modelId stringlengths 4 111 | lastModified stringlengths 24 24 | tags list | pipeline_tag stringlengths 5 30 ⌀ | author stringlengths 2 34 ⌀ | config null | securityStatus null | id stringlengths 4 111 | likes int64 0 9.53k | downloads int64 2 73.6M | library_name stringlengths 2 84 ⌀ | created timestamp[us] | card stringlengths 101 901k | card_len int64 101 901k | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Piyakrit/bert-finetuned-mrpc | 2023-10-22T05:59:32.000Z | [
"transformers",
"tf",
"bert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Piyakrit | null | null | Piyakrit/bert-finetuned-mrpc | 0 | 2 | transformers | 2023-10-22T05:31:48 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_keras_callback
model-index:
- name: bert-finetuned-mrpc
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# bert-finetuned-mrpc
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 5e-05, 'decay_steps': 1377, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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GuysTrans/bart-base-chat-512-seq-mini | 2023-10-25T02:07:26.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | GuysTrans | null | null | GuysTrans/bart-base-chat-512-seq-mini | 0 | 2 | transformers | 2023-10-22T06:38:46 | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-base-chat-512-seq-mini
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. -->
# bart-base-chat-512-seq-mini
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) 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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 125 | 1.9788 | 17.6745 | 8.08 | 14.3506 | 16.8208 | 20.0 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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HansOMEL/qa_plot | 2023-10-23T14:28:02.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | HansOMEL | null | null | HansOMEL/qa_plot | 0 | 2 | transformers | 2023-10-22T07:34:12 | ---
license: apache-2.0
base_model: hfl/chinese-bert-wwm-ext
tags:
- generated_from_trainer
model-index:
- name: qa_plot
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. -->
# qa_plot
This model is a fine-tuned version of [hfl/chinese-bert-wwm-ext](https://huggingface.co/hfl/chinese-bert-wwm-ext) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1978
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.1866 | 1.0 | 13687 | 1.1681 |
| 0.8957 | 2.0 | 27374 | 1.1712 |
| 1.113 | 3.0 | 41061 | 1.5591 |
| 0.8321 | 4.0 | 54748 | 1.6299 |
| 0.6172 | 5.0 | 68435 | 1.6239 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
TanmaySah/lb15epoch | 2023-10-23T11:19:49.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/lb15epoch | 0 | 2 | peft | 2023-10-22T13:14:13 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
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] |
zibaatak/book-buddy-question-generator | 2023-11-03T13:37:32.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:squad",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | zibaatak | null | null | zibaatak/book-buddy-question-generator | 0 | 2 | transformers | 2023-10-22T14:09:35 | ---
language:
- en
library_name: transformers
pipeline_tag: text2text-generation
license: mit
datasets:
- squad
---
# Book Buddy - Question Generator
This fine-tuned model was generated to help students study.
By submitting their texts, the model will generate a question to help them study.
## Model Details
- **Model Architecture**: T5
- **Tokenizer Used**:
- **Language**: English
- **Task**: Question Generation
## Model Usage
### How to Use
Provide instructions on how to use your model in a clear and concise manner.
```python
# Example code for using the model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained("t5_tokenizer")
# Load the model
model = AutoModelForSeq2SeqLM.from_pretrained("t5_trained_model")
# Generate a question
input_text = "Provide a sample input text."
input_ids = tokenizer.encode(input_text, return_tensors="pt", padding=True, max_length=512, truncation=True)
# Generate question
question_ids = model.generate(input_ids, max_length=32, num_return_sequences=1, num_beams=4)
# Decode the generated question
generated_question = tokenizer.decode(question_ids[0], skip_special_tokens=True)
print(f"Generated Question: {generated_question}") | 1,260 | [
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] |
AzureBlack/PsyMedRP-v1-20B-8bpw-8h-exl2 | 2023-10-25T16:15:16.000Z | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"nsfw",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | AzureBlack | null | null | AzureBlack/PsyMedRP-v1-20B-8bpw-8h-exl2 | 0 | 2 | transformers | 2023-10-22T15:04:44 | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
- nsfw
---
Exllama 2 version of the model created by the work of Undi95
Original Model https://huggingface.co/Undi95/PsyMedRP-v1-20B
Requires ExllamaV2, which is being developed by turboderp https://github.com/turboderp/exllamav2 under an MIT license.
