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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-09-05 06:27:37
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| likes
int64 0
11.7k
| library_name
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ckandemir/whisper-tiny
|
ckandemir
| 2024-02-16T10:08:28Z | 62 | 0 |
transformers
|
[
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:PolyAI/minds14",
"base_model:openai/whisper-tiny",
"base_model:finetune:openai/whisper-tiny",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2023-07-31T00:46:15Z |
---
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- PolyAI/minds14
metrics:
- wer
model-index:
- name: Whisper Tiny-Handy-Pretty - ckandemir
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: PolyAI/minds14
type: PolyAI/minds14
config: en-US
split: train[450:]
args: en-US
metrics:
- name: Wer
type: wer
value: 0.3116883116883117
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Tiny-Handy-Pretty - ckandemir
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the PolyAI/minds14 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5197
- Wer Ortho: 31.6471
- Wer: 0.3117
## Model description
More information needed
## Intended uses & 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: 12
- eval_batch_size: 12
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 100
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|
| 0.8427 | 1.32 | 50 | 0.5401 | 35.9655 | 0.3566 |
| 0.1982 | 2.63 | 100 | 0.5179 | 35.5336 | 0.3501 |
| 0.0531 | 3.95 | 150 | 0.5197 | 31.6471 | 0.3117 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.1
- Tokenizers 0.13.3
|
Hamzaharman/imageclassification
|
Hamzaharman
| 2024-02-16T10:07:37Z | 35 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-02-16T00:34:25Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: imageclassification
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.59375
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# imageclassification
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1467
- Accuracy: 0.5938
## Model description
More information needed
## Intended uses & 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
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 40 | 1.8113 | 0.35 |
| No log | 2.0 | 80 | 1.5533 | 0.3937 |
| No log | 3.0 | 120 | 1.4193 | 0.4688 |
| No log | 4.0 | 160 | 1.3237 | 0.5687 |
| No log | 5.0 | 200 | 1.2989 | 0.4938 |
| No log | 6.0 | 240 | 1.2901 | 0.5 |
| No log | 7.0 | 280 | 1.2380 | 0.5625 |
| No log | 8.0 | 320 | 1.1773 | 0.6125 |
| No log | 9.0 | 360 | 1.2149 | 0.5625 |
| No log | 10.0 | 400 | 1.2280 | 0.5312 |
| No log | 11.0 | 440 | 1.2326 | 0.5625 |
| No log | 12.0 | 480 | 1.1488 | 0.5875 |
| 1.0601 | 13.0 | 520 | 1.1597 | 0.6062 |
| 1.0601 | 14.0 | 560 | 1.1953 | 0.5563 |
| 1.0601 | 15.0 | 600 | 1.2011 | 0.55 |
| 1.0601 | 16.0 | 640 | 1.2294 | 0.55 |
| 1.0601 | 17.0 | 680 | 1.1972 | 0.5687 |
| 1.0601 | 18.0 | 720 | 1.3043 | 0.525 |
| 1.0601 | 19.0 | 760 | 1.2796 | 0.525 |
| 1.0601 | 20.0 | 800 | 1.1781 | 0.5813 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
hs4jk24erfc/test_fine_tuned_model
|
hs4jk24erfc
| 2024-02-16T10:04:29Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T10:03:14Z |
---
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 🤗 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]
|
maramzarkaoui/facebook1
|
maramzarkaoui
| 2024-02-16T10:04:18Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T10:04:16Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
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)
```
|
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_8_0.0002
|
ferrazzipietro
| 2024-02-16T10:03:41Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T10:03:28Z |
---
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 🤗 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]
|
warmestman/whisper-large-v3-mn-1
|
warmestman
| 2024-02-16T10:02:54Z | 7 | 0 |
transformers
|
[
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"mn",
"dataset:mozilla-foundation/common_voice_16_1",
"base_model:openai/whisper-large-v3",
"base_model:finetune:openai/whisper-large-v3",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-15T11:03:21Z |
---
language:
- mn
license: apache-2.0
base_model: openai/whisper-large-v3
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_16_1
metrics:
- wer
model-index:
- name: 'Whisper Small MN - Ankhbayasgalan Davaadorj '
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 16.1
type: mozilla-foundation/common_voice_16_1
config: mn
split: test
args: 'config: mn, split: test+validation'
metrics:
- name: Wer
type: wer
value: 67.84162771514984
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small MN - Ankhbayasgalan Davaadorj
This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5096
- Wer: 67.8416
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0832 | 3.94 | 1000 | 0.3988 | 73.6211 |
| 0.0051 | 7.87 | 2000 | 0.4563 | 66.0654 |
| 0.0004 | 11.81 | 3000 | 0.5096 | 67.8416 |
### Framework versions
- Transformers 4.37.2
- Pytorch 1.12.1+cu116
- Datasets 2.17.0
- Tokenizers 0.15.2
|
anjith672/dillu_high_train
|
anjith672
| 2024-02-16T10:01:58Z | 1 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] |
text-to-image
| 2024-02-16T07:31:37Z |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: dil
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was trained.
|
sadhaklal/bert-base-uncased-finetuned-mrpc-v2
|
sadhaklal
| 2024-02-16T09:59:17Z | 92 | 0 |
transformers
|
[
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"dataset:glue",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-14T10:14:44Z |
---
license: apache-2.0
datasets:
- glue
language:
- en
metrics:
- accuracy
- f1
library_name: transformers
pipeline_tag: text-classification
widget:
- text: The company didn 't detail the costs of the replacement and repairs . [SEP] But company officials expect the costs of the replacement work to run into the millions of dollars .
example_title: not_equivalent
- text: According to the federal Centers for Disease Control and Prevention ( news - web sites ) , there were 19 reported cases of measles in the United States in 2002 . [SEP] The Centers for Disease Control and Prevention said there were 19 reported cases of measles in the United States in 2002 .
example_title: equivalent
---
# bert-base-uncased-finetuned-mrpc-v2
BERT (`"bert-base-uncased"`) finetuned on MRPC (Microsoft Research Paraphrase Corpus).
The model predicts whether two sentences are semantically equivalent. It pertains to section 4 of chapter 3 of the Hugging Face "NLP Course" (https://huggingface.co/learn/nlp-course/chapter3/4).
It was trained using a custom PyTorch loop with Hugging Face Accelerate.
Code: https://github.com/sambitmukherjee/huggingface-notebooks/blob/main/course/en/chapter3/section4.ipynb
Experiment tracking: https://wandb.ai/sadhaklal/bert-base-uncased-finetuned-mrpc-v2
## Usage
```
from transformers import pipeline
classifier = pipeline("text-classification", model="sadhaklal/bert-base-uncased-finetuned-mrpc-v2")
sentence1 = "A tropical storm rapidly developed in the Gulf of Mexico Sunday and was expected to hit somewhere along the Texas or Louisiana coasts by Monday night ."
sentence2 = "A tropical storm rapidly developed in the Gulf of Mexico on Sunday and could have hurricane-force winds when it hits land somewhere along the Louisiana coast Monday night ."
sentence_pair = sentence1 + " [SEP] " + sentence2
print(classifier(sentence_pair))
sentence1 = "The settling companies would also assign their possible claims against the underwriters to the investor plaintiffs , he added ."
sentence2 = "Under the agreement , the settling companies will also assign their potential claims against the underwriters to the investors , he added ."
sentence_pair = sentence1 + " [SEP] " + sentence2
print(classifier(sentence_pair))
```
## Dataset
From the dataset page:
> The Microsoft Research Paraphrase Corpus (Dolan & Brockett, 2005) is a corpus of sentence pairs automatically extracted from online news sources, with human annotations for whether the sentences in the pair are semantically equivalent.
Examples: https://huggingface.co/datasets/glue/viewer/mrpc
## Metrics
Accuracy on the 'validation' split of MRPC: 0.875
F1 on the 'validation' split of MRPC: 0.9128
|
logicker/SkkuDS-DPO-72B-v1
|
logicker
| 2024-02-16T09:51:54Z | 52 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"pretrained, dpo",
"conversational",
"en",
"arxiv:2309.16609",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-15T08:14:26Z |
---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained, dpo
---
# Qwen1.5-72B
## DPO Tuning
- Dataset: Intel/orca_dpo_pairs
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Citation
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
|
shanhy/xlm-roberta-base_lr5e-06_seed42_basic_original_esp-kin-eng_train
|
shanhy
| 2024-02-16T09:51:53Z | 100 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T09:50:50Z |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_lr5e-06_seed42_basic_original_esp-kin-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_lr5e-06_seed42_basic_original_esp-kin-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0293
- Spearman Corr: 0.7251
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.63 | 200 | 0.0280 | 0.6016 |
| 0.062 | 3.27 | 400 | 0.0275 | 0.6391 |
| 0.0304 | 4.9 | 600 | 0.0293 | 0.6641 |
| 0.0245 | 6.53 | 800 | 0.0266 | 0.6888 |
| 0.0217 | 8.16 | 1000 | 0.0304 | 0.6900 |
| 0.0217 | 9.8 | 1200 | 0.0286 | 0.7016 |
| 0.0198 | 11.43 | 1400 | 0.0304 | 0.7080 |
| 0.0181 | 13.06 | 1600 | 0.0277 | 0.7103 |
| 0.0164 | 14.69 | 1800 | 0.0285 | 0.7086 |
| 0.0154 | 16.33 | 2000 | 0.0286 | 0.7233 |
| 0.0154 | 17.96 | 2200 | 0.0259 | 0.7209 |
| 0.0144 | 19.59 | 2400 | 0.0282 | 0.7160 |
| 0.0137 | 21.22 | 2600 | 0.0300 | 0.7168 |
| 0.0129 | 22.86 | 2800 | 0.0300 | 0.7215 |
| 0.0123 | 24.49 | 3000 | 0.0288 | 0.7262 |
| 0.0124 | 26.12 | 3200 | 0.0285 | 0.7256 |
| 0.0124 | 27.76 | 3400 | 0.0291 | 0.7220 |
| 0.0119 | 29.39 | 3600 | 0.0293 | 0.7251 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_4_0.0002
|
ferrazzipietro
| 2024-02-16T09:51:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T09:51:28Z |
---
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 🤗 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]
|
hewonty/longformer-ner-finetuned-pii
|
hewonty
| 2024-02-16T09:51:32Z | 91 | 1 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"longformer",
"token-classification",
"generated_from_trainer",
"base_model:allenai/longformer-base-4096",
"base_model:finetune:allenai/longformer-base-4096",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
token-classification
| 2024-02-15T15:02:46Z |
---
license: apache-2.0
base_model: allenai/longformer-base-4096
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: longformer-ner-finetuned-pii
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. -->
# longformer-ner-finetuned-pii
This model is a fine-tuned version of [allenai/longformer-base-4096](https://huggingface.co/allenai/longformer-base-4096) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0034
- Precision: 0.9772
- Recall: 0.9879
- F1: 0.9825
- Accuracy: 0.9993
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0063 | 1.0 | 1324 | 0.0050 | 0.9613 | 0.9842 | 0.9726 | 0.9990 |
| 0.0037 | 2.0 | 2648 | 0.0038 | 0.9735 | 0.9873 | 0.9803 | 0.9992 |
| 0.002 | 3.0 | 3972 | 0.0034 | 0.9772 | 0.9879 | 0.9825 | 0.9993 |
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.1
|
qkrwnstj/lora_mid_journal_1
|
qkrwnstj
| 2024-02-16T09:50:39Z | 3 | 1 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-16T08:43:31Z |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - qkrwnstj/lora_mid_journal_1
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the qkrwnstj/mid-captioning-dataset dataset. You can find some example images in the following.
