10.3047 | 0.0154 | 212 | 10.3047 | 0.0744 |
| 10.3203 | 0.0155 | 213 | 10.2969 | 0.0745 |
| 10.3125 | 0.0155 | 214 | 10.2969 | 0.0747 |
| 10.3047 | 0.0156 | 215 | 10.2969 | 0.0749 |
| 10.2969 | 0.0157 | 216 | 10.2969 | 0.0750 |
| 10.3047 | 0.0158 | 217 | 10.2969 | 0.0750 |
| 10.2969 | 0.0158 | 218 | 10.2891 | 0.0749 |
| 10.2891 | 0.0159 | 219 | 10.2891 | 0.0747 |
| 10.2969 | 0.0160 | 220 | 10.2891 | 0.0744 |
| 10.2969 | 0.0161 | 221 | 10.2891 | 0.0742 |
| 10.2891 | 0.0161 | 222 | 10.2891 | 0.0741 |
| 10.2891 | 0.0162 | 223 | 10.2812 | 0.0742 |
| 10.2891 | 0.0163 | 224 | 10.2812 | 0.0743 |
| 10.2891 | 0.0163 | 225 | 10.2812 | 0.0746 |
| 10.2969 | 0.0164 | 226 | 10.2812 | 0.0748 |
| 10.2812 | 0.0165 | 227 | 10.2734 | 0.0749 |
| 10.2891 | 0.0166 | 228 | 10.2734 | 0.0750 |
| 10.2734 | 0.0166 | 229 | 10.2734 | 0.0751 |
| 10.2969 | 0.0167 | 230 | 10.2734 | 0.0750 |
| 10.2656 | 0.0168 | 231 | 10.2734 | 0.0749 |
| 10.2734 | 0.0169 | 232 | 10.2656 | 0.0747 |
| 10.2734 | 0.0169 | 233 | 10.2656 | 0.0747 |
| 10.2734 | 0.0170 | 234 | 10.2656 | 0.0746 |
| 10.2656 | 0.0171 | 235 | 10.2656 | 0.0747 |
| 10.2656 | 0.0171 | 236 | 10.2656 | 0.0748 |
| 10.2734 | 0.0172 | 237 | 10.2578 | 0.0749 |
| 10.2656 | 0.0173 | 238 | 10.2578 | 0.0752 |
| 10.2734 | 0.0174 | 239 | 10.2578 | 0.0755 |
| 10.2578 | 0.0174 | 240 | 10.2578 | 0.0756 |
| 10.2734 | 0.0175 | 241 | 10.2578 | 0.0756 |
| 10.2656 | 0.0176 | 242 | 10.25 | 0.0756 |
| 10.2578 | 0.0177 | 243 | 10.25 | 0.0756 |
| 10.2578 | 0.0177 | 244 | 10.25 | 0.0756 |
| 10.2578 | 0.0178 | 245 | 10.25 | 0.0756 |
| 10.2578 | 0.0179 | 246 | 10.25 | 0.0756 |
| 10.2578 | 0.0179 | 247 | 10.2422 | 0.0757 |
| 10.2578 | 0.0180 | 248 | 10.2422 | 0.0758 |
| 10.2422 | 0.0181 | 249 | 10.2422 | 0.0759 |
| 10.2422 | 0.0182 | 250 | 10.2422 | 0.0759 |
| 10.2422 | 0.0182 | 251 | 10.2422 | 0.0759 |
| 10.2422 | 0.0183 | 252 | 10.2344 | 0.0759 |
| 10.2422 | 0.0184 | 253 | 10.2344 | 0.0759 |
| 10.2422 | 0.0185 | 254 | 10.2344 | 0.0759 |
| 10.2422 | 0.0185 | 255 | 10.2344 | 0.0761 |
| 10.2422 | 0.0186 | 256 | 10.2344 | 0.0761 |
| 10.2422 | 0.0187 | 257 | 10.2266 | 0.0760 |
| 10.2422 | 0.0187 | 258 | 10.2266 | 0.0760 |
| 10.2344 | 0.0188 | 259 | 10.2266 | 0.0759 |
| 10.2344 | 0.0189 | 260 | 10.2266 | 0.0759 |
| 10.2266 | 0.0190 | 261 | 10.2266 | 0.0760 |
| 10.2188 | 0.0190 | 262 | 10.2188 | 0.0760 |
| 10.2266 | 0.0191 | 263 | 10.2188 | 0.0762 |
| 10.2266 | 0.0192 | 264 | 10.2188 | 0.0762 |
| 10.2188 | 0.0193 | 265 | 10.2188 | 0.0762 |
| 10.2266 | 0.0193 | 266 | 10.2188 | 0.0762 |
| 10.2188 | 0.0194 | 267 | 10.2109 | 0.0762 |
| 10.2109 | 0.0195 | 268 | 10.2109 | 0.0763 |
| 10.2109 | 0.0195 | 269 | 10.2109 | 0.0762 |
| 10.2109 | 0.0196 | 270 | 10.2109 | 0.0761 |
| 10.2188 | 0.0197 | 271 | 10.2109 | 0.0761 |
| 10.2109 | 0.0198 | 272 | 10.2031 | 0.0760 |
| 10.2188 | 0.0198 | 273 | 10.2031 | 0.0761 |
| 10.2266 | 0.0199 | 274 | 10.2031 | 0.0762 |
| 10.2188 | 0.0200 | 275 | 10.2031 | 0.0762 |
| 10.2109 | 0.0201 | 276 | 10.1953 | 0.0761 |
| 10.2109 | 0.0201 | 277 | 10.1953 | 0.0762 |
| 10.1953 | 0.0202 | 278 | 10.1953 | 0.0762 |
| 10.2031 | 0.0203 | 279 | 10.1953 | 0.0763 |
| 10.2188 | 0.0203 | 280 | 10.1953 | 0.0765 |
| 10.1953 | 0.0204 | 281 | 10.1875 | 0.0766 |
| 10.1953 | 0.0205 | 282 | 10.1875 | 0.0767 |
| 10.2031 | 0.0206 | 283 | 10.1875 | 0.0767 |
| 10.1797 | 0.0206 | 284 | 10.1875 | 0.0766 |
| 10.1953 | 0.0207 | 285 | 10.1875 | 0.0765 |
| 10.1953 | 0.0208 | 286 | 10.1797 | 0.0764 |
| 10.1875 | 0.0209 | 287 | 10.1797 | 0.0764 |
| 10.1953 | 0.0209 | 288 | 10.1797 | 0.0765 |
| 10.1875 | 0.0210 | 289 | 10.1797 | 0.0765 |
| 10.1875 | 0.0211 | 290 | 10.1797 | 0.0768 |
| 10.1797 | 0.0211 | 291 | 10.1719 | 0.0770 |
| 10.1719 | 0.0212 | 292 | 10.1719 | 0.0771 |
| 10.1719 | 0.0213 | 293 | 10.1719 | 0.0772 |
| 10.1797 | 0.0214 | 294 | 10.1719 | 0.0773 |
| 10.1797 | 0.0214 | 295 | 10.1719 | 0.0773 |
| 10.1641 | 0.0215 | 296 | 10.1641 | 0.0773 |
| 10.1719 | 0.0216 | 297 | 10.1641 | 0.0773 |
| 10.1719 | 0.0217 | 298 | 10.1641 | 0.0773 |
| 10.1719 | 0.0217 | 299 | 10.1641 | 0.0773 |
| 10.1719 | 0.0218 | 300 | 10.1641 | 0.0773 |
| 10.1641 | 0.0219 | 301 | 10.1641 | 0.0773 |
| 10.1562 | 0.0219 | 302 | 10.1562 | 0.0772 |
| 10.1719 | 0.0220 | 303 | 10.1562 | 0.0771 |
| 10.1562 | 0.0221 | 304 | 10.1562 | 0.0772 |
| 10.1641 | 0.0222 | 305 | 10.1562 | 0.0773 |
| 10.1562 | 0.0222 | 306 | 10.1484 | 0.0773 |
| 10.1641 | 0.0223 | 307 | 10.1484 | 0.0773 |
| 10.1719 | 0.0224 | 308 | 10.1484 | 0.0775 |
| 10.1562 | 0.0224 | 309 | 10.1484 | 0.0775 |
| 10.1719 | 0.0225 | 310 | 10.1484 | 0.0775 |
| 10.1562 | 0.0226 | 311 | 10.1406 | 0.0774 |
| 10.1562 | 0.0227 | 312 | 10.1406 | 0.0774 |
| 10.1562 | 0.0227 | 313 | 10.1406 | 0.0773 |
| 10.1406 | 0.0228 | 314 | 10.1406 | 0.0774 |
| 10.1406 | 0.0229 | 315 | 10.1406 | 0.0774 |
| 10.1406 | 0.0230 | 316 | 10.1406 | 0.0774 |
| 10.1328 | 0.0230 | 317 | 10.1328 | 0.0775 |
| 10.1484 | 0.0231 | 318 | 10.1328 | 0.0775 |
| 10.1328 | 0.0232 | 319 | 10.1328 | 0.0775 |
| 10.1328 | 0.0232 | 320 | 10.1328 | 0.0775 |
| 10.125 | 0.0233 | 321 | 10.1328 | 0.0775 |
| 10.1406 | 0.0234 | 322 | 10.125 | 0.0776 |
| 10.1328 | 0.0235 | 323 | 10.125 | 0.0777 |
| 10.125 | 0.0235 | 324 | 10.125 | 0.0778 |
| 10.125 | 0.0236 | 325 | 10.125 | 0.0777 |
| 10.125 | 0.0237 | 326 | 10.125 | 0.0777 |
| 10.1328 | 0.0238 | 327 | 10.1172 | 0.0777 |
| 10.1172 | 0.0238 | 328 | 10.1172 | 0.0777 |
| 10.1172 | 0.0239 | 329 | 10.1172 | 0.0777 |
| 10.125 | 0.0240 | 330 | 10.1172 | 0.0778 |
| 10.1094 | 0.0240 | 331 | 10.1172 | 0.0778 |
| 10.1094 | 0.0241 | 332 | 10.1094 | 0.0777 |
| 10.1094 | 0.0242 | 333 | 10.1094 | 0.0776 |
| 10.1172 | 0.0243 | 334 | 10.1094 | 0.0775 |
| 10.125 | 0.0243 | 335 | 10.1094 | 0.0774 |
| 10.1172 | 0.0244 | 336 | 10.1094 | 0.0772 |
| 10.1016 | 0.0245 | 337 | 10.1016 | 0.0771 |
| 10.1094 | 0.0246 | 338 | 10.1016 | 0.0773 |
| 10.1172 | 0.0246 | 339 | 10.1016 | 0.0775 |
| 10.1094 | 0.0247 | 340 | 10.1016 | 0.0777 |
| 10.1172 | 0.0248 | 341 | 10.1016 | 0.0778 |
| 10.0938 | 0.0248 | 342 | 10.0938 | 0.0779 |
| 10.1016 | 0.0249 | 343 | 10.0938 | 0.0780 |
| 10.0938 | 0.0250 | 344 | 10.0938 | 0.0780 |
| 10.0938 | 0.0251 | 345 | 10.0938 | 0.0780 |
| 10.1016 | 0.0251 | 346 | 10.0938 | 0.0781 |
| 10.1094 | 0.0252 | 347 | 10.0859 | 0.0780 |
| 10.0938 | 0.0253 | 348 | 10.0859 | 0.0780 |
| 10.0938 | 0.0254 | 349 | 10.0859 | 0.0780 |
| 10.0859 | 0.0254 | 350 | 10.0859 | 0.0779 |
| 10.0859 | 0.0255 | 351 | 10.0859 | 0.0780 |
| 10.0938 | 0.0256 | 352 | 10.0781 | 0.0781 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_12_512 | gokulsrinivasagan | 2024-07-02T16:19:10Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:55:52Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_12_512
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.09167533902983765
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_12_512
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 8.9141
- Accuracy: 0.0917
## Model description
More information needed
## Intended uses & 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: 24
- eval_batch_size: 24
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.8828 | 0.0000 | 1 | 10.8828 | 0.0001 |
| 10.8984 | 0.0001 | 2 | 10.8828 | 0.0001 |
| 10.8906 | 0.0001 | 3 | 10.8828 | 0.0001 |
| 10.8828 | 0.0001 | 4 | 10.8828 | 0.0001 |
| 10.8828 | 0.0002 | 5 | 10.8828 | 0.0001 |
| 10.8828 | 0.0002 | 6 | 10.8828 | 0.0001 |
| 10.8906 | 0.0003 | 7 | 10.8828 | 0.0001 |
| 10.8828 | 0.0003 | 8 | 10.8828 | 0.0001 |
| 10.875 | 0.0003 | 9 | 10.8828 | 0.0001 |
| 10.8984 | 0.0004 | 10 | 10.8828 | 0.0001 |
| 10.8828 | 0.0004 | 11 | 10.8828 | 0.0001 |
| 10.8906 | 0.0004 | 12 | 10.8828 | 0.0001 |
| 10.8828 | 0.0005 | 13 | 10.8828 | 0.0001 |
| 10.8828 | 0.0005 | 14 | 10.8828 | 0.0001 |
| 10.8828 | 0.0005 | 15 | 10.8828 | 0.0001 |
| 10.8828 | 0.0006 | 16 | 10.8828 | 0.0001 |