```
PsyMedRP-v1-13B-p1:
[jondurbin/airoboros-l2-13b-3.0](0.85) x [ehartford/Samantha-1.11-13b](0.15)
PsyMedRP-v1-13B-p2:
[Xwin-LM/Xwin-LM-13B-V0.1](0.85) x [chaoyi-wu/MedLLaMA_13B](0.15)
PsyMedRP-v1-20B-p1:
[PsyMedRP-v1-13B-p1](0.90) x [migtissera/Synthia-13B-v1.2](0.10)
PsyMedRP-v1-20B-p2:
[PsyMedRP-v1-13B-p2](0.90) x [migtissera/Synthia-13B-v1.2](0.10)
PsyMedRP-v1-20B-p3:
[Huginn merge with Gryphe gradient to PsyMedRP-v1-20B-p1]
PsyMedRP-v1-20B-p4:
[Huginn merge with Gryphe gradient to PsyMedRP-v1-20B-p2]
PsyMedRP-v1-20B-p5:
Apply Undi95/LimaRP-v3-120-Days at 0.3 weight to PsyMedRP-v1-20B-p3
PsyMedRP-v1-20B-p6:
Apply Undi95/LimaRP-v3-120-Days at 0.3 weight to PsyMedRP-v1-20B-p4
PsyMedRP-v1-20B:
layer_slices:
- model: PsyMedRP-v1-20B-p5
start: 0
end: 16
- model: PsyMedRP-v1-20B-p6
start: 8
end: 20
- model: PsyMedRP-v1-20B-p5
start: 17
end: 32
- model: PsyMedRP-v1-20B-p6
start: 21
end: 40
```
In testing.
If you want to support me, you can [here](https://ko-fi.com/undiai). | 1,349 | [
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anandhan/Llama-2-7b-chat-hf-fine-tuned-adapters | 2023-10-22T15:33:04.000Z | [
"peft",
"region:us"
] | null | anandhan | null | null | anandhan/Llama-2-7b-chat-hf-fine-tuned-adapters | 0 | 2 | peft | 2023-10-22T15:33:02 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
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Naveengo/distilbert-finetune-on-imdb-for-masked_language | 2023-10-22T16:54:45.000Z | [
"transformers",
"tf",
"distilbert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | Naveengo | null | null | Naveengo/distilbert-finetune-on-imdb-for-masked_language | 0 | 2 | transformers | 2023-10-22T16:21:47 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: Naveengo/distilbert-finetune-on-imdb-for-masked_language
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Naveengo/distilbert-finetune-on-imdb-for-masked_language
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.2281
- Validation Loss: 2.1187
- Epoch: 1
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.2376 | 2.1858 | 0 |
| 2.2281 | 2.1187 | 1 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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art-bashkirev/DistilBERT-Test | 2023-10-22T19:26:34.000Z | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | art-bashkirev | null | null | art-bashkirev/DistilBERT-Test | 0 | 2 | transformers | 2023-10-22T18:28:18 | ---
license: apache-2.0
base_model: distilbert-base-cased
tags:
- generated_from_keras_callback
model-index:
- name: art-bashkirev/DistilBERT-Test
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# art-bashkirev/DistilBERT-Test
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6089
- Epoch: 8
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': 0.001, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Epoch |
|:----------:|:-----:|
| 0.6125 | 0 |
| 0.6085 | 1 |
| 0.6085 | 2 |
| 0.6090 | 3 |
| 0.6083 | 4 |
| 0.6089 | 5 |
| 0.6091 | 6 |
| 0.6108 | 7 |
| 0.6089 | 8 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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Sheryl815/clip-vit-base-patch32-purr | 2023-10-24T02:19:11.000Z | [
"transformers",
"pytorch",
"tf",
"jax",
"clip",
"zero-shot-image-classification",
"vision",
"arxiv:2103.00020",
"arxiv:1908.04913",
"endpoints_compatible",
"region:us"
] | zero-shot-image-classification | Sheryl815 | null | null | Sheryl815/clip-vit-base-patch32-purr | 0 | 2 | transformers | 2023-10-22T19:54:16 | ---
tags:
- vision
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/cat-dog-music.png
candidate_labels: playing music, playing sports
example_title: Cat & Dog
---
# Model Card: CLIP
Disclaimer: The model card is taken and modified from the official CLIP repository, it can be found [here](https://github.com/openai/CLIP/blob/main/model-card.md).