|
mindlywork/BandW2Char
|
mindlywork
| 2024-02-16T09:46:24Z | 34 | 1 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:cc",
"region:us"
] |
text-to-image
| 2024-02-16T09:43:27Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: BandW2Char
output:
url: images/l1DJDaP05ESt6MK9MtxN5bJj (1).png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: BandW2Char
license: cc
---
# BandW2Char
<Gallery />
## Model description
BandW2Char
## Trigger words
You should use `BandW2Char` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/dasdsff/BandW2Char/tree/main) them in the Files & versions tab.
|
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.01_2_0.002
|
ferrazzipietro
| 2024-02-16T09:45:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T09:45:19Z |
---
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 🤗 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]
|
eren23/sdxl-zavy-animemix-copax-jibmix-segmoe
|
eren23
| 2024-02-16T09:37:42Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"segmoe",
"merge",
"moe",
"sdxl",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] |
text-to-image
| 2024-02-16T09:13:57Z |
---
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- segmoe
- merge
- moe
- sdxl
---
This model is a segmoe merge of 4 models from civitAI:
https://civitai.com/models/269232/aam-xl-anime-mix?modelVersionId=303526
https://civitai.com/models/194768/jib-mix-realistic-xl?modelVersionId=335740
https://civitai.com/models/118111/copax-timelessxl-sdxl10?modelVersionId=344540
https://civitai.com/models/119229/zavychromaxl?modelVersionId=320428
Merged using the great project at: https://github.com/segmind/segmoe
To do something similar you can either follow the guide in readme or you can follow this blogpost: https://huggingface.co/blog/segmoe
The setting I used:
```
base_model: https://civitai.com/api/download/models/320428
num_experts: 4
moe_layers: all
num_experts_per_tok: 2
type: sdxl
experts:
- source_model: https://civitai.com/api/download/models/320428
positive_prompt: "RAW photo, photorealistic, film grain, candid camera, color graded cinematic, eye catchlights, atmospheric lighting, macro shot, skin pores, imperfections, natural."
negative_prompt: " (deformed iris, deformed pupils, semi-realistic, cgi, 3d, render, sketch, cartoon, drawing, anime), text, cropped, out of frame, worst quality, low quality, jpeg artifacts, ugly, duplicate, morbid, mutilated, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, mutation, deformed, blurry, dehydrated, bad anatomy, bad proportions, extra limbs, cloned face, disfigured, gross proportions, malformed limbs, missing arms, missing legs, extra arms, extra legs, fused fingers, too many fingers, long neck"
- source_model: https://civitai.com/api/download/models/303526?type=Model&format=SafeTensor&size=full&fp=fp16
positive_prompt: "1girl, mecha suit, samurai face mask, menpo, upper body, underboob, portrait, white orange armor, blonde shimmering hair, 8K, RAW, best quality, masterpiece, ultra high res, colorful, (medium wide shot), (dynamic perspective), sharp focus , (depth of field, bokeh:1.3), extremely detailed eyes and face, beautiful detailed eyes,large breasts,black gold, trimmed gear,In a futuristic weapons factory, ((masterpiece, best quality)), niji, from side, upper body, hips, anime style"
negative_prompt: "(low quality, worst quality:1.4), negativeXL_D, cgi, text, signature, watermark, extra limbs"
- source_model: https://civitai.com/api/download/models/335740?type=Model&format=SafeTensor&size=full&fp=fp32
positive_prompt: "cinematic photo Documentery Photo a brown bear in the Alaskan wilderness, discovery magazine, cinematic photorealistic, 8k uhd natural lighting, raw, rich, intricate details, key visual, atmospheric lighting, 35mm photograph, film, bokeh, professional, 4k, highly detailed . 35mm photograph, film, bokeh, professional, 4k, highly detailed"
negative_prompt: "drawing, painting, crayon, sketch, graphite, impressionist, noisy, blurry, soft, deformed, ugly, ((bokeh)) Deviantart, Deviantart, jpeg , (worst quality, low quality, normal quality, lowres, low details, oversaturated, undersaturated, overexposed, underexposed, grayscale, bw, bad photo, bad photography, bad art:1.4), (watermark, signature, text font, username, error, logo, words, letters, digits, autograph, trademark, name:1.2), (blur, blurry, grainy), morbid, ugly, asymmetrical, mutated malformed, mutilated, poorly lit, bad shadow, draft, cropped, out of frame, cut off, censored, jpeg artifacts, out of focus, glitch, duplicate, (airbrushed, cartoon, anime, semi-realistic, cgi, render, blender, digital art, manga, amateur:1.3), (3D ,3D Game, 3D Game Scene, 3D Character:1.1), (bad hands, bad anatomy, bad body, bad face, bad teeth, bad arms, bad legs, deformities:1.3)"
- source_model: https://civitai.com/api/download/models/344540
positive_prompt: "a photo portrait of an aztec demon shaman zombie in the style of apocalypto"
negative_prompt: "(worst quality, low quality, normal quality, lowres, low details, grayscale, bw), painting, drawing, sketch, cartoon, anime, manga, render, CG, 3d, watermark, signature, label, long neck"
```
# Usage
!pip install -U segmoe diffusers transformers
from segmoe import SegMoEPipeline
pipeline = SegMoEPipeline("eren23/sdxl-zavy-animemix-copax-jibmix-segmoe", device="cuda")
prompt = "fantastic land canvas, knight cat standing next to a purple medieval village wall"
negative_prompt = "nsfw, bad quality, worse quality"
img = pipeline(
prompt=prompt,
negative_prompt=negative_prompt,
height=512,
width=512,
num_inference_steps=30,
guidance_scale=7.5,
).images[0]
img.save("image.png")
# Example Images
|
leenag/check-malayalam
|
leenag
| 2024-02-16T09:35:45Z | 60 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-16T08:15:42Z |
---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: check-malayalam
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. -->
# check-malayalam
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1187
- Wer: 54.0682
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7411 | 0.46 | 100 | 0.5833 | 100.1244 |
| 0.2408 | 0.93 | 200 | 0.2089 | 72.5056 |
| 0.1299 | 1.39 | 300 | 0.1441 | 61.8811 |
| 0.1046 | 1.85 | 400 | 0.1250 | 55.8845 |
| 0.0763 | 2.31 | 500 | 0.1187 | 54.0682 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
thrunlab/Mistral_Sparse_refined_web_50p_2024-02-16
|
thrunlab
| 2024-02-16T09:28:06Z | 6 | 0 |
transformers
|
[
"transformers",
"safetensors",
"sparse_mistral",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:22:09Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_50p_2024-02-16
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. -->
# Mistral_Sparse_refined_web_50p_2024-02-16
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1260
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 3
- gradient_accumulation_steps: 3
- total_train_batch_size: 9
- total_eval_batch_size: 3
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.5975 | 0.01 | 25 | 2.6362 |
| 2.3082 | 0.01 | 50 | 2.5659 |
| 2.4024 | 0.02 | 75 | 2.5151 |
| 2.3358 | 0.02 | 100 | 2.4817 |
| 2.2267 | 0.03 | 125 | 2.4660 |
| 2.271 | 0.04 | 150 | 2.4456 |
| 2.1709 | 0.04 | 175 | 2.4413 |
| 2.2549 | 0.05 | 200 | 2.4306 |
| 2.2536 | 0.05 | 225 | 2.4243 |
| 2.2234 | 0.06 | 250 | 2.4212 |
| 2.2516 | 0.07 | 275 | 2.4202 |
| 2.2827 | 0.07 | 300 | 2.4146 |
| 2.1774 | 0.08 | 325 | 2.4156 |
| 2.278 | 0.08 | 350 | 2.4094 |
| 2.204 | 0.09 | 375 | 2.4088 |
| 2.1987 | 0.1 | 400 | 2.4073 |
| 2.1985 | 0.1 | 425 | 2.4041 |
| 2.2198 | 0.11 | 450 | 2.4069 |
| 2.2555 | 0.11 | 475 | 2.4014 |
| 2.1567 | 0.12 | 500 | 2.4017 |
| 2.2918 | 0.13 | 525 | 2.3998 |
| 2.2559 | 0.13 | 550 | 2.3959 |
| 2.2234 | 0.14 | 575 | 2.3978 |
| 2.2001 | 0.14 | 600 | 2.3944 |
| 2.1409 | 0.15 | 625 | 2.3957 |
| 2.2034 | 0.16 | 650 | 2.3981 |
| 2.1863 | 0.16 | 675 | 2.3941 |
| 2.2372 | 0.17 | 700 | 2.3936 |
| 2.2438 | 0.17 | 725 | 2.3953 |
| 2.2172 | 0.18 | 750 | 2.3943 |
| 2.1917 | 0.19 | 775 | 2.3921 |
| 2.1137 | 0.19 | 800 | 2.3912 |
| 2.0766 | 0.07 | 825 | 2.3935 |
| 2.1926 | 0.08 | 850 | 2.3913 |
| 2.2948 | 0.08 | 875 | 2.3915 |
| 2.1349 | 0.08 | 900 | 2.3917 |
| 2.2446 | 0.08 | 925 | 2.3876 |
| 2.253 | 0.09 | 950 | 2.3880 |
| 2.0729 | 0.09 | 975 | 2.3890 |
| 2.1965 | 0.09 | 1000 | 2.3873 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
ferrazzipietro/Mistral-7B-Instruct-v0.2_adapters_en.layer1_4_torch.bfloat16_16_32_0.05_8_0.0002
|
ferrazzipietro
| 2024-02-16T09:27:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T09:27:03Z |
---
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 🤗 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]
|
Azma-AI/azma-open-hermes-2.5-mistral-7B-conversation-agent-v1
|
Azma-AI
| 2024-02-16T09:23:44Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:Azma-AI/azma-final-conversation-dataset",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:54:19Z |
---
library_name: transformers
datasets:
- Azma-AI/azma-final-conversation-dataset
---
# 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 🤗 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]
|
seatond/EXTRACTION_rank64_lr2.2e-05_target7_epochs50step_laplha128_batch1_gradacc4
|
seatond
| 2024-02-16T09:21:20Z | 0 | 0 |
peft
|
[
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"base_model:adapter:TheBloke/Mistral-7B-Instruct-v0.2-GPTQ",
"region:us"
] | null | 2024-02-16T09:02:17Z |
---
library_name: peft
base_model: TheBloke/Mistral-7B-Instruct-v0.2-GPTQ
---
# 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 0.7.1.dev0
|
Menouar/saqr-7b-merged
|
Menouar
| 2024-02-16T09:20:31Z | 233 | 1 |
transformers
|
[
"transformers",
"safetensors",
"falcon",
"text-generation",
"saqr-7b-instrcut",
"Pytorch",
"conversational",
"custom_code",
"en",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:openbmb/UltraFeedback",
"dataset:gsm8k",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T09:10:53Z |
---
library_name: transformers
tags:
- saqr-7b-instrcut
- Pytorch
license: apache-2.0
datasets:
- HuggingFaceH4/ultrachat_200k
- openbmb/UltraFeedback
- gsm8k
language:
- en
pipeline_tag: text-generation
---
# saqr-7b-merged
This model is a merged version of [**saqr-7b-instruct**](https://huggingface.co/Menouar/saqr-7b-instruct) with LoRA Adapters.