| 10.875 | 0.0006 | 17 | 10.8828 | 0.0001 |
| 10.8828 | 0.0007 | 18 | 10.6328 | 0.0197 |
| 10.6641 | 0.0007 | 19 | 10.4844 | 0.0444 |
| 10.5078 | 0.0007 | 20 | 10.3828 | 0.0499 |
| 10.3984 | 0.0008 | 21 | 10.3125 | 0.0532 |
| 10.3438 | 0.0008 | 22 | 10.25 | 0.0550 |
| 10.2656 | 0.0008 | 23 | 10.2031 | 0.0562 |
| 10.25 | 0.0009 | 24 | 10.1641 | 0.0540 |
| 10.1875 | 0.0009 | 25 | 10.1328 | 0.0470 |
| 10.125 | 0.0009 | 26 | 10.1094 | 0.0461 |
| 10.125 | 0.0010 | 27 | 10.0859 | 0.0480 |
| 10.0938 | 0.0010 | 28 | 10.0703 | 0.0474 |
| 10.0625 | 0.0011 | 29 | 10.0547 | 0.0465 |
| 10.0703 | 0.0011 | 30 | 10.0391 | 0.0472 |
| 10.0156 | 0.0011 | 31 | 10.0234 | 0.0515 |
| 10.0859 | 0.0012 | 32 | 10.0156 | 0.0587 |
| 9.9922 | 0.0012 | 33 | 10.0078 | 0.0613 |
| 10.0234 | 0.0012 | 34 | 9.9922 | 0.0608 |
| 9.9609 | 0.0013 | 35 | 9.9844 | 0.0600 |
| 10.0391 | 0.0013 | 36 | 9.9766 | 0.0608 |
| 9.9922 | 0.0013 | 37 | 9.9609 | 0.0619 |
| 9.9688 | 0.0014 | 38 | 9.9531 | 0.0623 |
| 9.9453 | 0.0014 | 39 | 9.9375 | 0.0622 |
| 9.9609 | 0.0015 | 40 | 9.9297 | 0.0628 |
| 9.9609 | 0.0015 | 41 | 9.9141 | 0.0640 |
| 10.0234 | 0.0015 | 42 | 9.8984 | 0.0649 |
| 9.9375 | 0.0016 | 43 | 9.8906 | 0.0648 |
| 9.8516 | 0.0016 | 44 | 9.875 | 0.0644 |
| 9.8672 | 0.0016 | 45 | 9.8594 | 0.0643 |
| 9.8984 | 0.0017 | 46 | 9.8438 | 0.0643 |
| 9.875 | 0.0017 | 47 | 9.8359 | 0.0645 |
| 9.8672 | 0.0017 | 48 | 9.8203 | 0.0646 |
| 9.8984 | 0.0018 | 49 | 9.8125 | 0.0649 |
| 9.7891 | 0.0018 | 50 | 9.8047 | 0.0653 |
| 9.8281 | 0.0019 | 51 | 9.7891 | 0.0655 |
| 9.8281 | 0.0019 | 52 | 9.7812 | 0.0654 |
| 9.7969 | 0.0019 | 53 | 9.7734 | 0.0660 |
| 9.7812 | 0.0020 | 54 | 9.7656 | 0.0670 |
| 9.8047 | 0.0020 | 55 | 9.75 | 0.0682 |
| 9.7969 | 0.0020 | 56 | 9.7422 | 0.0688 |
| 9.7891 | 0.0021 | 57 | 9.7344 | 0.0691 |
| 9.6875 | 0.0021 | 58 | 9.7266 | 0.0690 |
| 9.7188 | 0.0021 | 59 | 9.7188 | 0.0686 |
| 9.7344 | 0.0022 | 60 | 9.7109 | 0.0682 |
| 9.7344 | 0.0022 | 61 | 9.6953 | 0.0687 |
| 9.7578 | 0.0023 | 62 | 9.6875 | 0.0697 |
| 9.6484 | 0.0023 | 63 | 9.6719 | 0.0708 |
| 9.6328 | 0.0023 | 64 | 9.6641 | 0.0715 |
| 9.7656 | 0.0024 | 65 | 9.6562 | 0.0721 |
| 9.6875 | 0.0024 | 66 | 9.6484 | 0.0725 |
| 9.6328 | 0.0024 | 67 | 9.6406 | 0.0727 |
| 9.6953 | 0.0025 | 68 | 9.6328 | 0.0734 |
| 9.7188 | 0.0025 | 69 | 9.625 | 0.0744 |
| 9.6875 | 0.0025 | 70 | 9.6172 | 0.0753 |
| 9.625 | 0.0026 | 71 | 9.6094 | 0.0763 |
| 9.6172 | 0.0026 | 72 | 9.6016 | 0.0769 |
| 9.6016 | 0.0027 | 73 | 9.5938 | 0.0771 |
| 9.6094 | 0.0027 | 74 | 9.5859 | 0.0771 |
| 9.5859 | 0.0027 | 75 | 9.5781 | 0.0771 |
| 9.5859 | 0.0028 | 76 | 9.5703 | 0.0767 |
| 9.5859 | 0.0028 | 77 | 9.5625 | 0.0765 |
| 9.5781 | 0.0028 | 78 | 9.5547 | 0.0764 |
| 9.6172 | 0.0029 | 79 | 9.5469 | 0.0763 |
| 9.5859 | 0.0029 | 80 | 9.5391 | 0.0768 |
| 9.5859 | 0.0029 | 81 | 9.5312 | 0.0770 |
| 9.5391 | 0.0030 | 82 | 9.5234 | 0.0770 |
| 9.5391 | 0.0030 | 83 | 9.5234 | 0.0764 |
| 9.5312 | 0.0031 | 84 | 9.5156 | 0.0758 |
| 9.5547 | 0.0031 | 85 | 9.5078 | 0.0757 |
| 9.5781 | 0.0031 | 86 | 9.5 | 0.0760 |
| 9.5703 | 0.0032 | 87 | 9.4922 | 0.0764 |
| 9.4844 | 0.0032 | 88 | 9.4844 | 0.0764 |
| 9.5312 | 0.0032 | 89 | 9.4766 | 0.0765 |
| 9.5312 | 0.0033 | 90 | 9.4688 | 0.0765 |
| 9.5078 | 0.0033 | 91 | 9.4688 | 0.0766 |
| 9.5 | 0.0033 | 92 | 9.4609 | 0.0768 |
| 9.4844 | 0.0034 | 93 | 9.4531 | 0.0769 |
| 9.4688 | 0.0034 | 94 | 9.4453 | 0.0773 |
| 9.5156 | 0.0035 | 95 | 9.4375 | 0.0777 |
| 9.4453 | 0.0035 | 96 | 9.4297 | 0.0783 |
| 9.4766 | 0.0035 | 97 | 9.4219 | 0.0794 |
| 9.4219 | 0.0036 | 98 | 9.4219 | 0.0804 |
| 9.4531 | 0.0036 | 99 | 9.4141 | 0.0814 |
| 9.4141 | 0.0036 | 100 | 9.4062 | 0.0819 |
| 9.375 | 0.0037 | 101 | 9.3984 | 0.0825 |
| 9.4219 | 0.0037 | 102 | 9.3906 | 0.0828 |
| 9.3828 | 0.0037 | 103 | 9.3828 | 0.0828 |
| 9.375 | 0.0038 | 104 | 9.3828 | 0.0827 |
| 9.3516 | 0.0038 | 105 | 9.375 | 0.0825 |
| 9.3906 | 0.0039 | 106 | 9.3672 | 0.0825 |
| 9.3672 | 0.0039 | 107 | 9.3594 | 0.0823 |
| 9.3359 | 0.0039 | 108 | 9.3516 | 0.0822 |
| 9.4062 | 0.0040 | 109 | 9.3438 | 0.0818 |
| 9.3906 | 0.0040 | 110 | 9.3438 | 0.0816 |
| 9.25 | 0.0040 | 111 | 9.3359 | 0.0816 |
| 9.3281 | 0.0041 | 112 | 9.3281 | 0.0816 |
| 9.375 | 0.0041 | 113 | 9.3203 | 0.0813 |
| 9.3906 | 0.0041 | 114 | 9.3203 | 0.0812 |
| 9.3203 | 0.0042 | 115 | 9.3125 | 0.0812 |
| 9.3125 | 0.0042 | 116 | 9.3047 | 0.0811 |
| 9.3359 | 0.0043 | 117 | 9.2969 | 0.0809 |
| 9.2812 | 0.0043 | 118 | 9.2969 | 0.0808 |
| 9.2031 | 0.0043 | 119 | 9.2891 | 0.0807 |
| 9.2422 | 0.0044 | 120 | 9.2812 | 0.0808 |
| 9.3047 | 0.0044 | 121 | 9.2812 | 0.0809 |
| 9.2969 | 0.0044 | 122 | 9.2734 | 0.0810 |
| 9.25 | 0.0045 | 123 | 9.2656 | 0.0815 |
| 9.3281 | 0.0045 | 124 | 9.2578 | 0.0825 |
| 9.2656 | 0.0045 | 125 | 9.2578 | 0.0836 |
| 9.3047 | 0.0046 | 126 | 9.25 | 0.0845 |
| 9.25 | 0.0046 | 127 | 9.2422 | 0.0850 |
| 9.2969 | 0.0046 | 128 | 9.2344 | 0.0852 |
| 9.3203 | 0.0047 | 129 | 9.2344 | 0.0853 |
| 9.25 | 0.0047 | 130 | 9.2266 | 0.0853 |
| 9.2422 | 0.0048 | 131 | 9.2188 | 0.0854 |
| 9.1641 | 0.0048 | 132 | 9.2109 | 0.0855 |
| 9.2109 | 0.0048 | 133 | 9.2109 | 0.0858 |
| 9.2422 | 0.0049 | 134 | 9.2031 | 0.0860 |
| 9.2188 | 0.0049 | 135 | 9.1953 | 0.0861 |
| 9.3047 | 0.0049 | 136 | 9.1875 | 0.0861 |
| 9.1641 | 0.0050 | 137 | 9.1875 | 0.0861 |
| 9.2188 | 0.0050 | 138 | 9.1797 | 0.0859 |
| 9.2422 | 0.0050 | 139 | 9.1719 | 0.0856 |
| 9.2422 | 0.0051 | 140 | 9.1719 | 0.0855 |
| 9.1484 | 0.0051 | 141 | 9.1641 | 0.0852 |
| 9.2422 | 0.0052 | 142 | 9.1562 | 0.0851 |
| 9.1953 | 0.0052 | 143 | 9.1484 | 0.0852 |
| 9.1641 | 0.0052 | 144 | 9.1484 | 0.0853 |
| 9.1875 | 0.0053 | 145 | 9.1406 | 0.0854 |
| 9.1172 | 0.0053 | 146 | 9.1328 | 0.0855 |
| 9.1094 | 0.0053 | 147 | 9.1328 | 0.0856 |
| 9.1328 | 0.0054 | 148 | 9.125 | 0.0859 |
| 9.1641 | 0.0054 | 149 | 9.1172 | 0.0863 |
| 9.1641 | 0.0054 | 150 | 9.1094 | 0.0868 |
| 9.1875 | 0.0055 | 151 | 9.1094 | 0.0873 |
| 9.2031 | 0.0055 | 152 | 9.1016 | 0.0875 |
| 9.0703 | 0.0056 | 153 | 9.0938 | 0.0880 |
| 9.1484 | 0.0056 | 154 | 9.0859 | 0.0884 |
| 9.0625 | 0.0056 | 155 | 9.0859 | 0.0888 |
| 9.0781 | 0.0057 | 156 | 9.0781 | 0.0889 |
| 9.0234 | 0.0057 | 157 | 9.0703 | 0.0892 |
| 9.0781 | 0.0057 | 158 | 9.0703 | 0.0894 |
| 9.0 | 0.0058 | 159 | 9.0625 | 0.0895 |
| 9.0312 | 0.0058 | 160 | 9.0547 | 0.0896 |
| 9.0391 | 0.0058 | 161 | 9.0547 | 0.0898 |
| 9.0469 | 0.0059 | 162 | 9.0469 | 0.0901 |
| 9.0859 | 0.0059 | 163 | 9.0391 | 0.0905 |
| 9.0078 | 0.0060 | 164 | 9.0312 | 0.0908 |
| 9.0156 | 0.0060 | 165 | 9.0312 | 0.0909 |
| 9.0469 | 0.0060 | 166 | 9.0234 | 0.0909 |
| 8.9219 | 0.0061 | 167 | 9.0234 | 0.0908 |
| 9.0312 | 0.0061 | 168 | 9.0156 | 0.0907 |
| 9.0938 | 0.0061 | 169 | 9.0078 | 0.0906 |
| 9.0156 | 0.0062 | 170 | 9.0 | 0.0902 |
| 9.0312 | 0.0062 | 171 | 9.0 | 0.0897 |
| 9.0625 | 0.0062 | 172 | 8.9922 | 0.0893 |
| 8.9844 | 0.0063 | 173 | 8.9844 | 0.0891 |
| 9.0703 | 0.0063 | 174 | 8.9844 | 0.0894 |
| 8.9609 | 0.0064 | 175 | 8.9766 | 0.0898 |
| 8.9922 | 0.0064 | 176 | 8.9766 | 0.0905 |
| 9.0234 | 0.0064 | 177 | 8.9688 | 0.0910 |
| 9.0234 | 0.0065 | 178 | 8.9609 | 0.0915 |
| 8.9219 | 0.0065 | 179 | 8.9531 | 0.0919 |
| 9.0234 | 0.0065 | 180 | 8.9531 | 0.0920 |
| 8.9375 | 0.0066 | 181 | 8.9453 | 0.0921 |
| 8.9688 | 0.0066 | 182 | 8.9375 | 0.0919 |
| 8.9375 | 0.0066 | 183 | 8.9375 | 0.0913 |
| 9.0 | 0.0067 | 184 | 8.9297 | 0.0912 |
| 8.9375 | 0.0067 | 185 | 8.9219 | 0.0913 |
| 8.9609 | 0.0068 | 186 | 8.9219 | 0.0913 |
| 8.9688 | 0.0068 | 187 | 8.9141 | 0.0917 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_6_256 | gokulsrinivasagan | 2024-07-02T16:09:26Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:55:52Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_6_256
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.08509852797509099
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_6_256
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 9.4766