## Model Details
The CLIP model was developed by researchers at OpenAI to learn about what contributes to robustness in computer vision tasks. The model was also developed to test the ability of models to generalize to arbitrary image classification tasks in a zero-shot manner. It was not developed for general model deployment - to deploy models like CLIP, researchers will first need to carefully study their capabilities in relation to the specific context they’re being deployed within.
### Model Date
January 2021
### Model Type
The model uses a ViT-B/32 Transformer architecture as an image encoder and uses a masked self-attention Transformer as a text encoder. These encoders are trained to maximize the similarity of (image, text) pairs via a contrastive loss.
The original implementation had two variants: one using a ResNet image encoder and the other using a Vision Transformer. This repository has the variant with the Vision Transformer.
### Documents
- [Blog Post](https://openai.com/blog/clip/)
- [CLIP Paper](https://arxiv.org/abs/2103.00020)
### Use with Transformers
```python3
from PIL import Image
import requests
from transformers import CLIPProcessor, CLIPModel
model = CLIPModel.from_pretrained("openai/clip-vit-base-patch32")
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
inputs = processor(text=["a photo of a cat", "a photo of a dog"], images=image, return_tensors="pt", padding=True)
outputs = model(**inputs)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
```
## Model Use
### Intended Use
The model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such models - the CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
#### Primary intended uses
The primary intended users of these models are AI researchers.
We primarily imagine the model will be used by researchers to better understand robustness, generalization, and other capabilities, biases, and constraints of computer vision models.
### Out-of-Scope Use Cases
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
## Data
The model was trained on publicly available image-caption data. This was done through a combination of crawling a handful of websites and using commonly-used pre-existing image datasets such as [YFCC100M](http://projects.dfki.uni-kl.de/yfcc100m/). A large portion of the data comes from our crawling of the internet. This means that the data is more representative of people and societies most connected to the internet which tend to skew towards more developed nations, and younger, male users.
### Data Mission Statement
Our goal with building this dataset was to test out robustness and generalizability in computer vision tasks. As a result, the focus was on gathering large quantities of data from different publicly-available internet data sources. The data was gathered in a mostly non-interventionist manner. However, we only crawled websites that had policies against excessively violent and adult images and allowed us to filter out such content. We do not intend for this dataset to be used as the basis for any commercial or deployed model and will not be releasing the dataset.
## Performance and Limitations
### Performance
We have evaluated the performance of CLIP on a wide range of benchmarks across a variety of computer vision datasets such as OCR to texture recognition to fine-grained classification. The paper describes model performance on the following datasets:
- Food101
- CIFAR10
- CIFAR100
- Birdsnap
- SUN397
- Stanford Cars
- FGVC Aircraft
- VOC2007
- DTD
- Oxford-IIIT Pet dataset
- Caltech101
- Flowers102
- MNIST
- SVHN
- IIIT5K
- Hateful Memes
- SST-2
- UCF101
- Kinetics700
- Country211
- CLEVR Counting
- KITTI Distance
- STL-10
- RareAct
- Flickr30
- MSCOCO
- ImageNet
- ImageNet-A
- ImageNet-R
- ImageNet Sketch
- ObjectNet (ImageNet Overlap)
- Youtube-BB
- ImageNet-Vid
## Limitations
CLIP and our analysis of it have a number of limitations. CLIP currently struggles with respect to certain tasks such as fine grained classification and counting objects. CLIP also poses issues with regards to fairness and bias which we discuss in the paper and briefly in the next section. Additionally, our approach to testing CLIP also has an important limitation- in many cases we have used linear probes to evaluate the performance of CLIP and there is evidence suggesting that linear probes can underestimate model performance.