<img src="https://huggingface.co/Menouar/saqr-7b-instruct/resolve/main/saqr.jpg" alt="Saqr Logo" width="800" style="margin-left:auto; margin-right:auto; display:block;"/>
|
Reshphil/lab1_finetuning
|
Reshphil
| 2024-02-16T09:16:48Z | 118 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-15T09:05:21Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.88398487672078
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8556
- Bleu: 52.8840
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
SeyedAli/Image-Arousal
|
SeyedAli
| 2024-02-16T09:14:59Z | 179 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2024-02-12T09:10:19Z |
---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- image-classification
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: vit-Arousal
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. -->
# Image-Arousal
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8522
- Accuracy: 0.6294
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.9023 | 0.78 | 100 | 0.8522 | 0.6294 |
| 0.5376 | 1.56 | 200 | 0.8592 | 0.6686 |
| 0.2473 | 2.34 | 300 | 0.9559 | 0.6510 |
| 0.0691 | 3.12 | 400 | 1.1399 | 0.6275 |
| 0.0821 | 3.91 | 500 | 1.2060 | 0.6392 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
muyuanma/marian-finetuned-kde4-en-to-fr
|
muyuanma
| 2024-02-16T09:12:40Z | 118 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-16T07:12:00Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 53.03371127884619
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8558
- Bleu: 53.0337
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
p1atdev/tokenizer_test_1
|
p1atdev
| 2024-02-16T09:11:02Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-15T06:19:02Z |
---
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 🤗 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]
|
Iggnis/Light-Novel-fine-tuned-mistral
|
Iggnis
| 2024-02-16T09:09:05Z | 0 | 1 | null |
[
"region:us"
] | null | 2024-02-16T07:39:23Z |
# Light Novel fine tuned with Mistral
*This model was created for a course in Université Savoie Mont Blanc*
We find tuned this model
```
mistralai/Mixtral-8x7B-Instruct-v0.1
```
And we added to it, light novel text to make it more UwU
```
alpindale/light-novels
```
|
rAIfle/ohno-8x7B-exl2-rpcal
|
rAIfle
| 2024-02-16T08:54:25Z | 0 | 0 | null |
[
"region:us"
] | null | 2024-02-15T13:28:29Z |
same deal as always, 200 rows, 8192 tokens, PIPPA, yadayada.
home-merged, check [rAIfle/ohno-8x7B-fp16](https://huggingface.co/rAIfle/ohno-8x7B-fp16) for source.
no clue what prompt to use, nor what settings. model deemed fit for human consumption, at least, though it is pretty braindamaged.
|
rAIfle/ohno-8x7B-fp16
|
rAIfle
| 2024-02-16T08:53:58Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:merge:Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora",
"base_model:Envoid/Mixtral-Instruct-ITR-8x7B",
"base_model:merge:Envoid/Mixtral-Instruct-ITR-8x7B",
"base_model:NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss",
"base_model:merge:NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss",
"base_model:retrieval-bar/Mixtral-8x7B-v0.1_case-briefs",
"base_model:merge:retrieval-bar/Mixtral-8x7B-v0.1_case-briefs",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-15T11:33:35Z |
---
base_model:
- Envoid/Mixtral-Instruct-ITR-8x7B
- Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
- Envoid/Mixtral-Instruct-ITR-8x7B
- retrieval-bar/Mixtral-8x7B-v0.1_case-briefs
- NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss
- Envoid/Mixtral-Instruct-ITR-8x7B
tags:
- mergekit
- merge
---
# ohno-8x7b
this... will either be my magnum opus... or terrible. no inbetweens!
Post-test verdict: It's mostly braindamaged. Might be my settings or something, idk.
the `./output` mentioned below is my own merge using identical recipe as [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B).
# output_merge2
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) as a base.
### Models Merged
The following models were included in the merge:
* ./output/ + /ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b
* [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) + [Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora](https://huggingface.co/Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora)
* [Envoid/Mixtral-Instruct-ITR-8x7B](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-8x7B) + [retrieval-bar/Mixtral-8x7B-v0.1_case-briefs](https://huggingface.co/retrieval-bar/Mixtral-8x7B-v0.1_case-briefs)
* [NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss](https://huggingface.co/NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: ./output/+/ai/LLM/tmp/pefts/daybreak-peft/mixtral-8x7b
parameters:
density: 0.66
weight: 1.0
- model: Envoid/Mixtral-Instruct-ITR-8x7B+retrieval-bar/Mixtral-8x7B-v0.1_case-briefs
parameters:
density: 0.1
weight: 0.25
- model: Envoid/Mixtral-Instruct-ITR-8x7B+Doctor-Shotgun/limarp-zloss-mixtral-8x7b-qlora
parameters:
density: 0.66
weight: 0.5
- model: NeverSleep/Noromaid-v0.4-Mixtral-Instruct-8x7b-Zloss
parameters:
density: 0.15
weight: 0.3
merge_method: dare_ties
base_model: Envoid/Mixtral-Instruct-ITR-8x7B
dtype: float16
```
|
jungyuko/DAVinCI-Yi-Ko-6B-v1.1
|
jungyuko
| 2024-02-16T08:51:00Z | 58 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:11:16Z |
---
license: cc-by-nc-4.0
---
## DAVinCI-Yi-Ko-6B-v1.1
This model is a fine-tuned version of [beomi/Yi-Ko-6B](https://huggingface.co/beomi/Yi-Ko-6B) on an unknown dataset.
### Model description
More information needed
### Intended uses & limitations
More information needed
### Training and evaluation data
More information needed
### Training procedure
### Training hypuerparameters
The following hyperparameters were used during training:
* learning_rate: 2e-05
* train_batch_size: 4
* eval_batch_size: 8
* seed: 42
* gradient_accumulation_steps: 8
* total_train_batch_size: 32
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr_scheduler_type: linear
* num_epochs: 1.0
* mixed_precision_training: Native AMP
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.0.0
* Tokenizers 0.15.0
|
thrunlab/Mistral_Sparse_refined_web_70p_2024-02-15
|
thrunlab
| 2024-02-16T08:49:13Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"sparse_mistral",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-02-15T08:42:43Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_70p_2024-02-15
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. -->
# Mistral_Sparse_refined_web_70p_2024-02-15
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2065
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.0372 | 0.0 | 25 | 3.1256 |
| 2.6176 | 0.01 | 50 | 2.8951 |
| 2.5321 | 0.01 | 75 | 2.7409 |
| 2.4603 | 0.02 | 100 | 2.6753 |
| 2.4033 | 0.02 | 125 | 2.6424 |
| 2.4821 | 0.02 | 150 | 2.6147 |
| 2.4008 | 0.03 | 175 | 2.5858 |
| 2.3651 | 0.03 | 200 | 2.5688 |
| 2.3873 | 0.04 | 225 | 2.5565 |
| 2.4145 | 0.04 | 250 | 2.5470 |
| 2.3295 | 0.04 | 275 | 2.5321 |
| 2.3458 | 0.05 | 300 | 2.5185 |
| 2.3587 | 0.05 | 325 | 2.5146 |
| 2.1873 | 0.06 | 350 | 2.5093 |
| 2.3502 | 0.06 | 375 | 2.5093 |
| 2.3837 | 0.06 | 400 | 2.5021 |
| 2.3747 | 0.07 | 425 | 2.4994 |
| 2.3292 | 0.07 | 450 | 2.4957 |
| 2.2438 | 0.08 | 475 | 2.4940 |
| 2.3102 | 0.08 | 500 | 2.4889 |
| 2.3791 | 0.08 | 525 | 2.4858 |
| 2.2743 | 0.09 | 550 | 2.4827 |
| 2.4148 | 0.09 | 575 | 2.4813 |
| 2.2115 | 0.1 | 600 | 2.4830 |
| 2.2963 | 0.1 | 625 | 2.4834 |
| 2.3762 | 0.1 | 650 | 2.4805 |
| 2.3657 | 0.11 | 675 | 2.4764 |
| 2.3219 | 0.11 | 700 | 2.4746 |
| 2.3166 | 0.12 | 725 | 2.4712 |
| 2.2193 | 0.12 | 750 | 2.4747 |
| 2.2629 | 0.12 | 775 | 2.4703 |
| 2.3504 | 0.13 | 800 | 2.4732 |
| 2.3523 | 0.13 | 825 | 2.4662 |
| 2.3362 | 0.14 | 850 | 2.4645 |
| 2.202 | 0.14 | 875 | 2.4659 |
| 2.2795 | 0.14 | 900 | 2.4682 |
| 2.2254 | 0.15 | 925 | 2.4621 |
| 2.3507 | 0.15 | 950 | 2.4642 |
| 2.2825 | 0.16 | 975 | 2.4624 |
| 2.3301 | 0.16 | 1000 | 2.4603 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_30000
|
DragosGorduza
| 2024-02-16T08:49:03Z | 46 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:48:23Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
shanhy/xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train
|
shanhy
| 2024-02-16T08:48:02Z | 101 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T08:46:59Z |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_lr0.0001_seed42_basic_original_esp-hau-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0382
- Spearman Corr: 0.5403
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.45 | 200 | 0.0446 | 0.0681 |
| 0.0622 | 2.91 | 400 | 0.0538 | nan |
| 0.0586 | 4.36 | 600 | 0.0468 | -0.0637 |
| 0.0586 | 5.82 | 800 | 0.0450 | -0.0228 |
| 0.0578 | 7.27 | 1000 | 0.0468 | nan |
| 0.0569 | 8.73 | 1200 | 0.0437 | 0.1100 |
| 0.0538 | 10.18 | 1400 | 0.0431 | 0.2566 |
| 0.0538 | 11.64 | 1600 | 0.0465 | nan |
| 0.0539 | 13.09 | 1800 | 0.0447 | 0.0202 |
| 0.0558 | 14.55 | 2000 | 0.0463 | 0.1008 |
| 0.0561 | 16.0 | 2200 | 0.0456 | nan |
| 0.0561 | 17.45 | 2400 | 0.0448 | 0.0437 |
| 0.0552 | 18.91 | 2600 | 0.0473 | 0.0297 |
| 0.0512 | 20.36 | 2800 | 0.0522 | 0.1850 |
| 0.0512 | 21.82 | 3000 | 0.0379 | 0.4856 |
| 0.0401 | 23.27 | 3200 | 0.0401 | 0.4589 |
| 0.0315 | 24.73 | 3400 | 0.0396 | 0.4645 |
| 0.0277 | 26.18 | 3600 | 0.0387 | 0.4813 |
| 0.0277 | 27.64 | 3800 | 0.0376 | 0.5494 |
| 0.0258 | 29.09 | 4000 | 0.0382 | 0.5403 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_50000
|
DragosGorduza
| 2024-02-16T08:47:38Z | 46 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:46:57Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_10000
|
DragosGorduza
| 2024-02-16T08:46:56Z | 47 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:46:15Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_80000
|
DragosGorduza
| 2024-02-16T08:46:15Z | 47 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:45:34Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_60000
|
DragosGorduza
| 2024-02-16T08:45:34Z | 48 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:44:51Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
DragosGorduza/FRPile_GPL_test_pipeline_DragosGorduza-FRPile_MLM_Basel-MISTRAL-notrescaled_20000
|
DragosGorduza
| 2024-02-16T08:44:07Z | 46 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2024-02-16T08:43:20Z |
---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('{MODEL_NAME}')
model = AutoModel.from_pretrained('{MODEL_NAME}')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 64875 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`gpl.toolkit.loss.MarginDistillationLoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 80000,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 350, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information -->
|
Shijia/furina_seed42_eng_kin_amh_latin_2e-05
|
Shijia
| 2024-02-16T08:42:46Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T08:41:14Z |
---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_amh_latin_2e-05
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. -->
# furina_seed42_eng_kin_amh_latin_2e-05
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0242
- Spearman Corr: 0.7470
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.75 | 200 | 0.0281 | 0.6376 |
| 0.0807 | 3.51 | 400 | 0.0246 | 0.7176 |
| 0.0216 | 5.26 | 600 | 0.0249 | 0.7301 |
| 0.0153 | 7.02 | 800 | 0.0226 | 0.7384 |
| 0.0119 | 8.77 | 1000 | 0.0236 | 0.7484 |
| 0.0096 | 10.53 | 1200 | 0.0247 | 0.7425 |
| 0.0079 | 12.28 | 1400 | 0.0226 | 0.7424 |
| 0.0068 | 14.04 | 1600 | 0.0236 | 0.7499 |
| 0.0068 | 15.79 | 1800 | 0.0235 | 0.7482 |
| 0.006 | 17.54 | 2000 | 0.0256 | 0.7489 |
| 0.0054 | 19.3 | 2200 | 0.0240 | 0.7489 |
| 0.005 | 21.05 | 2400 | 0.0242 | 0.7470 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
nldatascientist/hyp_vllm_v01_GGUF
|
nldatascientist
| 2024-02-16T08:41:09Z | 6 | 0 |
transformers
|
[
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-16T08:38:41Z |
---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: unsloth/mistral-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** nldatascientist
- **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)
|
vilm/Quyen-Pro-v0.1-GGUF
|
vilm
| 2024-02-16T08:38:18Z | 62 | 5 |
transformers
|
[
"transformers",
"gguf",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:LDJnr/Capybara",
"dataset:Intel/orca_dpo_pairs",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-02-06T16:39:52Z |
---
library_name: transformers
license: other
datasets:
- teknium/OpenHermes-2.5
- LDJnr/Capybara
- Intel/orca_dpo_pairs
- argilla/distilabel-intel-orca-dpo-pairs
language:
- en
---
# Quyen
<img src="quyen.webp" width="512" height="512" alt="Quyen">
# Model Description
Quyen is our first flagship LLM series based on the Qwen1.5 family. We introduced 6 different versions:
- **Quyen-SE (0.5B)**
- **Quyen-Mini (1.8B)**
- **Quyen (4B)**
- **Quyen-Plus (7B)**
- **Quyen-Pro (14B)**
- **Quyen-Pro-Max (72B)**
All models were trained with SFT and DPO using the following dataset:
- *OpenHermes-2.5* by **Teknium**
- *Capyabara* by **LDJ**
- *distilabel-intel-orca-dpo-pairs* by **argilla**
- *orca_dpo_pairs* by **Intel**
- and Private Data by **Ontocord** & **BEE-spoke-data**
# Prompt Template
- All Quyen models use ChatML as the default template:
```
<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Hello world.<|im_end|>
<|im_start|>assistant
```
- You can also use `apply_chat_template`:
```python
messages = [
{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."},
{"role": "user", "content": "Hello world."}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
```
# Benchmarks:
- Coming Soon! We will update the benchmarks later
# Acknowledgement
- We're incredibly grateful to **Tensoic** and **Ontocord** for their generous support with compute and data preparation.