- Accuracy: 0.0851
## Model description
More information needed
## Intended uses & 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: 36
- eval_batch_size: 36
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.8672 | 0.0001 | 1 | 10.8672 | 0.0045 |
| 10.8672 | 0.0001 | 2 | 10.8672 | 0.0045 |
| 10.8672 | 0.0002 | 3 | 10.8672 | 0.0045 |
| 10.8672 | 0.0002 | 4 | 10.8672 | 0.0045 |
| 10.8594 | 0.0003 | 5 | 10.8672 | 0.0045 |
| 10.8672 | 0.0003 | 6 | 10.8672 | 0.0045 |
| 10.8672 | 0.0004 | 7 | 10.8672 | 0.0045 |
| 10.8672 | 0.0004 | 8 | 10.8672 | 0.0045 |
| 10.8672 | 0.0005 | 9 | 10.8672 | 0.0045 |
| 10.8594 | 0.0005 | 10 | 10.8672 | 0.0045 |
| 10.8672 | 0.0006 | 11 | 10.8672 | 0.0045 |
| 10.8672 | 0.0007 | 12 | 10.8672 | 0.0045 |
| 10.8594 | 0.0007 | 13 | 10.8672 | 0.0045 |
| 10.8672 | 0.0008 | 14 | 10.8672 | 0.0045 |
| 10.8594 | 0.0008 | 15 | 10.8672 | 0.0045 |
| 10.8594 | 0.0009 | 16 | 10.8672 | 0.0045 |
| 10.8672 | 0.0009 | 17 | 10.8672 | 0.0045 |
| 10.8672 | 0.0010 | 18 | 10.8359 | 0.0086 |
| 10.8359 | 0.0010 | 19 | 10.8047 | 0.0108 |
| 10.8047 | 0.0011 | 20 | 10.7734 | 0.0113 |
| 10.7891 | 0.0011 | 21 | 10.75 | 0.0115 |
| 10.7578 | 0.0012 | 22 | 10.7266 | 0.0119 |
| 10.7188 | 0.0013 | 23 | 10.7031 | 0.0129 |
| 10.7188 | 0.0013 | 24 | 10.6797 | 0.0147 |
| 10.6953 | 0.0014 | 25 | 10.6641 | 0.0179 |
| 10.6719 | 0.0014 | 26 | 10.6406 | 0.0231 |
| 10.6562 | 0.0015 | 27 | 10.625 | 0.0286 |
| 10.6641 | 0.0015 | 28 | 10.6094 | 0.0347 |
| 10.6328 | 0.0016 | 29 | 10.5938 | 0.0399 |
| 10.6016 | 0.0016 | 30 | 10.5781 | 0.0436 |
| 10.6016 | 0.0017 | 31 | 10.5703 | 0.0463 |
| 10.5938 | 0.0017 | 32 | 10.5547 | 0.0479 |
| 10.5781 | 0.0018 | 33 | 10.5469 | 0.0484 |
| 10.5547 | 0.0019 | 34 | 10.5312 | 0.0484 |
| 10.5469 | 0.0019 | 35 | 10.5234 | 0.0484 |
| 10.5391 | 0.0020 | 36 | 10.5156 | 0.0482 |
| 10.5312 | 0.0020 | 37 | 10.5078 | 0.0475 |
| 10.5312 | 0.0021 | 38 | 10.4922 | 0.0475 |
| 10.4922 | 0.0021 | 39 | 10.4844 | 0.0476 |
| 10.5078 | 0.0022 | 40 | 10.4844 | 0.0477 |
| 10.4922 | 0.0022 | 41 | 10.4766 | 0.0481 |
| 10.4844 | 0.0023 | 42 | 10.4688 | 0.0486 |
| 10.4766 | 0.0023 | 43 | 10.4609 | 0.0493 |
| 10.4844 | 0.0024 | 44 | 10.4531 | 0.0495 |
| 10.4688 | 0.0025 | 45 | 10.4453 | 0.0503 |
| 10.4844 | 0.0025 | 46 | 10.4453 | 0.0513 |
| 10.4609 | 0.0026 | 47 | 10.4375 | 0.0522 |
| 10.4453 | 0.0026 | 48 | 10.4297 | 0.0526 |
| 10.4453 | 0.0027 | 49 | 10.4297 | 0.0532 |
| 10.4297 | 0.0027 | 50 | 10.4219 | 0.0537 |
| 10.4219 | 0.0028 | 51 | 10.4141 | 0.0544 |
| 10.4297 | 0.0028 | 52 | 10.4141 | 0.0548 |
| 10.4375 | 0.0029 | 53 | 10.4062 | 0.0554 |
| 10.4219 | 0.0029 | 54 | 10.4062 | 0.0558 |
| 10.4141 | 0.0030 | 55 | 10.3984 | 0.0565 |
| 10.4141 | 0.0031 | 56 | 10.3906 | 0.0574 |
| 10.4219 | 0.0031 | 57 | 10.3906 | 0.0583 |
| 10.4219 | 0.0032 | 58 | 10.3828 | 0.0591 |
| 10.3984 | 0.0032 | 59 | 10.3828 | 0.0598 |
| 10.3984 | 0.0033 | 60 | 10.375 | 0.0603 |
| 10.3984 | 0.0033 | 61 | 10.375 | 0.0607 |
| 10.3906 | 0.0034 | 62 | 10.3672 | 0.0611 |
| 10.3672 | 0.0034 | 63 | 10.3672 | 0.0615 |
| 10.3906 | 0.0035 | 64 | 10.3594 | 0.0616 |
| 10.3828 | 0.0035 | 65 | 10.3594 | 0.0615 |
| 10.3594 | 0.0036 | 66 | 10.3516 | 0.0614 |
| 10.3516 | 0.0037 | 67 | 10.3438 | 0.0610 |
| 10.3516 | 0.0037 | 68 | 10.3438 | 0.0609 |
| 10.3438 | 0.0038 | 69 | 10.3359 | 0.0611 |
| 10.3594 | 0.0038 | 70 | 10.3359 | 0.0610 |
| 10.3594 | 0.0039 | 71 | 10.3281 | 0.0610 |
| 10.3203 | 0.0039 | 72 | 10.3281 | 0.0610 |
| 10.3516 | 0.0040 | 73 | 10.3203 | 0.0610 |
| 10.3203 | 0.0040 | 74 | 10.3125 | 0.0611 |
| 10.3281 | 0.0041 | 75 | 10.3125 | 0.0612 |
| 10.3438 | 0.0041 | 76 | 10.3047 | 0.0614 |
| 10.2969 | 0.0042 | 77 | 10.3047 | 0.0618 |
| 10.3281 | 0.0043 | 78 | 10.2969 | 0.0622 |
| 10.2891 | 0.0043 | 79 | 10.2969 | 0.0628 |
| 10.3047 | 0.0044 | 80 | 10.2891 | 0.0632 |
| 10.2969 | 0.0044 | 81 | 10.2812 | 0.0637 |
| 10.2891 | 0.0045 | 82 | 10.2812 | 0.0643 |
| 10.3125 | 0.0045 | 83 | 10.2734 | 0.0649 |
| 10.2891 | 0.0046 | 84 | 10.2734 | 0.0654 |
| 10.2812 | 0.0046 | 85 | 10.2656 | 0.0657 |
| 10.3047 | 0.0047 | 86 | 10.2656 | 0.0659 |
| 10.2969 | 0.0047 | 87 | 10.2578 | 0.0660 |
| 10.2578 | 0.0048 | 88 | 10.25 | 0.0661 |
| 10.2812 | 0.0048 | 89 | 10.25 | 0.0662 |
| 10.2734 | 0.0049 | 90 | 10.2422 | 0.0663 |
| 10.2891 | 0.0050 | 91 | 10.2422 | 0.0664 |
| 10.2578 | 0.0050 | 92 | 10.2344 | 0.0666 |
| 10.2734 | 0.0051 | 93 | 10.2344 | 0.0668 |
| 10.2266 | 0.0051 | 94 | 10.2266 | 0.0671 |
| 10.2578 | 0.0052 | 95 | 10.2266 | 0.0674 |
| 10.25 | 0.0052 | 96 | 10.2188 | 0.0676 |
| 10.2266 | 0.0053 | 97 | 10.2188 | 0.0678 |
| 10.2266 | 0.0053 | 98 | 10.2109 | 0.0679 |
| 10.2344 | 0.0054 | 99 | 10.2109 | 0.0681 |
| 10.2422 | 0.0054 | 100 | 10.2031 | 0.0682 |
| 10.2422 | 0.0055 | 101 | 10.2031 | 0.0683 |
| 10.2266 | 0.0056 | 102 | 10.1953 | 0.0685 |
| 10.2188 | 0.0056 | 103 | 10.1953 | 0.0686 |
| 10.2109 | 0.0057 | 104 | 10.1875 | 0.0687 |
| 10.1797 | 0.0057 | 105 | 10.1875 | 0.0689 |
| 10.1797 | 0.0058 | 106 | 10.1797 | 0.0691 |
| 10.1719 | 0.0058 | 107 | 10.1797 | 0.0693 |
| 10.1875 | 0.0059 | 108 | 10.1719 | 0.0696 |
| 10.1797 | 0.0059 | 109 | 10.1719 | 0.0698 |
| 10.1797 | 0.0060 | 110 | 10.1641 | 0.0700 |
| 10.1406 | 0.0060 | 111 | 10.1641 | 0.0702 |
| 10.1719 | 0.0061 | 112 | 10.1641 | 0.0704 |
| 10.1953 | 0.0062 | 113 | 10.1562 | 0.0706 |
| 10.1719 | 0.0062 | 114 | 10.1562 | 0.0708 |
| 10.1641 | 0.0063 | 115 | 10.1484 | 0.0710 |
| 10.1719 | 0.0063 | 116 | 10.1484 | 0.0712 |
| 10.1484 | 0.0064 | 117 | 10.1406 | 0.0713 |
| 10.1562 | 0.0064 | 118 | 10.1406 | 0.0715 |
| 10.1562 | 0.0065 | 119 | 10.1328 | 0.0716 |
| 10.1484 | 0.0065 | 120 | 10.1328 | 0.0718 |
| 10.1406 | 0.0066 | 121 | 10.125 | 0.0719 |
| 10.1328 | 0.0066 | 122 | 10.125 | 0.0721 |
| 10.1641 | 0.0067 | 123 | 10.1172 | 0.0722 |
| 10.1328 | 0.0068 | 124 | 10.1172 | 0.0723 |
| 10.1484 | 0.0068 | 125 | 10.1094 | 0.0725 |
| 10.1406 | 0.0069 | 126 | 10.1094 | 0.0726 |
| 10.1406 | 0.0069 | 127 | 10.1016 | 0.0728 |
| 10.125 | 0.0070 | 128 | 10.1016 | 0.0729 |
| 10.1172 | 0.0070 | 129 | 10.0938 | 0.0731 |
| 10.1016 | 0.0071 | 130 | 10.0938 | 0.0732 |
| 10.1172 | 0.0071 | 131 | 10.0859 | 0.0733 |
| 10.1172 | 0.0072 | 132 | 10.0859 | 0.0734 |
| 10.1172 | 0.0072 | 133 | 10.0859 | 0.0736 |
| 10.0938 | 0.0073 | 134 | 10.0781 | 0.0737 |
| 10.1094 | 0.0074 | 135 | 10.0781 | 0.0738 |
| 10.1094 | 0.0074 | 136 | 10.0703 | 0.0740 |
| 10.0703 | 0.0075 | 137 | 10.0703 | 0.0742 |
| 10.0781 | 0.0075 | 138 | 10.0625 | 0.0743 |
| 10.0781 | 0.0076 | 139 | 10.0625 | 0.0745 |
| 10.0781 | 0.0076 | 140 | 10.0547 | 0.0746 |
| 10.0625 | 0.0077 | 141 | 10.0547 | 0.0747 |
| 10.0781 | 0.0077 | 142 | 10.0469 | 0.0749 |
| 10.0391 | 0.0078 | 143 | 10.0469 | 0.0750 |
| 10.0703 | 0.0078 | 144 | 10.0469 | 0.0751 |
| 10.0391 | 0.0079 | 145 | 10.0391 | 0.0753 |
| 10.0469 | 0.0080 | 146 | 10.0391 | 0.0754 |
| 10.0547 | 0.0080 | 147 | 10.0312 | 0.0755 |
| 10.0703 | 0.0081 | 148 | 10.0312 | 0.0756 |
| 10.0469 | 0.0081 | 149 | 10.0234 | 0.0757 |
| 10.0391 | 0.0082 | 150 | 10.0234 | 0.0759 |
| 10.0391 | 0.0082 | 151 | 10.0156 | 0.0760 |
| 10.0391 | 0.0083 | 152 | 10.0156 | 0.0761 |
| 10.0391 | 0.0083 | 153 | 10.0156 | 0.0762 |
| 10.0469 | 0.0084 | 154 | 10.0078 | 0.0763 |
| 10.0312 | 0.0084 | 155 | 10.0078 | 0.0765 |
| 9.9844 | 0.0085 | 156 | 10.0 | 0.0766 |
| 10.0 | 0.0086 | 157 | 10.0 | 0.0767 |
| 10.0078 | 0.0086 | 158 | 9.9922 | 0.0768 |
| 10.0078 | 0.0087 | 159 | 9.9922 | 0.0769 |
| 10.0234 | 0.0087 | 160 | 9.9922 | 0.0770 |
| 9.9922 | 0.0088 | 161 | 9.9844 | 0.0771 |
| 9.9922 | 0.0088 | 162 | 9.9844 | 0.0772 |
| 9.9766 | 0.0089 | 163 | 9.9766 | 0.0773 |
| 9.9922 | 0.0089 | 164 | 9.9766 | 0.0773 |
| 9.9766 | 0.0090 | 165 | 9.9688 | 0.0774 |
| 9.9844 | 0.0090 | 166 | 9.9688 | 0.0775 |
| 9.9766 | 0.0091 | 167 | 9.9688 | 0.0776 |
| 9.9844 | 0.0092 | 168 | 9.9609 | 0.0777 |
| 9.9609 | 0.0092 | 169 | 9.9609 | 0.0778 |
| 9.9766 | 0.0093 | 170 | 9.9531 | 0.0778 |
| 9.9531 | 0.0093 | 171 | 9.9531 | 0.0779 |
| 9.9922 | 0.0094 | 172 | 9.9531 | 0.0780 |
| 9.9531 | 0.0094 | 173 | 9.9453 | 0.0781 |
| 9.9375 | 0.0095 | 174 | 9.9453 | 0.0781 |
| 9.9688 | 0.0095 | 175 | 9.9375 | 0.0782 |
| 9.9453 | 0.0096 | 176 | 9.9375 | 0.0783 |
| 9.9375 | 0.0096 | 177 | 9.9375 | 0.0783 |
| 9.9375 | 0.0097 | 178 | 9.9297 | 0.0784 |
| 9.9453 | 0.0098 | 179 | 9.9297 | 0.0785 |
| 9.9453 | 0.0098 | 180 | 9.9219 | 0.0786 |
| 9.9297 | 0.0099 | 181 | 9.9219 | 0.0787 |
| 9.9375 | 0.0099 | 182 | 9.9141 | 0.0787 |
| 9.9375 | 0.0100 | 183 | 9.9141 | 0.0788 |
| 9.8984 | 0.0100 | 184 | 9.9141 | 0.0789 |
| 9.9375 | 0.0101 | 185 | 9.9062 | 0.0790 |
| 9.9297 | 0.0101 | 186 | 9.9062 | 0.0791 |
| 9.9297 | 0.0102 | 187 | 9.8984 | 0.0791 |
| 9.9141 | 0.0102 | 188 | 9.8984 | 0.0792 |
| 9.9219 | 0.0103 | 189 | 9.8984 | 0.0793 |
| 9.8984 | 0.0104 | 190 | 9.8906 | 0.0793 |
| 9.8828 | 0.0104 | 191 | 9.8906 | 0.0794 |
| 9.8984 | 0.0105 | 192 | 9.8828 | 0.0795 |
| 9.8906 | 0.0105 | 193 | 9.8828 | 0.0796 |
| 9.9062 | 0.0106 | 194 | 9.8828 | 0.0797 |
| 9.875 | 0.0106 | 195 | 9.875 | 0.0798 |
| 9.8594 | 0.0107 | 196 | 9.875 | 0.0798 |
| 9.8828 | 0.0107 | 197 | 9.875 | 0.0799 |
| 9.8984 | 0.0108 | 198 | 9.8672 | 0.0800 |
| 9.8906 | 0.0108 | 199 | 9.8672 | 0.0801 |
| 9.9062 | 0.0109 | 200 | 9.8594 | 0.0801 |
| 9.8672 | 0.0110 | 201 | 9.8594 | 0.0802 |
| 9.8672 | 0.0110 | 202 | 9.8594 | 0.0803 |
| 9.8906 | 0.0111 | 203 | 9.8516 | 0.0804 |
| 9.8828 | 0.0111 | 204 | 9.8516 | 0.0804 |
| 9.8906 | 0.0112 | 205 | 9.8438 | 0.0805 |
| 9.8828 | 0.0112 | 206 | 9.8438 | 0.0805 |
| 9.8594 | 0.0113 | 207 | 9.8438 | 0.0806 |
| 9.875 | 0.0113 | 208 | 9.8359 | 0.0806 |
| 9.8594 | 0.0114 | 209 | 9.8359 | 0.0807 |
| 9.8516 | 0.0114 | 210 | 9.8281 | 0.0808 |
| 9.8359 | 0.0115 | 211 | 9.8281 | 0.0809 |
| 9.8281 | 0.0116 | 212 | 9.8281 | 0.0810 |
| 9.8516 | 0.0116 | 213 | 9.8203 | 0.0810 |
| 9.8516 | 0.0117 | 214 | 9.8203 | 0.0811 |
| 9.8281 | 0.0117 | 215 | 9.8203 | 0.0811 |
| 9.8438 | 0.0118 | 216 | 9.8125 | 0.0812 |
| 9.8359 | 0.0118 | 217 | 9.8125 | 0.0813 |
| 9.8281 | 0.0119 | 218 | 9.8047 | 0.0814 |
| 9.8281 | 0.0119 | 219 | 9.8047 | 0.0815 |
| 9.8281 | 0.0120 | 220 | 9.8047 | 0.0815 |
| 9.7969 | 0.0120 | 221 | 9.7969 | 0.0816 |
| 9.8281 | 0.0121 | 222 | 9.7969 | 0.0816 |
| 9.8047 | 0.0122 | 223 | 9.7891 | 0.0817 |
| 9.8047 | 0.0122 | 224 | 9.7891 | 0.0818 |
| 9.8047 | 0.0123 | 225 | 9.7891 | 0.0818 |
| 9.8047 | 0.0123 | 226 | 9.7812 | 0.0819 |
| 9.8281 | 0.0124 | 227 | 9.7812 | 0.0819 |
| 9.7812 | 0.0124 | 228 | 9.7812 | 0.0819 |
| 9.7891 | 0.0125 | 229 | 9.7734 | 0.0820 |
| 9.7969 | 0.0125 | 230 | 9.7734 | 0.0821 |
| 9.7578 | 0.0126 | 231 | 9.7656 | 0.0821 |
| 9.8125 | 0.0126 | 232 | 9.7656 | 0.0822 |
| 9.7734 | 0.0127 | 233 | 9.7656 | 0.0823 |
| 9.7656 | 0.0128 | 234 | 9.7578 | 0.0823 |
| 9.7578 | 0.0128 | 235 | 9.7578 | 0.0824 |
| 9.7891 | 0.0129 | 236 | 9.7578 | 0.0824 |
| 9.7812 | 0.0129 | 237 | 9.75 | 0.0824 |
| 9.7656 | 0.0130 | 238 | 9.75 | 0.0825 |
| 9.7969 | 0.0130 | 239 | 9.75 | 0.0825 |
| 9.75 | 0.0131 | 240 | 9.7422 | 0.0825 |
| 9.7734 | 0.0131 | 241 | 9.7422 | 0.0825 |
| 9.7578 | 0.0132 | 242 | 9.7344 | 0.0825 |
| 9.7656 | 0.0132 | 243 | 9.7344 | 0.0825 |
| 9.7266 | 0.0133 | 244 | 9.7344 | 0.0826 |
| 9.75 | 0.0134 | 245 | 9.7266 | 0.0826 |
| 9.7422 | 0.0134 | 246 | 9.7266 | 0.0827 |
| 9.75 | 0.0135 | 247 | 9.7266 | 0.0827 |
| 9.7656 | 0.0135 | 248 | 9.7188 | 0.0828 |
| 9.7266 | 0.0136 | 249 | 9.7188 | 0.0828 |
| 9.75 | 0.0136 | 250 | 9.7109 | 0.0828 |
| 9.7266 | 0.0137 | 251 | 9.7109 | 0.0829 |
| 9.7266 | 0.0137 | 252 | 9.7109 | 0.0829 |
| 9.7266 | 0.0138 | 253 | 9.7031 | 0.0829 |
| 9.7266 | 0.0138 | 254 | 9.7031 | 0.0829 |
| 9.7344 | 0.0139 | 255 | 9.7031 | 0.0829 |
| 9.7109 | 0.0139 | 256 | 9.6953 | 0.0829 |
| 9.7109 | 0.0140 | 257 | 9.6953 | 0.0829 |
| 9.7109 | 0.0141 | 258 | 9.6953 | 0.0830 |
| 9.7031 | 0.0141 | 259 | 9.6875 | 0.0830 |
| 9.7109 | 0.0142 | 260 | 9.6875 | 0.0831 |
| 9.6953 | 0.0142 | 261 | 9.6797 | 0.0832 |
| 9.7031 | 0.0143 | 262 | 9.6797 | 0.0832 |
| 9.6953 | 0.0143 | 263 | 9.6797 | 0.0832 |
| 9.6875 | 0.0144 | 264 | 9.6719 | 0.0833 |
| 9.6719 | 0.0144 | 265 | 9.6719 | 0.0833 |
| 9.6797 | 0.0145 | 266 | 9.6719 | 0.0832 |
| 9.7188 | 0.0145 | 267 | 9.6641 | 0.0833 |
| 9.6953 | 0.0146 | 268 | 9.6641 | 0.0833 |
| 9.6797 | 0.0147 | 269 | 9.6641 | 0.0833 |
| 9.6719 | 0.0147 | 270 | 9.6562 | 0.0834 |
| 9.6875 | 0.0148 | 271 | 9.6562 | 0.0834 |
| 9.6641 | 0.0148 | 272 | 9.6484 | 0.0835 |
| 9.6719 | 0.0149 | 273 | 9.6484 | 0.0836 |
| 9.6719 | 0.0149 | 274 | 9.6484 | 0.0836 |
| 9.6406 | 0.0150 | 275 | 9.6406 | 0.0837 |
| 9.6641 | 0.0150 | 276 | 9.6406 | 0.0837 |
| 9.6328 | 0.0151 | 277 | 9.6406 | 0.0838 |
| 9.6328 | 0.0151 | 278 | 9.6328 | 0.0838 |
| 9.6484 | 0.0152 | 279 | 9.6328 | 0.0838 |
| 9.6484 | 0.0153 | 280 | 9.6328 | 0.0838 |
| 9.6875 | 0.0153 | 281 | 9.625 | 0.0838 |
| 9.6328 | 0.0154 | 282 | 9.625 | 0.0838 |
| 9.6562 | 0.0154 | 283 | 9.6172 | 0.0838 |
| 9.6719 | 0.0155 | 284 | 9.6172 | 0.0838 |
| 9.6641 | 0.0155 | 285 | 9.6172 | 0.0838 |
| 9.6328 | 0.0156 | 286 | 9.6094 | 0.0838 |
| 9.6328 | 0.0156 | 287 | 9.6094 | 0.0839 |
| 9.625 | 0.0157 | 288 | 9.6094 | 0.0839 |
| 9.6328 | 0.0157 | 289 | 9.6016 | 0.0840 |
| 9.6172 | 0.0158 | 290 | 9.6016 | 0.0840 |
| 9.6172 | 0.0159 | 291 | 9.6016 | 0.0841 |
| 9.6094 | 0.0159 | 292 | 9.5938 | 0.0841 |
| 9.6172 | 0.0160 | 293 | 9.5938 | 0.0842 |
| 9.6094 | 0.0160 | 294 | 9.5938 | 0.0842 |
| 9.6328 | 0.0161 | 295 | 9.5859 | 0.0842 |
| 9.5938 | 0.0161 | 296 | 9.5859 | 0.0842 |
| 9.5938 | 0.0162 | 297 | 9.5781 | 0.0842 |
| 9.6016 | 0.0162 | 298 | 9.5781 | 0.0842 |
| 9.5781 | 0.0163 | 299 | 9.5781 | 0.0842 |
| 9.5938 | 0.0163 | 300 | 9.5703 | 0.0843 |
| 9.5938 | 0.0164 | 301 | 9.5703 | 0.0843 |
| 9.6016 | 0.0165 | 302 | 9.5703 | 0.0844 |
| 9.5781 | 0.0165 | 303 | 9.5625 | 0.0845 |
| 9.6016 | 0.0166 | 304 | 9.5625 | 0.0845 |
| 9.5703 | 0.0166 | 305 | 9.5625 | 0.0845 |
| 9.5781 | 0.0167 | 306 | 9.5547 | 0.0845 |
| 9.5938 | 0.0167 | 307 | 9.5547 | 0.0846 |
| 9.5391 | 0.0168 | 308 | 9.5547 | 0.0846 |
| 9.5625 | 0.0168 | 309 | 9.5469 | 0.0846 |
| 9.5547 | 0.0169 | 310 | 9.5469 | 0.0846 |
| 9.5703 | 0.0169 | 311 | 9.5469 | 0.0846 |
| 9.5625 | 0.0170 | 312 | 9.5391 | 0.0846 |
| 9.5469 | 0.0171 | 313 | 9.5391 | 0.0846 |
| 9.5469 | 0.0171 | 314 | 9.5391 | 0.0846 |
| 9.5391 | 0.0172 | 315 | 9.5312 | 0.0847 |
| 9.5781 | 0.0172 | 316 | 9.5312 | 0.0847 |
| 9.5469 | 0.0173 | 317 | 9.5312 | 0.0847 |
| 9.5312 | 0.0173 | 318 | 9.5234 | 0.0848 |
| 9.5703 | 0.0174 | 319 | 9.5234 | 0.0848 |
| 9.5312 | 0.0174 | 320 | 9.5234 | 0.0848 |
| 9.5703 | 0.0175 | 321 | 9.5156 | 0.0848 |
| 9.5312 | 0.0175 | 322 | 9.5156 | 0.0849 |
| 9.5391 | 0.0176 | 323 | 9.5078 | 0.0849 |
| 9.5156 | 0.0177 | 324 | 9.5078 | 0.0849 |
| 9.5234 | 0.0177 | 325 | 9.5078 | 0.0849 |
| 9.5391 | 0.0178 | 326 | 9.5 | 0.0849 |
| 9.5078 | 0.0178 | 327 | 9.5 | 0.0849 |
| 9.5312 | 0.0179 | 328 | 9.5 | 0.0848 |
| 9.5078 | 0.0179 | 329 | 9.4922 | 0.0848 |
| 9.5234 | 0.0180 | 330 | 9.4922 | 0.0847 |
| 9.5078 | 0.0180 | 331 | 9.4922 | 0.0848 |
| 9.4922 | 0.0181 | 332 | 9.4844 | 0.0848 |
| 9.5 | 0.0181 | 333 | 9.4844 | 0.0849 |
| 9.5078 | 0.0182 | 334 | 9.4844 | 0.0850 |
| 9.4766 | 0.0183 | 335 | 9.4766 | 0.0851 |
| 9.5 | 0.0183 | 336 | 9.4766 | 0.0851 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
gokulsrinivasagan/gpt_train_12_256 | gokulsrinivasagan | 2024-07-02T16:11:14Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"dataset:gokuls/wiki_book_corpus_raw_dataset_tiny",
"base_model:openai-community/gpt2",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:56:23Z | ---
license: mit
base_model: openai-community/gpt2
tags:
- generated_from_trainer
datasets:
- gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- accuracy