|
wesleyyip/pony
|
wesleyyip
| 2024-02-16T08:37:39Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2024-02-16T08:36:41Z |
---
license: other
license_name: other
license_link: LICENSE
---
|
isha1/sap_abap_ft_model_orig_150
|
isha1
| 2024-02-16T08:34:55Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:32:54Z |
---
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 🤗 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]
|
worldboss/tinyllama-1.8B-Chat-nia-elbowai-ft-v2
|
worldboss
| 2024-02-16T08:26:30Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:26:27Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
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)
```
|
fzzhang/mistralv1_gsm8k_merged
|
fzzhang
| 2024-02-16T08:24:57Z | 47 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:gsm8k",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T08:11:53Z |
---
library_name: transformers
license: apache-2.0
datasets:
- gsm8k
language:
- en
pipeline_tag: text-generation
---
# 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 🤗 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]
|
technocrat3128/sentiment_analysis_Twitter_roberta_Balanced_2
|
technocrat3128
| 2024-02-16T08:15:26Z | 96 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T08:15:08Z |
---
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 🤗 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]
|
Shijia/furina_seed42_eng_amh_hau_latin_2e-05
|
Shijia
| 2024-02-16T08:12:30Z | 100 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T08:11:01Z |
---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_amh_hau_latin_2e-05
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. -->
# furina_seed42_eng_amh_hau_latin_2e-05
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0206
- Spearman Corr: 0.7847
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.55 | 200 | 0.0268 | 0.6876 |
| 0.091 | 3.1 | 400 | 0.0215 | 0.7561 |
| 0.0258 | 4.65 | 600 | 0.0199 | 0.7798 |
| 0.0187 | 6.2 | 800 | 0.0210 | 0.7878 |
| 0.0187 | 7.75 | 1000 | 0.0213 | 0.7817 |
| 0.0148 | 9.3 | 1200 | 0.0238 | 0.7787 |
| 0.0126 | 10.85 | 1400 | 0.0203 | 0.7780 |
| 0.0102 | 12.4 | 1600 | 0.0207 | 0.7800 |
| 0.0088 | 13.95 | 1800 | 0.0206 | 0.7863 |
| 0.0088 | 15.5 | 2000 | 0.0223 | 0.7771 |
| 0.0079 | 17.05 | 2200 | 0.0206 | 0.7847 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
ImNotTarzan/finetuning-sentiment-model-3000-samples
|
ImNotTarzan
| 2024-02-16T08:11:28Z | 195 | 0 |
transformers
|
[
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-13T08:37:07Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: finetuning-sentiment-model-3000-samples
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. -->
# finetuning-sentiment-model-3000-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cpu
- Datasets 2.17.0
- Tokenizers 0.15.1
|
UsmanAXAI/whisper-small-ft-common-voice-11-ar
|
UsmanAXAI
| 2024-02-16T08:07:00Z | 64 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"ar-asr-leaderboard",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-02-15T08:35:33Z |
---
language:
- hi
license: apache-2.0
tags:
- ar-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
base_model: openai/whisper-small
model-index:
- name: Whisper Small Ar - AxAI
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: ar
split: None
args: 'config: ar, split: test[:10%]'
metrics:
- type: wer
value: 116.16948508455253
name: Wer
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Ar - AxAI
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8021
- Wer: 116.1695
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0331 | 4.15 | 1000 | 0.5950 | 130.9519 |
| 0.0031 | 8.3 | 2000 | 0.7200 | 114.9724 |
| 0.0006 | 12.45 | 3000 | 0.7821 | 116.3405 |
| 0.0006 | 16.6 | 4000 | 0.8021 | 116.1695 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
ppisljar/slo_g2p_byt5
|
ppisljar
| 2024-02-16T08:06:43Z | 0 | 0 |
nemo
|
[
"nemo",
"onnx",
"sl",
"license:cc-by-3.0",
"region:us"
] | null | 2024-01-22T12:49:03Z |
---
license: cc-by-3.0
language:
- sl
metrics:
- wer
- cer
library_name: nemo
---
Slovenian G2P model
google/byt5-small trained on G2P task, with sentence level dataset
CER: 0.25%
check infer.py for example usage
|
Habana/albert-xxlarge-v1
|
Habana
| 2024-02-16T08:04:52Z | 3,087 | 0 | null |
[
"optimum_habana",
"license:apache-2.0",
"region:us"
] | null | 2022-04-22T18:05:35Z |
---
license: apache-2.0
---
[Optimum Habana](https://github.com/huggingface/optimum-habana) is the interface between the Hugging Face Transformers and Diffusers libraries and Habana's Gaudi processor (HPU).
It provides a set of tools enabling easy and fast model loading, training and inference on single- and multi-HPU settings for different downstream tasks.
Learn more about how to take advantage of the power of Habana HPUs to train and deploy Transformers and Diffusers models at [hf.co/hardware/habana](https://huggingface.co/hardware/habana).
## ALBERT XXLarge model HPU configuration
This model only contains the `GaudiConfig` file for running the [albert-xxlarge-v1](https://huggingface.co/albert-xxlarge-v1) model on Habana's Gaudi processors (HPU).
**This model contains no model weights, only a GaudiConfig.**
This enables to specify:
- `use_torch_autocast`: whether to use PyTorch's autocast mixed precision
- `use_fused_adam`: whether to use Habana's custom AdamW implementation
- `use_fused_clip_norm`: whether to use Habana's fused gradient norm clipping operator
## Usage
The model is instantiated the same way as in the Transformers library.
The only difference is that there are a few new training arguments specific to HPUs.
[Here](https://github.com/huggingface/optimum-habana/blob/main/examples/question-answering/run_qa.py) is a question-answering example script to fine-tune a model on SQuAD. You can run it with ALBERT XXL with the following command:
```bash
python run_qa.py \
--model_name_or_path albert-xxlarge-v1 \
--gaudi_config_name Habana/albert-xxlarge-v1 \
--dataset_name squad \
--do_train \
--do_eval \
--per_device_train_batch_size 12 \
--per_device_eval_batch_size 2 \
--learning_rate 5e-6 \
--num_train_epochs 2 \
--max_seq_length 384 \
--output_dir /tmp/squad/ \
--use_habana \
--use_lazy_mode \
--throughput_warmup_steps 3 \
--bf16
```
Check the [documentation](https://huggingface.co/docs/optimum/habana/index) out for more advanced usage and examples.
|
Brackly/malawi_quantized_model
|
Brackly
| 2024-02-16T08:03:28Z | 60 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-16T07:59:39Z |
---
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 🤗 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]
|
Isotonic/smol_llama_DialogSumm
|
Isotonic
| 2024-02-16T07:57:03Z | 91 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:Felladrin/Smol-Llama-101M-Chat-v1",
"base_model:finetune:Felladrin/Smol-Llama-101M-Chat-v1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-09T14:47:23Z |
---
license: apache-2.0
base_model: Felladrin/Smol-Llama-101M-Chat-v1
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: smol_llama_DialogSumm
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. -->
# smol_llama_DialogSumm
This model is a fine-tuned version of [Felladrin/Smol-Llama-101M-Chat-v1](https://huggingface.co/Felladrin/Smol-Llama-101M-Chat-v1) on [Isotonic/DialogSumm](https://huggingface.co/datasets/Isotonic/DialogSumm) dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8918
- Accuracy: 0.6050
## Model description
More information needed
## Intended uses & 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-04
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_ratio: 0.3
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 411 | 2.0053 | 0.5871 |
| 2.0885 | 2.0 | 822 | 1.9287 | 0.5971 |
| 1.8728 | 3.0 | 1233 | 1.8916 | 0.6039 |
| 1.7214 | 4.0 | 1644 | 1.8918 | 0.6050 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.1
|
Shijia/furina_seed42_eng_amh_esp_cross_2e-05
|
Shijia
| 2024-02-16T07:48:25Z | 99 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T07:46:54Z |
---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_amh_esp_cross_2e-05
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. -->
# furina_seed42_eng_amh_esp_cross_2e-05
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0188
- Spearman Corr: 0.7538
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 0.53 | 200 | 0.0279 | 0.6644 |
| No log | 1.06 | 400 | 0.0165 | 0.7300 |
| No log | 1.59 | 600 | 0.0170 | 0.7252 |
| 0.0434 | 2.12 | 800 | 0.0157 | 0.7411 |
| 0.0434 | 2.65 | 1000 | 0.0160 | 0.7515 |
| 0.0434 | 3.17 | 1200 | 0.0162 | 0.7554 |
| 0.0434 | 3.7 | 1400 | 0.0160 | 0.7602 |
| 0.019 | 4.23 | 1600 | 0.0170 | 0.7490 |
| 0.019 | 4.76 | 1800 | 0.0169 | 0.7468 |
| 0.019 | 5.29 | 2000 | 0.0165 | 0.7473 |
| 0.019 | 5.82 | 2200 | 0.0181 | 0.7492 |
| 0.0138 | 6.35 | 2400 | 0.0188 | 0.7538 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
jaehy12/jh_new_2
|
jaehy12
| 2024-02-16T07:47:56Z | 5 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T07:38:20Z |
---
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 🤗 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]
|
Mohamedarsath/dog
|
Mohamedarsath
| 2024-02-16T07:44:20Z | 1 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2024-02-16T07:35:47Z |
---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### dog Dreambooth model trained by Mohamedarsath following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: JPCE-063
Sample pictures of this concept:
|
LegoClipStars/Olivia_Woods_RH
|
LegoClipStars
| 2024-02-16T07:33:00Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"license:cc-by-4.0",
"region:us"
] |
text-to-image
| 2024-02-16T07:32:20Z |
---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: NEFT
parameters:
negative_prompt: High school student
output:
url: images/03c0f2cdac101fd97c150f00b4a871d4.jpg
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: Please spare me
license: cc-by-4.0
---
# Olivia_Woods_RH
<Gallery />
## Model description
Here's my RVC voice model of Olivia Woods from Rainbow High season 4.