model-index:
- name: gpt_train_12_256
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: gokuls/wiki_book_corpus_raw_dataset_tiny
type: gokuls/wiki_book_corpus_raw_dataset_tiny
metrics:
- name: Accuracy
type: accuracy
value: 0.08778952977191952
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt_train_12_256
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/gpt2) on the gokuls/wiki_book_corpus_raw_dataset_tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 9.6016
- Accuracy: 0.0878
## Model description
More information needed
## Intended uses & 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: 36
- eval_batch_size: 36
- seed: 10
- distributed_type: multi-GPU
- 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 | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 10.875 | 0.0001 | 1 | 10.875 | 0.0031 |
| 10.875 | 0.0001 | 2 | 10.875 | 0.0031 |
| 10.875 | 0.0002 | 3 | 10.875 | 0.0031 |
| 10.875 | 0.0002 | 4 | 10.875 | 0.0031 |
| 10.8672 | 0.0003 | 5 | 10.875 | 0.0031 |
| 10.875 | 0.0003 | 6 | 10.875 | 0.0031 |
| 10.8672 | 0.0004 | 7 | 10.875 | 0.0031 |
| 10.875 | 0.0004 | 8 | 10.875 | 0.0031 |
| 10.875 | 0.0005 | 9 | 10.875 | 0.0031 |
| 10.875 | 0.0005 | 10 | 10.875 | 0.0031 |
| 10.875 | 0.0006 | 11 | 10.875 | 0.0031 |
| 10.875 | 0.0007 | 12 | 10.875 | 0.0031 |
| 10.875 | 0.0007 | 13 | 10.875 | 0.0031 |
| 10.875 | 0.0008 | 14 | 10.875 | 0.0031 |
| 10.875 | 0.0008 | 15 | 10.875 | 0.0031 |
| 10.875 | 0.0009 | 16 | 10.875 | 0.0031 |
| 10.8672 | 0.0009 | 17 | 10.875 | 0.0031 |
| 10.875 | 0.0010 | 18 | 10.8047 | 0.0103 |
| 10.8125 | 0.0010 | 19 | 10.75 | 0.0119 |
| 10.7578 | 0.0011 | 20 | 10.6953 | 0.0180 |
| 10.7188 | 0.0011 | 21 | 10.6562 | 0.0319 |
| 10.6719 | 0.0012 | 22 | 10.625 | 0.0470 |
| 10.6328 | 0.0013 | 23 | 10.5938 | 0.0530 |
| 10.6172 | 0.0013 | 24 | 10.5703 | 0.0542 |
| 10.5859 | 0.0014 | 25 | 10.5469 | 0.0543 |
| 10.5547 | 0.0014 | 26 | 10.5312 | 0.0540 |
| 10.5391 | 0.0015 | 27 | 10.5156 | 0.0534 |
| 10.5547 | 0.0015 | 28 | 10.5 | 0.0531 |
| 10.5156 | 0.0016 | 29 | 10.4844 | 0.0535 |
| 10.4844 | 0.0016 | 30 | 10.4766 | 0.0542 |
| 10.4844 | 0.0017 | 31 | 10.4609 | 0.0548 |
| 10.4766 | 0.0017 | 32 | 10.4531 | 0.0551 |
| 10.4766 | 0.0018 | 33 | 10.4453 | 0.0557 |
| 10.4531 | 0.0019 | 34 | 10.4375 | 0.0565 |
| 10.4453 | 0.0019 | 35 | 10.4297 | 0.0570 |
| 10.4375 | 0.0020 | 36 | 10.4219 | 0.0575 |
| 10.4375 | 0.0020 | 37 | 10.4141 | 0.0581 |
| 10.4453 | 0.0021 | 38 | 10.4141 | 0.0583 |
| 10.3984 | 0.0021 | 39 | 10.4062 | 0.0585 |
| 10.4141 | 0.0022 | 40 | 10.3984 | 0.0586 |
| 10.4062 | 0.0022 | 41 | 10.3906 | 0.0587 |
| 10.3984 | 0.0023 | 42 | 10.3906 | 0.0587 |
| 10.3906 | 0.0023 | 43 | 10.3828 | 0.0588 |
| 10.4062 | 0.0024 | 44 | 10.375 | 0.0591 |
| 10.375 | 0.0025 | 45 | 10.375 | 0.0592 |
| 10.3984 | 0.0025 | 46 | 10.3672 | 0.0592 |
| 10.3828 | 0.0026 | 47 | 10.3594 | 0.0593 |
| 10.375 | 0.0026 | 48 | 10.3516 | 0.0597 |
| 10.3594 | 0.0027 | 49 | 10.3516 | 0.0599 |
| 10.3516 | 0.0027 | 50 | 10.3438 | 0.0602 |
| 10.3438 | 0.0028 | 51 | 10.3359 | 0.0604 |
| 10.3516 | 0.0028 | 52 | 10.3281 | 0.0606 |
| 10.3594 | 0.0029 | 53 | 10.3281 | 0.0607 |
| 10.3438 | 0.0029 | 54 | 10.3203 | 0.0608 |
| 10.3281 | 0.0030 | 55 | 10.3125 | 0.0608 |
| 10.3281 | 0.0031 | 56 | 10.3125 | 0.0607 |
| 10.3281 | 0.0031 | 57 | 10.3047 | 0.0607 |
| 10.3438 | 0.0032 | 58 | 10.3047 | 0.0607 |
| 10.3125 | 0.0032 | 59 | 10.2969 | 0.0609 |
| 10.3203 | 0.0033 | 60 | 10.2969 | 0.0612 |
| 10.3125 | 0.0033 | 61 | 10.2891 | 0.0615 |
| 10.2969 | 0.0034 | 62 | 10.2812 | 0.0618 |
| 10.2891 | 0.0034 | 63 | 10.2812 | 0.0620 |
| 10.2969 | 0.0035 | 64 | 10.2734 | 0.0622 |
| 10.2891 | 0.0035 | 65 | 10.2734 | 0.0622 |
| 10.2734 | 0.0036 | 66 | 10.2656 | 0.0623 |
| 10.2656 | 0.0037 | 67 | 10.2656 | 0.0623 |
| 10.2656 | 0.0037 | 68 | 10.2578 | 0.0623 |
| 10.2578 | 0.0038 | 69 | 10.25 | 0.0622 |
| 10.25 | 0.0038 | 70 | 10.25 | 0.0622 |
| 10.2656 | 0.0039 | 71 | 10.2422 | 0.0623 |
| 10.2344 | 0.0039 | 72 | 10.2422 | 0.0626 |
| 10.2578 | 0.0040 | 73 | 10.2344 | 0.0629 |
| 10.2266 | 0.0040 | 74 | 10.2344 | 0.0632 |
| 10.2422 | 0.0041 | 75 | 10.2266 | 0.0633 |
| 10.2656 | 0.0041 | 76 | 10.2266 | 0.0633 |
| 10.2266 | 0.0042 | 77 | 10.2188 | 0.0632 |
| 10.2422 | 0.0043 | 78 | 10.2188 | 0.0631 |
| 10.2031 | 0.0043 | 79 | 10.2109 | 0.0630 |
| 10.2031 | 0.0044 | 80 | 10.2109 | 0.0631 |
| 10.2188 | 0.0044 | 81 | 10.2031 | 0.0633 |
| 10.2188 | 0.0045 | 82 | 10.2031 | 0.0637 |
| 10.2344 | 0.0045 | 83 | 10.1953 | 0.0641 |
| 10.2188 | 0.0046 | 84 | 10.1953 | 0.0647 |
| 10.2031 | 0.0046 | 85 | 10.1875 | 0.0653 |
| 10.2266 | 0.0047 | 86 | 10.1875 | 0.0657 |
| 10.2109 | 0.0047 | 87 | 10.1797 | 0.0660 |
| 10.1641 | 0.0048 | 88 | 10.1797 | 0.0660 |
| 10.1953 | 0.0048 | 89 | 10.1719 | 0.0660 |
| 10.1875 | 0.0049 | 90 | 10.1719 | 0.0658 |
| 10.2031 | 0.0050 | 91 | 10.1641 | 0.0658 |
| 10.1719 | 0.0050 | 92 | 10.1641 | 0.0658 |
| 10.1953 | 0.0051 | 93 | 10.1562 | 0.0660 |
| 10.1641 | 0.0051 | 94 | 10.1562 | 0.0665 |
| 10.1797 | 0.0052 | 95 | 10.1484 | 0.0673 |
| 10.1797 | 0.0052 | 96 | 10.1484 | 0.0682 |
| 10.1406 | 0.0053 | 97 | 10.1406 | 0.0690 |
| 10.1562 | 0.0053 | 98 | 10.1406 | 0.0696 |
| 10.1406 | 0.0054 | 99 | 10.1328 | 0.0699 |
| 10.1641 | 0.0054 | 100 | 10.1328 | 0.0700 |
| 10.1797 | 0.0055 | 101 | 10.125 | 0.0699 |
| 10.1484 | 0.0056 | 102 | 10.125 | 0.0699 |
| 10.1406 | 0.0056 | 103 | 10.1172 | 0.0701 |
| 10.1328 | 0.0057 | 104 | 10.1172 | 0.0706 |
| 10.0938 | 0.0057 | 105 | 10.1094 | 0.0712 |
| 10.1016 | 0.0058 | 106 | 10.1094 | 0.0719 |
| 10.1016 | 0.0058 | 107 | 10.1016 | 0.0725 |
| 10.1094 | 0.0059 | 108 | 10.1016 | 0.0728 |
| 10.1016 | 0.0059 | 109 | 10.1016 | 0.0729 |
| 10.1016 | 0.0060 | 110 | 10.0938 | 0.0729 |
| 10.0781 | 0.0060 | 111 | 10.0938 | 0.0728 |
| 10.0938 | 0.0061 | 112 | 10.0859 | 0.0727 |
| 10.1172 | 0.0062 | 113 | 10.0859 | 0.0725 |
| 10.1016 | 0.0062 | 114 | 10.0781 | 0.0725 |
| 10.0938 | 0.0063 | 115 | 10.0781 | 0.0726 |
| 10.1016 | 0.0063 | 116 | 10.0703 | 0.0730 |
| 10.0703 | 0.0064 | 117 | 10.0703 | 0.0733 |
| 10.0938 | 0.0064 | 118 | 10.0625 | 0.0738 |
| 10.0859 | 0.0065 | 119 | 10.0625 | 0.0742 |
| 10.0781 | 0.0065 | 120 | 10.0625 | 0.0744 |
| 10.0625 | 0.0066 | 121 | 10.0547 | 0.0745 |
| 10.0547 | 0.0066 | 122 | 10.0547 | 0.0746 |
| 10.0781 | 0.0067 | 123 | 10.0469 | 0.0746 |
| 10.0625 | 0.0068 | 124 | 10.0469 | 0.0745 |
| 10.0781 | 0.0068 | 125 | 10.0391 | 0.0745 |
| 10.0781 | 0.0069 | 126 | 10.0391 | 0.0747 |
| 10.0703 | 0.0069 | 127 | 10.0391 | 0.0752 |
| 10.0547 | 0.0070 | 128 | 10.0312 | 0.0758 |
| 10.0469 | 0.0070 | 129 | 10.0312 | 0.0762 |
| 10.0391 | 0.0071 | 130 | 10.0234 | 0.0765 |
| 10.0391 | 0.0071 | 131 | 10.0234 | 0.0765 |
| 10.0469 | 0.0072 | 132 | 10.0156 | 0.0764 |
| 10.0469 | 0.0072 | 133 | 10.0156 | 0.0761 |
| 10.0234 | 0.0073 | 134 | 10.0156 | 0.0759 |
| 10.0312 | 0.0074 | 135 | 10.0078 | 0.0757 |
| 10.0312 | 0.0074 | 136 | 10.0078 | 0.0757 |
| 10.0078 | 0.0075 | 137 | 10.0 | 0.0759 |
| 10.0 | 0.0075 | 138 | 10.0 | 0.0763 |
| 10.0078 | 0.0076 | 139 | 10.0 | 0.0768 |
| 10.0234 | 0.0076 | 140 | 9.9922 | 0.0774 |
| 9.9922 | 0.0077 | 141 | 9.9922 | 0.0779 |
| 10.0234 | 0.0077 | 142 | 9.9844 | 0.0782 |
| 9.9766 | 0.0078 | 143 | 9.9844 | 0.0783 |
| 10.0156 | 0.0078 | 144 | 9.9844 | 0.0782 |
| 9.9844 | 0.0079 | 145 | 9.9766 | 0.0780 |
| 9.9922 | 0.0080 | 146 | 9.9766 | 0.0778 |
| 9.9844 | 0.0080 | 147 | 9.9688 | 0.0776 |
| 10.0 | 0.0081 | 148 | 9.9688 | 0.0775 |
| 9.9766 | 0.0081 | 149 | 9.9688 | 0.0776 |
| 9.9688 | 0.0082 | 150 | 9.9609 | 0.0778 |
| 9.9844 | 0.0082 | 151 | 9.9609 | 0.0782 |
| 9.9766 | 0.0083 | 152 | 9.9531 | 0.0785 |
| 9.9766 | 0.0083 | 153 | 9.9531 | 0.0787 |
| 9.9922 | 0.0084 | 154 | 9.9453 | 0.0787 |
| 9.9688 | 0.0084 | 155 | 9.9453 | 0.0787 |
| 9.9141 | 0.0085 | 156 | 9.9453 | 0.0785 |
| 9.9453 | 0.0086 | 157 | 9.9375 | 0.0783 |
| 9.9375 | 0.0086 | 158 | 9.9375 | 0.0782 |
| 9.9453 | 0.0087 | 159 | 9.9375 | 0.0782 |
| 9.9531 | 0.0087 | 160 | 9.9297 | 0.0784 |
| 9.9297 | 0.0088 | 161 | 9.9297 | 0.0788 |
| 9.9375 | 0.0088 | 162 | 9.9219 | 0.0793 |
| 9.9219 | 0.0089 | 163 | 9.9219 | 0.0797 |
| 9.9297 | 0.0089 | 164 | 9.9219 | 0.0799 |
| 9.9219 | 0.0090 | 165 | 9.9141 | 0.0802 |
| 9.9141 | 0.0090 | 166 | 9.9141 | 0.0801 |
| 9.9141 | 0.0091 | 167 | 9.9062 | 0.0799 |
| 9.9219 | 0.0092 | 168 | 9.9062 | 0.0797 |
| 9.9062 | 0.0092 | 169 | 9.9062 | 0.0795 |
| 9.9062 | 0.0093 | 170 | 9.8984 | 0.0795 |
| 9.9062 | 0.0093 | 171 | 9.8984 | 0.0797 |
| 9.9297 | 0.0094 | 172 | 9.8906 | 0.0800 |
| 9.8984 | 0.0094 | 173 | 9.8906 | 0.0804 |
| 9.875 | 0.0095 | 174 | 9.8906 | 0.0808 |
| 9.8984 | 0.0095 | 175 | 9.8828 | 0.0810 |
| 9.8828 | 0.0096 | 176 | 9.8828 | 0.0811 |
| 9.8828 | 0.0096 | 177 | 9.8828 | 0.0811 |
| 9.875 | 0.0097 | 178 | 9.875 | 0.0808 |
| 9.8828 | 0.0098 | 179 | 9.875 | 0.0805 |
| 9.8906 | 0.0098 | 180 | 9.8672 | 0.0803 |
| 9.8594 | 0.0099 | 181 | 9.8672 | 0.0803 |
| 9.8828 | 0.0099 | 182 | 9.8672 | 0.0804 |
| 9.8906 | 0.0100 | 183 | 9.8594 | 0.0807 |
| 9.8438 | 0.0100 | 184 | 9.8594 | 0.0809 |
| 9.8672 | 0.0101 | 185 | 9.8516 | 0.0810 |
| 9.8828 | 0.0101 | 186 | 9.8516 | 0.0811 |
| 9.8828 | 0.0102 | 187 | 9.8516 | 0.0811 |
| 9.8594 | 0.0102 | 188 | 9.8438 | 0.0811 |
| 9.8672 | 0.0103 | 189 | 9.8438 | 0.0811 |
| 9.8516 | 0.0104 | 190 | 9.8438 | 0.0812 |
| 9.8281 | 0.0104 | 191 | 9.8359 | 0.0813 |
| 9.8359 | 0.0105 | 192 | 9.8359 | 0.0816 |
| 9.8359 | 0.0105 | 193 | 9.8281 | 0.0818 |
| 9.8516 | 0.0106 | 194 | 9.8281 | 0.0819 |
| 9.8125 | 0.0106 | 195 | 9.8281 | 0.0817 |
| 9.8047 | 0.0107 | 196 | 9.8203 | 0.0815 |
| 9.8203 | 0.0107 | 197 | 9.8203 | 0.0814 |
| 9.8438 | 0.0108 | 198 | 9.8203 | 0.0814 |
| 9.8281 | 0.0108 | 199 | 9.8125 | 0.0815 |
| 9.8516 | 0.0109 | 200 | 9.8125 | 0.0819 |
| 9.8125 | 0.0110 | 201 | 9.8047 | 0.0823 |
| 9.7969 | 0.0110 | 202 | 9.8047 | 0.0826 |
| 9.8359 | 0.0111 | 203 | 9.8047 | 0.0827 |
| 9.8359 | 0.0111 | 204 | 9.7969 | 0.0828 |
| 9.8281 | 0.0112 | 205 | 9.7969 | 0.0826 |
| 9.8359 | 0.0112 | 206 | 9.7969 | 0.0824 |
| 9.8125 | 0.0113 | 207 | 9.7891 | 0.0823 |
| 9.8281 | 0.0113 | 208 | 9.7891 | 0.0824 |
| 9.8203 | 0.0114 | 209 | 9.7812 | 0.0826 |
| 9.7891 | 0.0114 | 210 | 9.7812 | 0.0826 |
| 9.7734 | 0.0115 | 211 | 9.7812 | 0.0826 |
| 9.7734 | 0.0116 | 212 | 9.7734 | 0.0830 |
| 9.7969 | 0.0116 | 213 | 9.7734 | 0.0835 |
| 9.7969 | 0.0117 | 214 | 9.7656 | 0.0840 |
| 9.7656 | 0.0117 | 215 | 9.7656 | 0.0844 |
| 9.7891 | 0.0118 | 216 | 9.7656 | 0.0844 |
| 9.7812 | 0.0118 | 217 | 9.7578 | 0.0845 |
| 9.7812 | 0.0119 | 218 | 9.7578 | 0.0844 |
| 9.7891 | 0.0119 | 219 | 9.7578 | 0.0844 |
| 9.7734 | 0.0120 | 220 | 9.75 | 0.0844 |
| 9.75 | 0.0120 | 221 | 9.75 | 0.0844 |
| 9.7578 | 0.0121 | 222 | 9.7422 | 0.0843 |
| 9.7422 | 0.0122 | 223 | 9.7422 | 0.0842 |
| 9.7578 | 0.0122 | 224 | 9.7422 | 0.0843 |
| 9.7344 | 0.0123 | 225 | 9.7344 | 0.0845 |
| 9.7578 | 0.0123 | 226 | 9.7344 | 0.0848 |
| 9.7734 | 0.0124 | 227 | 9.7344 | 0.0851 |
| 9.7266 | 0.0124 | 228 | 9.7266 | 0.0851 |
| 9.7344 | 0.0125 | 229 | 9.7266 | 0.0849 |
| 9.7344 | 0.0125 | 230 | 9.7266 | 0.0849 |
| 9.6875 | 0.0126 | 231 | 9.7188 | 0.0850 |
| 9.75 | 0.0126 | 232 | 9.7188 | 0.0854 |
| 9.7188 | 0.0127 | 233 | 9.7109 | 0.0857 |
| 9.7109 | 0.0128 | 234 | 9.7109 | 0.0860 |
| 9.7031 | 0.0128 | 235 | 9.7109 | 0.0861 |
| 9.7422 | 0.0129 | 236 | 9.7031 | 0.0861 |
| 9.7266 | 0.0129 | 237 | 9.7031 | 0.0861 |
| 9.7109 | 0.0130 | 238 | 9.7031 | 0.0858 |
| 9.7422 | 0.0130 | 239 | 9.6953 | 0.0856 |
| 9.6875 | 0.0131 | 240 | 9.6953 | 0.0854 |
| 9.7109 | 0.0131 | 241 | 9.6953 | 0.0853 |
| 9.6953 | 0.0132 | 242 | 9.6875 | 0.0853 |
| 9.7109 | 0.0132 | 243 | 9.6875 | 0.0856 |
| 9.6719 | 0.0133 | 244 | 9.6797 | 0.0859 |
| 9.7109 | 0.0134 | 245 | 9.6797 | 0.0863 |
| 9.6719 | 0.0134 | 246 | 9.6797 | 0.0866 |
| 9.7109 | 0.0135 | 247 | 9.6719 | 0.0867 |
| 9.7031 | 0.0135 | 248 | 9.6719 | 0.0866 |
| 9.6641 | 0.0136 | 249 | 9.6719 | 0.0866 |
| 9.6953 | 0.0136 | 250 | 9.6641 | 0.0866 |
| 9.6641 | 0.0137 | 251 | 9.6641 | 0.0866 |
| 9.6719 | 0.0137 | 252 | 9.6641 | 0.0868 |
| 9.6719 | 0.0138 | 253 | 9.6562 | 0.0869 |
| 9.6797 | 0.0138 | 254 | 9.6562 | 0.0870 |
| 9.6797 | 0.0139 | 255 | 9.6484 | 0.0870 |
| 9.6641 | 0.0139 | 256 | 9.6484 | 0.0870 |
| 9.6562 | 0.0140 | 257 | 9.6484 | 0.0869 |
| 9.6562 | 0.0141 | 258 | 9.6406 | 0.0867 |
| 9.6562 | 0.0141 | 259 | 9.6406 | 0.0865 |
| 9.6641 | 0.0142 | 260 | 9.6406 | 0.0866 |
| 9.6406 | 0.0142 | 261 | 9.6328 | 0.0868 |
| 9.6484 | 0.0143 | 262 | 9.6328 | 0.0871 |
| 9.6484 | 0.0143 | 263 | 9.6328 | 0.0873 |
| 9.6328 | 0.0144 | 264 | 9.625 | 0.0874 |
| 9.625 | 0.0144 | 265 | 9.625 | 0.0875 |
| 9.6328 | 0.0145 | 266 | 9.6172 | 0.0877 |
| 9.6641 | 0.0145 | 267 | 9.6172 | 0.0877 |
| 9.6484 | 0.0146 | 268 | 9.6172 | 0.0877 |
| 9.6328 | 0.0147 | 269 | 9.6094 | 0.0877 |
| 9.625 | 0.0147 | 270 | 9.6094 | 0.0875 |
| 9.625 | 0.0148 | 271 | 9.6094 | 0.0875 |
| 9.6094 | 0.0148 | 272 | 9.6016 | 0.0875 |
| 9.6172 | 0.0149 | 273 | 9.6016 | 0.0877 |
| 9.625 | 0.0149 | 274 | 9.6016 | 0.0878 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.1.0a0+32f93b1
- Datasets 2.20.0
- Tokenizers 0.19.1
|
murtuzaakhtari/results | murtuzaakhtari | 2024-07-01T13:57:19Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:57:19Z | Entry not found |
DavidLanz/Llama3_tw_8B_btc_qlora | DavidLanz | 2024-07-01T13:58:48Z | 0 | 2 | peft | [
"peft",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"text-generation",
"en",
"base_model:DavidLanz/Llama3-tw-8B-Instruct",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-07-01T13:57:31Z | ---
language:
- en
license: apache-2.0
library_name: peft
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
base_model: DavidLanz/Llama3-tw-8B-Instruct
model_name: Llama 3 8B Instruct
inference: false
model_creator: Meta Llama 3
model_type: llama
pipeline_tag: text-generation
quantized_by: QLoRA
---
# Model Card for Model ID
This PEFT weight is for predicting BTC price.
Disclaimer: This model is for a time series problem on LLM performance, and it's not for investment advice; any prediction results are not a basis for investment reference.
## Model Details
Training data source: BTC/USD provided by [Binance](https://www.binance.com/).
### Model Description
This repo contains QLoRA format model files for [Meta's Llama 3 8B tw Instruct](https://huggingface.co/DavidLanz/Llama3-tw-8B-Instruct).
## Uses
```python
import torch
from peft import LoraConfig, PeftModel
from transformers import (
AutoModelForCausalLM,
AutoTokenizer,
BitsAndBytesConfig,
HfArgumentParser,
TrainingArguments,
TextStreamer,
pipeline,
logging,
)
device_map = {"": 0}
use_4bit = True
bnb_4bit_compute_dtype = "float16"
bnb_4bit_quant_type = "nf4"
use_nested_quant = False
compute_dtype = getattr(torch, bnb_4bit_compute_dtype)
bnb_config = BitsAndBytesConfig(
load_in_4bit=use_4bit,
bnb_4bit_quant_type=bnb_4bit_quant_type,
bnb_4bit_compute_dtype=compute_dtype,
bnb_4bit_use_double_quant=use_nested_quant,
)
based_model_path = "DavidLanz/Llama3-tw-8B-Instruct"
adapter_path = "DavidLanz/Llama3_tw_8B_btc_qlora"
base_model = AutoModelForCausalLM.from_pretrained(
based_model_path,
low_cpu_mem_usage=True,
return_dict=True,
quantization_config=bnb_config,
torch_dtype=torch.float16,
device_map=device_map,
)
model = PeftModel.from_pretrained(base_model, adapter_path)
tokenizer = AutoTokenizer.from_pretrained(based_model_path, trust_remote_code=True)
import torch
from transformers import pipeline, TextStreamer
text_gen_pipeline = pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
tokenizer=tokenizer,
)
messages = [
{
"role": "system",
"content": "δ½ ζ―δΈδ½ε°ζ₯ηBTCθζ¬θ²¨εΉ£εζεΈ«",
},