## Trigger words
You should use `Please spare me` to trigger the image generation.
## Download model
[Download](/LegoClipStars/Olivia_Woods_RH/tree/main) them in the Files & versions tab.
|
VenkateshSoni/roberta-finetuned-subjqa-movies_2
|
VenkateshSoni
| 2024-02-16T07:21:15Z | 93 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-16T07:20:38Z |
---
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-finetuned-subjqa-movies_2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-finetuned-subjqa-movies_2
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
sravaniayyagari/llama2-finetuned-model-latest
|
sravaniayyagari
| 2024-02-16T07:18:53Z | 60 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2024-02-16T06:41:24Z |
---
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 🤗 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]
|
dost-asti/BigBird-tl-cased
|
dost-asti
| 2024-02-16T07:09:03Z | 118 | 0 |
transformers
|
[
"transformers",
"pytorch",
"big_bird",
"fill-mask",
"Tagalog",
"Taglish",
"tl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-17T03:44:00Z |
---
language:
- tl
tags:
- Tagalog
- Taglish
---
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano.
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
```
|
dost-asti/gpt2-tl-cased
|
dost-asti
| 2024-02-16T07:08:48Z | 191 | 0 |
transformers
|
[
"transformers",
"pytorch",
"gpt2",
"text-generation",
"Tagalog",
"Taglish",
"tl",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2023-11-17T03:24:35Z |
---
language:
- tl
tags:
- Tagalog
- Taglish
- gpt2
---
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano.
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
```
|
dost-asti/BERT-tl-cased
|
dost-asti
| 2024-02-16T07:08:29Z | 111 | 0 |
transformers
|
[
"transformers",
"pytorch",
"bert",
"fill-mask",
"Tagalog",
"Taglish",
"tl",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-16T14:09:35Z |
---
language:
- tl
tags:
- Tagalog
- Taglish
---
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano.
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 5,159,917 instances from formal channels and 3,057,180 from informal sources. More information on pre-processing and training parameters on our paper
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
```
|
CatBarks/GPT2ES_ClassWeighted10_tokenizer
|
CatBarks
| 2024-02-16T07:08:12Z | 0 | 0 |
transformers
|
[
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-02-16T07:08:11Z |
---
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 🤗 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]
|
dost-asti/RoBERTa-ceb-cased
|
dost-asti
| 2024-02-16T07:07:30Z | 101 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"RoBERTa",
"Cebuano",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-15T04:26:18Z |
---
tags:
- RoBERTa
- Cebuano
---
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano.
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels and 1,816,735 from informal sources. More information on pre-processing and training parameters on our paper
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
```
|
dost-asti/BERT-ceb-cased
|
dost-asti
| 2024-02-16T07:06:46Z | 158 | 0 |
transformers
|
[
"transformers",
"pytorch",
"roberta",
"fill-mask",
"BERT",
"Cebuano",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
fill-mask
| 2023-11-15T02:22:08Z |
---
tags:
- BERT
- Cebuano
---
## Model Description
As part of the ITANONG project's 10 billion-token Tagalog dataset, we have introduced our initial pre-trained language models for Philippine languages. Our model suite encompasses various BERT-based, GPT-based, and Sentence Transformers tailored for Tagalog,Taglish and Cebuano.
## Training Details
This model was trained using an Nvidia V100-32GB GPU on DOST-ASTI Computing and Archiving Research Environment (COARE) - https://asti.dost.gov.ph/projects/coare/
### Training Data
The training dataset was compiled from both formal and informal sources, consisting of 194,001 instances from formal channels and 1,816,735 from informal sources. More information on pre-processing and training parameters on our paper
## Citation
Paper : iTANONG-DS : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select Philippine Language
Bibtex:
```
@inproceedings{visperas-etal-2023-itanong,
title = "i{TANONG}-{DS} : A Collection of Benchmark Datasets for Downstream Natural Language Processing Tasks on Select {P}hilippine Languages",
author = "Visperas, Moses L. and
Borjal, Christalline Joie and
Adoptante, Aunhel John M and
Abacial, Danielle Shine R. and
Decano, Ma. Miciella and
Peramo, Elmer C",
editor = "Abbas, Mourad and
Freihat, Abed Alhakim",
booktitle = "Proceedings of the 6th International Conference on Natural Language and Speech Processing (ICNLSP 2023)",
month = dec,
year = "2023",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.icnlsp-1.34",
pages = "316--323",
}
```
|
Krooz/placement-classification-mistral-7b-instruct-v1
|
Krooz
| 2024-02-16T07:04:16Z | 6 | 0 |
peft
|
[
"peft",
"safetensors",
"Education",
"text-classification",
"en",
"dataset:Krooz/Campus_Recruitment_Text",
"license:cc",
"region:us"
] |
text-classification
| 2024-02-15T16:22:21Z |
---
license: cc
datasets:
- Krooz/Campus_Recruitment_Text
language:
- en
library_name: peft
pipeline_tag: text-classification
tags:
- Education
---
## Recruitment Guide Mistral 7B-Instruct
Mistral 7B instruct fine-tuned on the [Campus Recruitment Text](https://huggingface.co/datasets/Krooz/Campus_Recruitment_Text) dataset with LoRA and 4bit quantization. See the Github [repository](https://github.com/Kirushikesh/Campus_Recruitment_Prediction_LLM)
for training details. This model is trained with student's university record as input and, Placement status as output. Try out the application in huggingface [spaces](https://huggingface.co/spaces/Krooz/Campus_Recruitment_Demo)
## Usage
This repo contains the LoRA parameters of the fine-tuned Mistral 7B model. To perform inference, load and use the model as follows:
```
import torch
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
def format_instruction(input):
return return f"""### Instruction:
Classify the student into Placed/NotPlaced based on his/her college report details. The report includes marks scored by the student in various courses and extra curricular activities taken by them.
### Report:
{input}
### Label:
"""
# input is a report card of an university graduate
prompt = """John is a college-level student who has demonstrated academic excellence throughout his schooling journey. He has a cumulative GPA of 6.7 in his university, indicating his strong academic abilities. Additionally, he scored 63 on an aptitude test, showcasing his analytical and problem-solving skills. John has also engaged in one project, demonstrating his creativity and practical skills.
In terms of extracurricular activities, John is actively involved in a range of areas. He has participated in one project, which showcases his ability to work collaboratively and achieve results independently. However, he has zero internships and zero workshops/certifications, which could have been an area for improvement.
In terms of soft skills, John has a rating of 3.8, which suggests that he has strong social and communication capabilities. He has no placement training, meaning he would benefit from gaining hands-on experience in a professional environment.
Overall, John has good academic and extracurricular achievements but would benefit from gaining more practical work experience and soft skills training. He has the potential to be an excellent candidate in the future, and it would be beneficial to him to further develop these areas."""