{"role": "user", "content": "ζ¨ζ₯ιη€εΉηΊ64437.18οΌζι«εΉηΊ64960.37οΌζδ½εΉηΊ62953.90οΌζΆη€εΉηΊ64808.35οΌδΊ€ζιηΊ808273.27γθ«ι ζΈ¬δ»ζ₯BTCηζΆη€εΉ?"},
]
prompt = text_gen_pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
text_gen_pipeline.tokenizer.eos_token_id,
text_gen_pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = text_gen_pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Framework versions
- PEFT 0.11.1 |
CennetOguz/cooking_blip2_2 | CennetOguz | 2024-07-01T18:13:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T13:58:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
itay-nakash/model_387dff9370_sweep_skilled-waterfall-1163 | itay-nakash | 2024-07-01T13:58:36Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T13:58:36Z | Entry not found |
ikedachin/codeparrot-small | ikedachin | 2024-07-01T14:00:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T13:59:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
pbisht/budtenderai | pbisht | 2024-07-01T14:25:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"dataset:pbisht/train_ha.csv",
"base_model:UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-01T14:00:55Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: UCLA-AGI/Llama-3-Instruct-8B-SPPO-Iter3
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- pbisht/train_ha.csv
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
aaalby/Isa | aaalby | 2024-07-01T14:01:43Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-01T14:01:02Z | ---
license: openrail
---
|
chjoo7/kicon_mixtral87_qlora_v3 | chjoo7 | 2024-07-01T14:03:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T14:01:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
summertime0/nashk9 | summertime0 | 2024-07-01T16:00:55Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:01:19Z | Entry not found |
fiveflow/orpo-gemma | fiveflow | 2024-07-01T14:01:39Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:01:39Z | Entry not found |
ClementineBleuze/roberta_prefix_cont_ll_SEP | ClementineBleuze | 2024-07-02T07:12:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T14:02:09Z | ---
license: mit
base_model: FacebookAI/roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_prefix_cont_ll_SEP
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_prefix_cont_ll_SEP
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0923
- F1 Weighted: 0.8912
- F1 Samples: 0.8996
- F1 Macro: 0.7665
- F1 Micro: 0.8944
- Accuracy: 0.8708
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 Macro | F1 Micro | F1 Samples | F1 Weighted | Validation Loss |
|:-------------:|:------:|:----:|:--------:|:--------:|:--------:|:----------:|:-----------:|:---------------:|
| 0.2635 | 0.3381 | 500 | 0.7165 | 0.3985 | 0.7662 | 0.7362 | 0.7323 | 0.1760 |
| 0.164 | 0.6761 | 1000 | 0.7855 | 0.6106 | 0.8291 | 0.8080 | 0.8107 | 0.1356 |
| 0.1401 | 1.0142 | 1500 | 0.8024 | 0.6610 | 0.8398 | 0.8270 | 0.8268 | 0.1214 |
| 0.1225 | 1.3523 | 2000 | 0.7916 | 0.6825 | 0.8334 | 0.8186 | 0.8256 | 0.1242 |
| 0.1116 | 1.6903 | 2500 | 0.8227 | 0.7166 | 0.8575 | 0.8541 | 0.8531 | 0.1112 |
| 0.1058 | 2.0284 | 3000 | 0.8180 | 0.7133 | 0.8528 | 0.8501 | 0.8489 | 0.1147 |
| 0.0828 | 2.3665 | 3500 | 0.8315 | 0.7210 | 0.8650 | 0.8601 | 0.8594 | 0.1070 |
| 0.0857 | 2.7045 | 4000 | 0.8403 | 0.7118 | 0.8683 | 0.8672 | 0.8611 | 0.1052 |
| 0.0802 | 3.0426 | 4500 | 0.8566 | 0.7411 | 0.8849 | 0.8851 | 0.8785 | 0.0954 |
| 0.0636 | 3.3807 | 5000 | 0.0955 | 0.8775 | 0.8868 | 0.7236 | 0.8850 | 0.8613 |
| 0.0629 | 3.7187 | 5500 | 0.0982 | 0.8830 | 0.8911 | 0.7424 | 0.8881 | 0.8586 |
| 0.0606 | 4.0568 | 6000 | 0.0990 | 0.8805 | 0.8894 | 0.7428 | 0.8873 | 0.8620 |
| 0.0466 | 4.3949 | 6500 | 0.0923 | 0.8912 | 0.8996 | 0.7665 | 0.8944 | 0.8708 |
| 0.0465 | 4.7329 | 7000 | 0.0966 | 0.8876 | 0.8969 | 0.7602 | 0.8914 | 0.8694 |
| 0.0459 | 5.0710 | 7500 | 0.0973 | 0.8896 | 0.8965 | 0.7535 | 0.8951 | 0.8701 |
| 0.0374 | 5.4091 | 8000 | 0.0992 | 0.8868 | 0.8923 | 0.7876 | 0.8877 | 0.8647 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1
|
srinivasan-sridhar28/Tiny-Storyteller | srinivasan-sridhar28 | 2024-07-01T14:02:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:02:15Z | Entry not found |
fiveflow/orpo_gemma | fiveflow | 2024-07-01T14:02:30Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:02:30Z | Entry not found |
jerryyun/kicon_mixtral87_qlora_v3 | jerryyun | 2024-07-01T14:04:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T14:03:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
apersonnaz/crystalDetect_bin_uv_512_20240701-160327 | apersonnaz | 2024-07-01T15:32:46Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-01T14:03:30Z | Entry not found |
apersonnaz/crystalDetect_bin_vis_512_20240701-160327 | apersonnaz | 2024-07-01T15:52:39Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-01T14:03:30Z | Entry not found |
aatreyajha/self_trained_distilbert | aatreyajha | 2024-07-01T14:04:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:04:15Z | Entry not found |
GraydientPlatformAPI/dreamweaver25 | GraydientPlatformAPI | 2024-07-01T15:00:40Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-01T14:06:00Z | Entry not found |
Chairles-alex/autotrain-mistral-small | Chairles-alex | 2024-07-01T14:07:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-01T14:07:00Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: mistralai/Mistral-7B-Instruct-v0.3
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Rajesh939/q-FrozenLake-v1-4x4-noSlippery | Rajesh939 | 2024-07-01T14:07:26Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-01T14:07:22Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="Rajesh939/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
GraydientPlatformAPI/satpony-xl | GraydientPlatformAPI | 2024-07-01T14:31:22Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-01T14:08:58Z | Entry not found |
Rajesh939/MaximusTaxi | Rajesh939 | 2024-07-01T14:09:22Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-01T14:09:20Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: MaximusTaxi
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="Rajesh939/MaximusTaxi", 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"])
```
|
chjoo7/kicon_mixtral87_qlora_merged_v3 | chjoo7 | 2024-07-01T14:15:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-07-01T14:10:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
GraydientPlatformAPI/pixel-ahusaky | GraydientPlatformAPI | 2024-07-01T14:31:36Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-01T14:10:13Z | Entry not found |
Zoya/igor_crafter_llm | Zoya | 2024-07-01T14:12:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-07-01T14:11:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
dhruvvaidh/cover-letter-gen-llama2 | dhruvvaidh | 2024-07-01T14:13:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T14:12:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Franzin/bigbird-roberta-base-goemotions-ekman-multilabel | Franzin | 2024-07-01T14:13:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"big_bird",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-01T14:13:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
YulinWangThu/zephyr-7b-dpo-full | YulinWangThu | 2024-07-01T14:14:11Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:14:11Z | Entry not found |
fiveflow/npo | fiveflow | 2024-07-01T14:21:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T14:16:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
heziiiii/hydit22 | heziiiii | 2024-07-02T12:59:22Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:16:49Z | Entry not found |
herronej/v1-finetuned | herronej | 2024-07-01T14:39:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T14:21:10Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
itay-nakash/model_387dff9370_sweep_twilight-rain-1164 | itay-nakash | 2024-07-01T14:27:25Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:27:25Z | Entry not found |