prompt = format_instruction(prompt)
# load base LLM model, LoRA params and tokenizer
model = AutoPeftModelForCausalLM.from_pretrained(
"Krooz/placement-classification-mistral-7b-instruct-v1",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
load_in_4bit=True,
)
tokenizer = AutoTokenizer.from_pretrained("Krooz/placement-classification-mistral-7b-instruct-v1")
input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
# inference
with torch.inference_mode():
outputs = model.generate(
input_ids=input_ids,
max_new_tokens=100,
do_sample=False
)
# decode output tokens and strip response
outputs = outputs.detach().cpu().numpy()
outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)
output = outputs[0][len(prompt):]
```
References:
* https://medium.com/@jeremyarancio/fine-tune-an-llm-on-your-personal-data-create-a-the-lord-of-the-rings-storyteller-6826dd614fa9
* https://blog.neuralwork.ai/an-llm-fine-tuning-cookbook-with-mistral-7b/
* https://blog.gopenai.com/fine-tuning-mistral-7b-instruct-model-in-colab-a-beginners-guide-0f7bebccf11c
|
satheeshTM/distilbert-base-uncased-finetuned-emotion
|
satheeshTM
| 2024-02-16T07:04:13Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-01-10T04:50:32Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: emotion
type: emotion
config: split
split: validation
args: split
metrics:
- name: Accuracy
type: accuracy
value: 0.9265
- name: F1
type: f1
value: 0.9263544647982521
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2262
- Accuracy: 0.9265
- F1: 0.9264
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8603 | 1.0 | 250 | 0.3328 | 0.902 | 0.9014 |
| 0.2575 | 2.0 | 500 | 0.2262 | 0.9265 | 0.9264 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
worldboss/tinyllama-1.8B-Chat-elbowai-ft
|
worldboss
| 2024-02-16T07:01:34Z | 0 | 0 | null |
[
"safetensors",
"autotrain",
"text-generation",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T07:01:31Z |
---
tags:
- autotrain
- text-generation
widget:
- text: "I love AutoTrain because "
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)
```
|
shanhy/xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train
|
shanhy
| 2024-02-16T06:54:38Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T06:53:33Z |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_lr5e-06_seed42_basic_original_kin-amh-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0312
- Spearman Corr: 0.7479
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.75 | 200 | 0.0278 | 0.6150 |
| 0.0637 | 3.51 | 400 | 0.0254 | 0.6786 |
| 0.0312 | 5.26 | 600 | 0.0284 | 0.7074 |
| 0.0249 | 7.02 | 800 | 0.0320 | 0.7195 |
| 0.0199 | 8.77 | 1000 | 0.0344 | 0.7285 |
| 0.0189 | 10.53 | 1200 | 0.0303 | 0.7318 |
| 0.0167 | 12.28 | 1400 | 0.0299 | 0.7356 |
| 0.0154 | 14.04 | 1600 | 0.0319 | 0.7411 |
| 0.0154 | 15.79 | 1800 | 0.0304 | 0.7382 |
| 0.0141 | 17.54 | 2000 | 0.0327 | 0.7460 |
| 0.0134 | 19.3 | 2200 | 0.0305 | 0.7507 |
| 0.0123 | 21.05 | 2400 | 0.0312 | 0.7479 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Jarmac/lab1_random
|
Jarmac
| 2024-02-16T06:54:15Z | 117 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-16T04:36:38Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
model-index:
- name: lab1_random
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. -->
# lab1_random
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Fukurokun/MemGPT-DPO-MoE-2-mem-6.0bpw-exl2
|
Fukurokun
| 2024-02-16T06:53:41Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"mixtral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T06:52:36Z |
---
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 🤗 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]
|
fliarbi/phi-2-hummanize1
|
fliarbi
| 2024-02-16T06:50:25Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-02-16T06:50:15Z |
---
license: mit
library_name: peft
tags:
- generated_from_trainer
base_model: microsoft/phi-2
model-index:
- name: phi-2-hummanize1
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. -->
# phi-2-hummanize1
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7074
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0025
- train_batch_size: 20
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 20
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.02 | 1 | 2.0864 |
| No log | 0.05 | 2 | 4.0671 |
| No log | 0.07 | 3 | 3.6332 |
| No log | 0.09 | 4 | 2.5537 |
| 3.0197 | 0.11 | 5 | 2.3394 |
| 3.0197 | 0.14 | 6 | 2.8862 |
| 3.0197 | 0.16 | 7 | 2.5140 |
| 3.0197 | 0.18 | 8 | 2.4603 |
| 3.0197 | 0.21 | 9 | 2.2094 |
| 2.5958 | 0.23 | 10 | 2.1767 |
| 2.5958 | 0.25 | 11 | 2.3343 |
| 2.5958 | 0.28 | 12 | 2.2511 |
| 2.5958 | 0.3 | 13 | 2.1854 |
| 2.5958 | 0.32 | 14 | 2.1385 |
| 2.2944 | 0.34 | 15 | 2.3556 |
| 2.2944 | 0.37 | 16 | 2.2056 |
| 2.2944 | 0.39 | 17 | 2.2127 |
| 2.2944 | 0.41 | 18 | 2.1507 |
| 2.2944 | 0.44 | 19 | 2.1388 |
| 2.2841 | 0.46 | 20 | 2.6540 |
| 2.2841 | 0.48 | 21 | 2.8934 |
| 2.2841 | 0.51 | 22 | 3.0981 |
| 2.2841 | 0.53 | 23 | 2.4155 |
| 2.2841 | 0.55 | 24 | 2.1754 |
| 2.7585 | 0.57 | 25 | 2.0927 |
| 2.7585 | 0.6 | 26 | 2.0865 |
| 2.7585 | 0.62 | 27 | 2.2345 |
| 2.7585 | 0.64 | 28 | 2.4123 |
| 2.7585 | 0.67 | 29 | 2.7718 |
| 2.3906 | 0.69 | 30 | 4.2964 |
| 2.3906 | 0.71 | 31 | 6.5295 |
| 2.3906 | 0.73 | 32 | 5.8489 |
| 2.3906 | 0.76 | 33 | 7.2467 |
| 2.3906 | 0.78 | 34 | 7.6353 |
| 6.5839 | 0.8 | 35 | 7.7842 |
| 6.5839 | 0.83 | 36 | 8.8627 |
| 6.5839 | 0.85 | 37 | 7.9511 |
| 6.5839 | 0.87 | 38 | 9.7736 |
| 6.5839 | 0.9 | 39 | 8.3666 |
| 8.6795 | 0.92 | 40 | 8.9768 |
| 8.6795 | 0.94 | 41 | 9.0808 |
| 8.6795 | 0.96 | 42 | 8.5933 |
| 8.6795 | 0.99 | 43 | 8.9317 |
| 8.6795 | 1.01 | 44 | 8.5291 |
| 8.8177 | 1.03 | 45 | 8.5935 |
| 8.8177 | 1.06 | 46 | 8.6773 |
| 8.8177 | 1.08 | 47 | 8.5914 |
| 8.8177 | 1.1 | 48 | 8.5006 |
| 8.8177 | 1.13 | 49 | 8.3959 |
| 8.6883 | 1.15 | 50 | 8.2375 |
| 8.6883 | 1.17 | 51 | 8.2022 |
| 8.6883 | 1.19 | 52 | 8.2063 |
| 8.6883 | 1.22 | 53 | 8.2254 |
| 8.6883 | 1.24 | 54 | 8.3408 |
| 8.4216 | 1.26 | 55 | 8.0367 |
| 8.4216 | 1.29 | 56 | 7.8776 |
| 8.4216 | 1.31 | 57 | 7.6720 |
| 8.4216 | 1.33 | 58 | 7.5050 |
| 8.4216 | 1.35 | 59 | 7.3863 |
| 7.8151 | 1.38 | 60 | 7.3775 |
| 7.8151 | 1.4 | 61 | 7.3820 |
| 7.8151 | 1.42 | 62 | 7.2597 |
| 7.8151 | 1.45 | 63 | 7.1959 |
| 7.8151 | 1.47 | 64 | 7.1233 |
| 7.3639 | 1.49 | 65 | 7.0625 |
| 7.3639 | 1.52 | 66 | 7.0302 |
| 7.3639 | 1.54 | 67 | 6.9862 |
| 7.3639 | 1.56 | 68 | 6.9601 |
| 7.3639 | 1.58 | 69 | 6.9606 |
| 7.1152 | 1.61 | 70 | 6.8977 |
| 7.1152 | 1.63 | 71 | 6.8981 |
| 7.1152 | 1.65 | 72 | 6.8453 |
| 7.1152 | 1.68 | 73 | 6.8523 |
| 7.1152 | 1.7 | 74 | 6.8641 |
| 6.9712 | 1.72 | 75 | 6.8261 |
| 6.9712 | 1.75 | 76 | 6.8273 |
| 6.9712 | 1.77 | 77 | 6.8053 |
| 6.9712 | 1.79 | 78 | 6.7712 |
| 6.9712 | 1.81 | 79 | 6.7542 |
| 6.8925 | 1.84 | 80 | 6.7466 |
| 6.8925 | 1.86 | 81 | 6.7341 |
| 6.8925 | 1.88 | 82 | 6.7255 |
| 6.8925 | 1.91 | 83 | 6.7211 |
| 6.8925 | 1.93 | 84 | 6.7154 |
| 6.8192 | 1.95 | 85 | 6.7103 |
| 6.8192 | 1.97 | 86 | 6.7074 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.17.0
- Tokenizers 0.15.2
|
fliarbi/mistral-hummanize1
|
fliarbi
| 2024-02-16T06:49:56Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-02-16T06:49:35Z |
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral-hummanize1
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. -->
# mistral-hummanize1
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3784
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.5e-05
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 12
- total_train_batch_size: 144
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.4936 | 0.41 | 50 | 1.4588 |
| 1.434 | 0.83 | 100 | 1.4090 |
| 1.4032 | 1.24 | 150 | 1.3872 |
| 1.392 | 1.65 | 200 | 1.3784 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0
- Datasets 2.17.0
- Tokenizers 0.15.2
|
shanhy/xlm-roberta-base_lr0.0001_seed42_basic_original_amh-esp-eng_train
|
shanhy
| 2024-02-16T06:42:51Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T06:41:55Z |
---
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base_lr0.0001_seed42_basic_original_amh-esp-eng_train
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base_lr0.0001_seed42_basic_original_amh-esp-eng_train
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0250
- Spearman Corr: 0.6480
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 1.59 | 200 | 0.0288 | 0.5877 |
| 0.0428 | 3.17 | 400 | 0.0254 | 0.6442 |
| 0.0257 | 4.76 | 600 | 0.0262 | 0.6591 |
| 0.0193 | 6.35 | 800 | 0.0268 | 0.6513 |
| 0.0193 | 7.94 | 1000 | 0.0304 | 0.6502 |
| 0.0156 | 9.52 | 1200 | 0.0233 | 0.6600 |
| 0.0129 | 11.11 | 1400 | 0.0238 | 0.6470 |
| 0.0112 | 12.7 | 1600 | 0.0269 | 0.6441 |
| 0.0097 | 14.29 | 1800 | 0.0269 | 0.6461 |
| 0.0097 | 15.87 | 2000 | 0.0316 | 0.6497 |
| 0.0097 | 17.46 | 2200 | 0.0255 | 0.6536 |
| 0.0088 | 19.05 | 2400 | 0.0269 | 0.6527 |
| 0.0082 | 20.63 | 2600 | 0.0257 | 0.6516 |
| 0.008 | 22.22 | 2800 | 0.0246 | 0.6578 |
| 0.008 | 23.81 | 3000 | 0.0261 | 0.6603 |
| 0.0076 | 25.4 | 3200 | 0.0250 | 0.6480 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
tsavage68/chat_1000STEPS_1e6_05beta_DPO
|
tsavage68
| 2024-02-16T06:36:41Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T06:33:00Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: chat_1000STEPS_1e6_05beta_DPO
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. -->
# chat_1000STEPS_1e6_05beta_DPO
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7047
- Rewards/chosen: -0.5484
- Rewards/rejected: -0.8442
- Rewards/accuracies: 0.5319
- Rewards/margins: 0.2958
- Logps/rejected: -20.4796
- Logps/chosen: -17.8414
- Logits/rejected: -0.6334
- Logits/chosen: -0.6333
## Model description
More information needed
## Intended uses & 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-06
- train_batch_size: 4
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6923 | 0.2 | 100 | 0.6978 | -0.3692 | -0.4056 | 0.4549 | 0.0364 | -19.6025 | -17.4830 | -0.6054 | -0.6052 |
| 0.7106 | 0.39 | 200 | 0.7053 | 0.1136 | -0.0026 | 0.4791 | 0.1161 | -18.7964 | -16.5175 | -0.6058 | -0.6056 |
| 0.5991 | 0.59 | 300 | 0.7229 | -0.2199 | -0.3741 | 0.4879 | 0.1541 | -19.5394 | -17.1845 | -0.6117 | -0.6115 |
| 0.7082 | 0.78 | 400 | 0.7221 | -0.0056 | -0.1904 | 0.5033 | 0.1848 | -19.1721 | -16.7559 | -0.5870 | -0.5868 |
| 0.6684 | 0.98 | 500 | 0.7010 | -0.1029 | -0.3043 | 0.5275 | 0.2014 | -19.3998 | -16.9504 | -0.5454 | -0.5452 |
| 0.2004 | 1.17 | 600 | 0.6974 | -0.4104 | -0.6928 | 0.5341 | 0.2824 | -20.1768 | -17.5654 | -0.6005 | -0.6004 |
| 0.2715 | 1.37 | 700 | 0.7012 | -0.5147 | -0.8128 | 0.5429 | 0.2981 | -20.4169 | -17.7741 | -0.6258 | -0.6257 |
| 0.2303 | 1.56 | 800 | 0.7031 | -0.5366 | -0.8347 | 0.5341 | 0.2981 | -20.4606 | -17.8177 | -0.6321 | -0.6320 |
| 0.2729 | 1.76 | 900 | 0.7052 | -0.5480 | -0.8437 | 0.5341 | 0.2957 | -20.4787 | -17.8406 | -0.6333 | -0.6331 |
| 0.2621 | 1.95 | 1000 | 0.7047 | -0.5484 | -0.8442 | 0.5319 | 0.2958 | -20.4796 | -17.8414 | -0.6334 | -0.6333 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
|
yoon1000/TrOCR_0216
|
yoon1000
| 2024-02-16T06:34:58Z | 33 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:microsoft/trocr-base-stage1",
"base_model:finetune:microsoft/trocr-base-stage1",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2024-02-16T06:32:11Z |
---
base_model: microsoft/trocr-base-stage1
tags:
- generated_from_trainer
model-index:
- name: TrOCR_0216
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. -->
# TrOCR_0216
This model is a fine-tuned version of [microsoft/trocr-base-stage1](https://huggingface.co/microsoft/trocr-base-stage1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8095
- Cer: 0.0608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Cer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.626 | 0.5 | 500 | 0.9106 | 0.1848 |
| 0.5456 | 1.0 | 1000 | 0.7220 | 0.1927 |
| 0.4588 | 1.5 | 1500 | 0.7064 | 0.1240 |
| 0.6771 | 2.0 | 2000 | 0.7207 | 0.2169 |
| 0.3778 | 2.5 | 2500 | 0.6689 | 0.1283 |
| 0.4543 | 3.0 | 3000 | 0.6833 | 0.3052 |
| 0.4428 | 3.5 | 3500 | 0.6604 | 0.0893 |
| 0.4899 | 4.0 | 4000 | 0.7024 | 0.0692 |
| 0.2548 | 4.5 | 4500 | 1.0137 | 0.1599 |
| 0.0851 | 5.0 | 5000 | 1.8095 | 0.0608 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.13.0
- Tokenizers 0.15.0
|
millyjep/Milliychloe
|
millyjep
| 2024-02-16T06:26:42Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-16T06:26:42Z |
---
license: creativeml-openrail-m
---
|
Trendyol/Trendyol-LLM-7b-chat-v0.1
|
Trendyol
| 2024-02-16T06:15:07Z | 3,141 | 108 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"tr",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-01-30T12:49:34Z |
---
language:
- tr
- en
pipeline_tag: text-generation
license: apache-2.0
---
<img src="https://huggingface.co/Trendyol/Trendyol-LLM-7b-chat-v0.1/resolve/main/llama-tr-image.jpeg"
alt="drawing" width="400"/>
# **Trendyol LLM**
Trendyol LLM is a generative model that is based on LLaMa2 7B model. This is the repository for the chat model.
## Model Details
**Model Developers** Trendyol
**Variations** base and chat variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Trendyol LLM is an auto-regressive language model (based on LLaMa2 7b) that uses an optimized transformer architecture. The chat version is fine-tuned on 180K instruction sets with the following trainables by using LoRA:
- **lr**=1e-4
- **lora_rank**=64
- **lora_alpha**=128
- **lora_trainable**=q_proj,v_proj,k_proj,o_proj,gate_proj,down_proj,up_proj
- **modules_to_save**=embed_tokens,lm_head
- **lora_dropout**=0.05
- **fp16**=True
- **max_seq_length**=1024
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/peft/lora_diagram.png"
alt="drawing" width="600"/>
## Usage
```python
from transformers import AutoModelForCausalLM, LlamaTokenizer, pipeline
model_id = "Trendyol/Trendyol-LLM-7b-chat-v0.1"
tokenizer = LlamaTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id,
device_map='auto',
load_in_8bit=True)
sampling_params = dict(do_sample=True, temperature=0.3, top_k=50, top_p=0.9)
pipe = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
return_full_text=True,
repetition_penalty=1.1
)
DEFAULT_SYSTEM_PROMPT = "Sen yardımcı bir asistansın ve sana verilen talimatlar doğrultusunda en iyi cevabı üretmeye çalışacaksın.\n"
TEMPLATE = (
"[INST] <<SYS>>\n"
"{system_prompt}\n"
"<</SYS>>\n\n"
"{instruction} [/INST]"
)
def generate_prompt(instruction, system_prompt=DEFAULT_SYSTEM_PROMPT):
return TEMPLATE.format_map({'instruction': instruction,'system_prompt': system_prompt})
def generate_output(user_query, sys_prompt=DEFAULT_SYSTEM_PROMPT):
prompt = generate_prompt(user_query, sys_prompt)
outputs = pipe(prompt,
**sampling_params
)
return outputs[0]["generated_text"].split("[/INST]")[-1]
user_query = "Türkiye'de kaç il var?"
response = generate_output(user_query)
print(response)
```
with chat template:
```python
pipe = pipeline("conversational",
model=model,
tokenizer=tokenizer,
device_map="auto",
max_new_tokens=1024,
repetition_penalty=1.1
)
messages = [
{
"role": "system",
"content": "Sen yardımsever bir chatbotsun. Sana verilen diyalog akışına dikkat ederek diyaloğu devam ettir.",
},
{"role": "user", "content": "Türkiye'de kaç il var?"}
]
outputs = pipe(messages, **sampling_params)
print(outputs)
```
## Limitations, Risks, Bias, and Ethical Considerations
### Limitations and Known Biases
- **Primary Function and Application:** Trendyol LLM, an autoregressive language model, is primarily designed to predict the next token in a text string. While often used for various applications, it is important to note that it has not undergone extensive real-world application testing. Its effectiveness and reliability across diverse scenarios remain largely unverified.
- **Language Comprehension and Generation:** The model is primarily trained in standard English and Turkish. Its performance in understanding and generating slang, informal language, or other languages may be limited, leading to potential errors or misinterpretations.
- **Generation of False Information:** Users should be aware that Trendyol LLM may produce inaccurate or misleading information. Outputs should be considered as starting points or suggestions rather than definitive answers.
### Risks and Ethical Considerations
- **Potential for Harmful Use:** There is a risk that Trendyol LLM could be used to generate offensive or harmful language. We strongly discourage its use for any such purposes and emphasize the need for application-specific safety and fairness evaluations before deployment.
- **Unintended Content and Bias:** The model was trained on a large corpus of text data, which was not explicitly checked for offensive content or existing biases. Consequently, it may inadvertently produce content that reflects these biases or inaccuracies.
- **Toxicity:** Despite efforts to select appropriate training data, the model is capable of generating harmful content, especially when prompted explicitly. We encourage the open-source community to engage in developing strategies to minimize such risks.
### Recommendations for Safe and Ethical Usage
- **Human Oversight:** We recommend incorporating a human curation layer or using filters to manage and improve the quality of outputs, especially in public-facing applications. This approach can help mitigate the risk of generating objectionable content unexpectedly.
- **Application-Specific Testing:** Developers intending to use Trendyol LLM should conduct thorough safety testing and optimization tailored to their specific applications. This is crucial, as the model’s responses can be unpredictable and may occasionally be biased, inaccurate, or offensive.
- **Responsible Development and Deployment:** It is the responsibility of developers and users of Trendyol LLM to ensure its ethical and safe application. We urge users to be mindful of the model's limitations and to employ appropriate safeguards to prevent misuse or harmful consequences.
|
aisuko/opt-125m-gptq
|
aisuko
| 2024-02-16T06:14:51Z | 68 | 0 |
transformers
|
[
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] |
text-generation
| 2024-01-22T06:34:51Z |
---
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 🤗 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]
|
simprosysneel100/tiny-llam-v2
|
simprosysneel100
| 2024-02-16T06:04:18Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-02-16T05:48:08Z |
---
library_name: peft
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
---
# 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 0.8.2
|
Ashwini1412/asr-nepali
|
Ashwini1412
| 2024-02-16T06:03:52Z | 66 | 0 |
transformers
|
[
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-large-xlsr-53",
"base_model:finetune:facebook/wav2vec2-large-xlsr-53",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2024-01-05T13:08:15Z |
---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
- accuracy
model-index:
- name: asr-nepali
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. -->
# asr-nepali
This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
- Wer: 1.0
- Cer: 0.9965
- Accuracy: 0.0035
## Model description
More information needed
## Intended uses & 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 180
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---:|:------:|:--------:|
| 451.545 | 1.46 | 100 | 43.3285 | 1.0 | 0.9684 | 0.0316 |
| 194.4567 | 2.92 | 200 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 4.38 | 300 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 5.84 | 400 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 7.3 | 500 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 8.76 | 600 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 10.22 | 700 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 11.68 | 800 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 13.14 | 900 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 14.6 | 1000 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 16.06 | 1100 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 17.52 | 1200 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 18.98 | 1300 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 20.44 | 1400 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 21.9 | 1500 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 23.36 | 1600 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 24.82 | 1700 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 26.28 | 1800 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 27.74 | 1900 | nan | 1.0 | 0.9965 | 0.0035 |
| 0.0 | 29.2 | 2000 | nan | 1.0 | 0.9965 | 0.0035 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
sanjay782/test_qg
|
sanjay782
| 2024-02-16T05:49:46Z | 0 | 0 |
peft
|
[
"peft",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-hf",
"base_model:adapter:NousResearch/Llama-2-7b-hf",
"region:us"
] | null | 2024-02-16T05:43:21Z |
---
library_name: peft
base_model: NousResearch/Llama-2-7b-hf
---
# 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]
- **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 Data 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 Data 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2.dev0
|
LarryAIDraw/satsuki
|
LarryAIDraw
| 2024-02-16T05:40:15Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-16T05:33:18Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/55245/satsukiblue-archive-or-goofy-ai
|
LarryAIDraw/dim32NaiUzenkyoka-000002
|
LarryAIDraw
| 2024-02-16T05:39:53Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-16T05:32:37Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/27859/uzenkyoka-or-mato-seihei-no-slave
|
LarryAIDraw/dim32Nai5_fubukiazuma_uniform-000003
|
LarryAIDraw
| 2024-02-16T05:39:25Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-02-16T05:31:50Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/15202?modelVersionId=65665
|
hupenc/flan-t5-small-ChnSentiCorp-2
|
hupenc
| 2024-02-16T05:36:23Z | 89 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-16T05:36:12Z |
---
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: flan-t5-small-ChnSentiCorp-2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-ChnSentiCorp-2
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3113
- Accuracy: 0.6253
## Model description
More information needed
## Intended uses & 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
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3573 | 1.0 | 552 | 0.3182 | 0.6225 |
| 0.3348 | 2.0 | 1104 | 0.3134 | 0.6369 |
| 0.3303 | 3.0 | 1656 | 0.3114 | 0.6211 |
| 0.3213 | 4.0 | 2208 | 0.3113 | 0.6253 |
| 0.3215 | 5.0 | 2760 | 0.3123 | 0.6294 |
| 0.3157 | 6.0 | 3312 | 0.3123 | 0.6397 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.2
- Datasets 2.17.0
- Tokenizers 0.15.1
|
codescv123/ppo-LunarLander-v2
|
codescv123
| 2024-02-16T05:36:21Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2024-02-16T05:36:02Z |
---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 255.91 +/- 18.35
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
hemakumari/g_name
|
hemakumari
| 2024-02-16T05:28:47Z | 90 | 0 |
transformers
|
[
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T05:28:27Z |
---
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 🤗 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]
|
Evan-Lin/positive-chosen-llama-chat-without-none
|
Evan-Lin
| 2024-02-16T05:19:23Z | 1 | 0 |
peft
|
[
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-01-29T10:25:17Z |
---
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-chat-hf
model-index:
- name: dpo-llama-chat-without-none
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. -->
# dpo-llama-chat-without-none
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.9481
- Rewards/chosen: 4.6795
- Rewards/rejected: 2.8189
- Rewards/accuracies: 0.8547
- Rewards/margins: 1.8606
- Logps/rejected: -60.8495
- Logps/chosen: -50.0326
- Logits/rejected: -0.2216
- Logits/chosen: -0.2323
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 6.3 | 0.24 | 100 | 6.1290 | 3.4767 | 3.2110 | 0.5920 | 0.2657 | -56.9286 | -62.0606 | -0.2723 | -0.2654 |
| 5.5843 | 0.48 | 200 | 5.8936 | 3.6904 | 3.2305 | 0.6520 | 0.4599 | -56.7330 | -59.9230 | 0.2517 | 0.2475 |
| 5.757 | 0.72 | 300 | 5.6694 | 3.9164 | 3.1893 | 0.7253 | 0.7271 | -57.1450 | -57.6631 | 0.3505 | 0.3418 |
| 5.5385 | 0.96 | 400 | 5.4629 | 4.1466 | 3.1351 | 0.7600 | 1.0115 | -57.6871 | -55.3611 | 0.2059 | 0.1970 |
| 5.2301 | 1.2 | 500 | 5.2891 | 4.3324 | 3.0305 | 0.7880 | 1.3020 | -58.7338 | -53.5027 | 0.1063 | 0.0968 |
| 5.0115 | 1.44 | 600 | 5.1601 | 4.4582 | 2.9458 | 0.8213 | 1.5124 | -59.5800 | -52.2452 | -0.1082 | -0.1154 |
| 4.9893 | 1.68 | 700 | 5.0431 | 4.5787 | 2.9142 | 0.8413 | 1.6645 | -59.8968 | -51.0404 | -0.1716 | -0.1829 |
| 5.0292 | 1.92 | 800 | 4.9770 | 4.6501 | 2.8827 | 0.8427 | 1.7673 | -60.2111 | -50.3266 | -0.1929 | -0.2042 |
| 4.331 | 2.16 | 900 | 4.9577 | 4.6724 | 2.8191 | 0.8480 | 1.8534 | -60.8478 | -50.1027 | -0.2005 | -0.2121 |
| 4.5481 | 2.4 | 1000 | 4.9481 | 4.6795 | 2.8189 | 0.8547 | 1.8606 | -60.8495 | -50.0326 | -0.2216 | -0.2323 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.36.2
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
thrunlab/Mistral_Sparse_refined_web_90p_2024-02-15
|
thrunlab
| 2024-02-16T05:16:51Z | 3 | 0 |
transformers
|
[
"transformers",
"safetensors",
"sparse_mistral",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] |
text-generation
| 2024-02-16T04:13:03Z |
---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: Mistral_Sparse_refined_web_90p_2024-02-15
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. -->
# Mistral_Sparse_refined_web_90p_2024-02-15
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 7.5010
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
EENDA/distilbert-finetuned-squadv2
|
EENDA
| 2024-02-16T05:10:45Z | 92 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
question-answering
| 2024-02-16T02:37:55Z |
---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned-squadv2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-finetuned-squadv2
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
|
ggomma/aika-lora-test-247c3da0-d57b-4072-abba-1447069513f6
|
ggomma
| 2024-02-16T04:57:59Z | 4 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:KantoRegion/99mix-converted",
"base_model:adapter:KantoRegion/99mix-converted",
"license:creativeml-openrail-m",
"region:us"
] |
text-to-image
| 2024-02-16T03:27:28Z |
---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
base_model: ggomma/test
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# LoRA text2image fine-tuning - ggomma/aika-lora-test-247c3da0-d57b-4072-abba-1447069513f6
These are LoRA adaption weights for ggomma/test. The weights were fine-tuned on the ggomma/aika-images dataset. You can find some example images in the following.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model]
|
FINNUMBER/Yi-Ko-6B-Finch-TQA-full
|
FINNUMBER
| 2024-02-16T04:54:36Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T04:17:57Z |
---
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 🤗 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]
|
Kudod/my_awesome_en_vi_model
|
Kudod
| 2024-02-16T04:53:47Z | 3 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"base_model:CLAck/en-vi",
"base_model:finetune:CLAck/en-vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-16T04:02:11Z |
---
license: apache-2.0
base_model: CLAck/en-vi
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: my_awesome_en_vi_model
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. -->
# my_awesome_en_vi_model
This model is a fine-tuned version of [CLAck/en-vi](https://huggingface.co/CLAck/en-vi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4345
- Bleu: 26.4223
- Gen Len: 33.0654
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.3586 | 1.0 | 8333 | 0.4580 | 24.9697 | 32.9346 |
| 0.3303 | 2.0 | 16666 | 0.4345 | 26.4223 | 33.0654 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.1+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
|
tsavage68/chat_1000STEPS_1e6rate_SFT_SFT
|
tsavage68
| 2024-02-16T04:51:33Z | 4 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:finetune:meta-llama/Llama-2-7b-chat-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T04:48:17Z |
---
base_model: meta-llama/Llama-2-7b-chat-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: chat_1000STEPS_1e6rate_SFT_SFT
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. -->
# chat_1000STEPS_1e6rate_SFT_SFT
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3054
## Model description
More information needed
## Intended uses & 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-06
- train_batch_size: 4
- eval_batch_size: 1
- 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: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3957 | 0.2 | 100 | 0.3739 |
| 0.3295 | 0.39 | 200 | 0.3239 |
| 0.3211 | 0.59 | 300 | 0.3141 |
| 0.3047 | 0.78 | 400 | 0.3095 |
| 0.3072 | 0.98 | 500 | 0.3072 |
| 0.3006 | 1.17 | 600 | 0.3060 |
| 0.3109 | 1.37 | 700 | 0.3055 |
| 0.2994 | 1.56 | 800 | 0.3054 |
| 0.3219 | 1.76 | 900 | 0.3054 |
| 0.3016 | 1.95 | 1000 | 0.3054 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.0.0+cu117
- Datasets 2.17.0
- Tokenizers 0.15.2
|
LoudAI/kubwa-7b-josh
|
LoudAI
| 2024-02-16T04:49:42Z | 192 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:beomi/llama-2-ko-7b",
"base_model:merge:beomi/llama-2-ko-7b",
"base_model:defog/sqlcoder-7b-2",
"base_model:merge:defog/sqlcoder-7b-2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2024-02-16T04:49:13Z |
---
base_model:
- beomi/llama-2-ko-7b
- defog/sqlcoder-7b-2
library_name: transformers
tags:
- mergekit
- merge
---
# sqlcoder-7b-2-slerp
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)
* [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model:
model:
path: defog/sqlcoder-7b-2
dtype: float16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, -1]
model:
model:
path: defog/sqlcoder-7b-2
- layer_range: [0, -1]
model:
model:
path: beomi/llama-2-ko-7b
```
|
Jarmac/lab1_finetuning
|
Jarmac
| 2024-02-16T04:35:55Z | 118 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-15T22:46:07Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
model-index:
- name: lab1_finetuning
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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Shijia/furina_seed42_eng_kin_amh_cross_5e-06
|
Shijia
| 2024-02-16T04:29:49Z | 89 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T04:28:26Z |
---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_amh_cross_5e-06
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. -->
# furina_seed42_eng_kin_amh_cross_5e-06
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0235
- Spearman Corr: 0.7429
## Model description
More information needed
## Intended uses & 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: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 0.59 | 200 | 0.0574 | 0.0907 |
| No log | 1.17 | 400 | 0.0433 | 0.3618 |
| No log | 1.76 | 600 | 0.0302 | 0.5602 |
| 0.0806 | 2.35 | 800 | 0.0310 | 0.6452 |
| 0.0806 | 2.93 | 1000 | 0.0311 | 0.6667 |
| 0.0806 | 3.52 | 1200 | 0.0341 | 0.6832 |
| 0.0313 | 4.11 | 1400 | 0.0263 | 0.6957 |
| 0.0313 | 4.69 | 1600 | 0.0366 | 0.7020 |
| 0.0313 | 5.28 | 1800 | 0.0311 | 0.7107 |
| 0.0313 | 5.87 | 2000 | 0.0340 | 0.7112 |
| 0.0257 | 6.45 | 2200 | 0.0251 | 0.7188 |
| 0.0257 | 7.04 | 2400 | 0.0229 | 0.7220 |
| 0.0257 | 7.62 | 2600 | 0.0243 | 0.7361 |
| 0.0226 | 8.21 | 2800 | 0.0217 | 0.7414 |
| 0.0226 | 8.8 | 3000 | 0.0231 | 0.7376 |
| 0.0226 | 9.38 | 3200 | 0.0233 | 0.7431 |
| 0.0226 | 9.97 | 3400 | 0.0257 | 0.7369 |
| 0.0199 | 10.56 | 3600 | 0.0241 | 0.7474 |
| 0.0199 | 11.14 | 3800 | 0.0253 | 0.7411 |
| 0.0199 | 11.73 | 4000 | 0.0257 | 0.7478 |
| 0.0178 | 12.32 | 4200 | 0.0267 | 0.7471 |
| 0.0178 | 12.9 | 4400 | 0.0235 | 0.7429 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
haochenhe/lab1_finetuning
|
haochenhe
| 2024-02-16T04:17:54Z | 119 | 0 |
transformers
|
[
"transformers",
"safetensors",
"marian",
"text2text-generation",
"generated_from_trainer",
"dataset:kde4",
"base_model:Helsinki-NLP/opus-mt-en-fr",
"base_model:finetune:Helsinki-NLP/opus-mt-en-fr",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2024-02-15T22:30:38Z |
---
license: apache-2.0
base_model: Helsinki-NLP/opus-mt-en-fr
tags:
- generated_from_trainer
datasets:
- kde4
model-index:
- name: lab1_finetuning
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. -->
# lab1_finetuning
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
Shijia/furina_seed42_eng_kin_amh_cross_2e-05
|
Shijia
| 2024-02-16T04:11:11Z | 99 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:yihongLiu/furina",
"base_model:finetune:yihongLiu/furina",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2024-02-16T04:09:44Z |
---
base_model: yihongLiu/furina
tags:
- generated_from_trainer
model-index:
- name: furina_seed42_eng_kin_amh_cross_2e-05
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. -->
# furina_seed42_eng_kin_amh_cross_2e-05
This model is a fine-tuned version of [yihongLiu/furina](https://huggingface.co/yihongLiu/furina) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0237
- Spearman Corr: 0.7281
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Spearman Corr |
|:-------------:|:-----:|:----:|:---------------:|:-------------:|
| No log | 0.59 | 200 | 0.0274 | 0.5858 |
| No log | 1.17 | 400 | 0.0222 | 0.6777 |
| No log | 1.76 | 600 | 0.0266 | 0.7074 |
| 0.0409 | 2.35 | 800 | 0.0264 | 0.7084 |
| 0.0409 | 2.93 | 1000 | 0.0214 | 0.7181 |
| 0.0409 | 3.52 | 1200 | 0.0209 | 0.7224 |
| 0.02 | 4.11 | 1400 | 0.0213 | 0.7251 |
| 0.02 | 4.69 | 1600 | 0.0246 | 0.7180 |
| 0.02 | 5.28 | 1800 | 0.0270 | 0.7267 |
| 0.02 | 5.87 | 2000 | 0.0232 | 0.7295 |
| 0.014 | 6.45 | 2200 | 0.0218 | 0.7259 |
| 0.014 | 7.04 | 2400 | 0.0238 | 0.7276 |
| 0.014 | 7.62 | 2600 | 0.0224 | 0.7192 |
| 0.0102 | 8.21 | 2800 | 0.0237 | 0.7281 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2
|
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