AliElshabory/whisper-small-hi | AliElshabory | 2024-07-01T14:27:38Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:27:37Z | Entry not found |
Devops-hestabit/mixtral-instruct-trt-quant | Devops-hestabit | 2024-07-01T14:45:29Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T14:27:46Z | ---
license: apache-2.0
---
|
habulaj/4351935627 | habulaj | 2024-07-01T14:28:33Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:28:28Z | Entry not found |
aprilcui11/llama-3-8b-chat-doctor | aprilcui11 | 2024-07-01T17:10:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T14:30:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
habulaj/1666785573 | habulaj | 2024-07-01T14:30:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:30:09Z | Entry not found |
zhhp1314520/gemma-2-9b | zhhp1314520 | 2024-07-01T14:31:04Z | 0 | 0 | null | [
"license:gemma",
"region:us"
] | null | 2024-07-01T14:31:04Z | ---
license: gemma
---
|
mayarmostafa/videomae-base-finetuned-bleeding-exp_0 | mayarmostafa | 2024-07-02T16:52:36Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2024-07-01T14:32:05Z | ---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-bleeding-exp_0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# videomae-base-finetuned-bleeding-exp_0
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4958
- Accuracy: 0.5
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.04 | 2 | 0.6967 | 0.5 |
| No log | 1.04 | 4 | 0.6799 | 0.75 |
| No log | 2.04 | 6 | 0.6721 | 0.75 |
| No log | 3.04 | 8 | 0.6742 | 0.75 |
| 0.6424 | 4.04 | 10 | 0.6927 | 0.25 |
| 0.6424 | 5.04 | 12 | 0.7295 | 0.5 |
| 0.6424 | 6.04 | 14 | 0.8047 | 0.5 |
| 0.6424 | 7.04 | 16 | 0.8589 | 0.5 |
| 0.6424 | 8.04 | 18 | 0.8842 | 0.5 |
| 0.6123 | 9.04 | 20 | 0.9349 | 0.5 |
| 0.6123 | 10.04 | 22 | 0.9543 | 0.5 |
| 0.6123 | 11.04 | 24 | 0.9924 | 0.5 |
| 0.6123 | 12.04 | 26 | 1.0729 | 0.5 |
| 0.6123 | 13.04 | 28 | 1.2268 | 0.5 |
| 0.3641 | 14.04 | 30 | 1.3759 | 0.5 |
| 0.3641 | 15.04 | 32 | 1.4344 | 0.5 |
| 0.3641 | 16.04 | 34 | 1.4563 | 0.5 |
| 0.3641 | 17.04 | 36 | 1.4365 | 0.5 |
| 0.3641 | 18.04 | 38 | 1.4343 | 0.5 |
| 0.4378 | 19.04 | 40 | 1.4375 | 0.5 |
| 0.4378 | 20.04 | 42 | 1.4530 | 0.5 |
| 0.4378 | 21.04 | 44 | 1.4732 | 0.5 |
| 0.4378 | 22.04 | 46 | 1.4877 | 0.5 |
| 0.4378 | 23.04 | 48 | 1.4919 | 0.5 |
| 0.222 | 24.04 | 50 | 1.4958 | 0.5 |
### Framework versions
- Transformers 4.40.2
- Pytorch 1.12.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
sameeahameed/llama-3-model_lora_model_LMD_updates | sameeahameed | 2024-07-01T14:33:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T14:33:14Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** sameeahameed
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sameeahameed/llama-3-model_lora_model_LMD_updated | sameeahameed | 2024-07-01T14:33:26Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-01T14:33:24Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PrabhakarVenkat/Stock-Analysis_with_CrewAI | PrabhakarVenkat | 2024-07-01T14:38:42Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:33:34Z | Entry not found |
ohjimin/hscode | ohjimin | 2024-07-01T14:41:28Z | 0 | 0 | null | [
"safetensors",
"license:unlicense",
"region:us"
] | null | 2024-07-01T14:35:56Z | ---
license: unlicense
---
|
GraydientPlatformAPI/wai-simpleuse | GraydientPlatformAPI | 2024-07-01T15:00:53Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-07-01T14:40:22Z | Entry not found |
DeusImperator/sunfall-midnight-miqu-v0.2-v1.5-70B_exl2_2.4bpw_rpcal_mk2 | DeusImperator | 2024-07-01T15:29:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"exl2",
"region:us"
] | text-generation | 2024-07-01T14:41:59Z | ---
library_name: transformers
tags:
- not-for-all-audiences
---
# sunfall-midnight-miqu-v0.2-v1.5-70B - EXL2 2.4bpw rpcal_mk2
This is a 2.4bpw EXL2 quant of [crestf411/sunfall-midnight-miqu-v0.2-v1.5-70B](https://huggingface.co/crestf411/sunfall-midnight-miqu-v0.2-v1.5-70B)
This quant was made using exllamav2-0.1.6 with [Bluemoon-light dataset](https://huggingface.co/datasets/ParasiticRogue/Bluemoon-Light) for RP.
This quant fits 25k context on 24GB VRAM on Windows in my local testing (with exl2 Q4 cache), you might be able to get more depending on other things taking VRAM.
I tested this quant shortly in some random RPs (including ones over 8k and 20k context) and it seems to work fine.
## Prompt Templates
I used Vicuna version of calibration dataset, so probably Vicuna will be best here.
### Original readme below
---
Sunfall (2024-06-07) dataset trained directly on top of https://huggingface.co/sophosympatheia/Midnight-Miqu-70B-v1.5
Beware, depraved. Not suitable for any audience.
Experimental. Please give feedback. Begone if you demand perfection.
This is still an early stage experiment.
*Recommend a decently high temperature. Start with temp 1.7, smoothing factor 0.3.*
To use lore book tags, make sure you use **Status: Blue (constant)** and write e.g.
```
Follow the Diamond Law at all costs.
Tags: humor, dark, complex storytelling, intricate characters, immersive.
```
This model has been trained on context that mimics that of Silly Tavern's Mistral preset, with the following settings:
**System Prompt:**
```
You are an expert actor that can fully immerse yourself into any role given. You do not break character for any reason. Currently your role is {{char}}, which is described in detail below. As {{char}}, continue the exchange with {{user}}.
```
Below method still works, but the lore book approach above is more convenient:
**System Same as User Enabled** (This is the default)
**Author's Note** (In-chat @ Depth 4)
```
Follow The Diamond Law at all costs.
```
Below method still works, but unless you want to write tags for a specific character card only, the lore book approach above is more convenient:
**Scenario Information** (open a character card and press "Advanced Definitions") may also contain tags at the end to guide the model further. E.g.:
```
Two friends having fun. Set in 1947.
Tags: dark, exploration, friendship, self-discovery, historical fiction
```
The card has also been trained on content which includes a narrator card, which was used when the content did not mainly revolve around two characters. Future versions will expand on this idea, so forgive the vagueness at this time.
(The Diamond Law is this: https://files.catbox.moe/d15m3g.txt -- So far results are unclear, but the training was done with this phrase included, and the training data adheres to the law.)
The model has also been trained to do storywriting, both interactively with the user and on its own. The system message ends up looking something like this:
```
You are an expert storyteller, who can roleplay or write compelling stories. Follow the Diamond Law. Below is a scenario with character descriptions and content tags. Write a story together with the user based on this scenario.
Scenario: The story is about James, blabla.
James is an overweight 63 year old blabla.
Lucy: James's 62 year old wife.
Tags: tag1, tag2, tag3, ...
```
If you remove the "together with the user" part, the model will be more inclined to write on its own.
|
yuchuantian/IPG | yuchuantian | 2024-07-01T14:51:43Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T14:42:00Z | ---
license: apache-2.0
---
|
niccosala/Llama-3-8B-sft-lora-ultrachat | niccosala | 2024-07-01T14:42:33Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-01T14:42:33Z | Entry not found |
Moriacrafter/Qwen1.5-1.8B-4bit_DepressionDetection | Moriacrafter | 2024-07-01T14:44:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-01T14:43:23Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## 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. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.