### Bias and Fairness
We find that the performance of CLIP - and the specific biases it exhibits - can depend significantly on class design and the choices one makes for categories to include and exclude. We tested the risk of certain kinds of denigration with CLIP by classifying images of people from [Fairface](https://arxiv.org/abs/1908.04913) into crime-related and non-human animal categories. We found significant disparities with respect to race and gender. Additionally, we found that these disparities could shift based on how the classes were constructed. (Details captured in the Broader Impacts Section in the paper).
We also tested the performance of CLIP on gender, race and age classification using the Fairface dataset (We default to using race categories as they are constructed in the Fairface dataset.) in order to assess quality of performance across different demographics. We found accuracy >96% across all races for gender classification with ‘Middle Eastern’ having the highest accuracy (98.4%) and ‘White’ having the lowest (96.5%). Additionally, CLIP averaged ~93% for racial classification and ~63% for age classification. Our use of evaluations to test for gender, race and age classification as well as denigration harms is simply to evaluate performance of the model across people and surface potential risks and not to demonstrate an endorsement/enthusiasm for such tasks.
## Feedback
### Where to send questions or comments about the model
Please use [this Google Form](https://forms.gle/Uv7afRH5dvY34ZEs9) | 7,930 | [
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LoneStriker/SynthIA-7B-v2.0-3.0bpw-h6-exl2 | 2023-10-22T21:04:25.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/SynthIA-7B-v2.0-3.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-22T21:04:12 | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is insulin synthesized?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/SynthIA-7B-v2.0"
output_file_path = "./SynthIA-7B-v2.0-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
``` | 2,322 | [
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] |
LoneStriker/SynthIA-7B-v2.0-8.0bpw-h6-exl2 | 2023-10-22T21:32:45.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | LoneStriker | null | null | LoneStriker/SynthIA-7B-v2.0-8.0bpw-h6-exl2 | 0 | 2 | transformers | 2023-10-22T21:31:55 | ---
license: apache-2.0
pipeline_tag: text-generation
language:
- en
library_name: transformers
---
## Example Usage
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is insulin synthesized?
ASSISTANT:
```
### Code example:
```python
import torch, json
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "migtissera/SynthIA-7B-v2.0"
output_file_path = "./SynthIA-7B-v2.0-conversations.jsonl"
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
``` | 2,322 | [
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Meztli66/speecht5_finetuned_voxpopuli_sl | 2023-10-23T02:31:28.000Z | [
"transformers",
"pytorch",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"dataset:voxpopuli",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | Meztli66 | null | null | Meztli66/speecht5_finetuned_voxpopuli_sl | 0 | 2 | transformers | 2023-10-23T01:42:32 | ---
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: speecht5_finetuned_voxpopuli_sl
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. -->
# speecht5_finetuned_voxpopuli_sl
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on the voxpopuli dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4727
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 2
- 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
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4897 | 34.19 | 1000 | 0.4727 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hung200504/bert-covidqa | 2023-10-23T02:05:27.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | hung200504 | null | null | hung200504/bert-covidqa | 0 | 2 | transformers | 2023-10-23T02:05:06 | ---
base_model: phiyodr/bert-base-finetuned-squad2
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: bert-covidqa
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-covidqa
This model is a fine-tuned version of [phiyodr/bert-base-finetuned-squad2](https://huggingface.co/phiyodr/bert-base-finetuned-squad2) on the covid_qa_deepset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6451
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5829 | 0.04 | 5 | 1.0001 |
| 0.7899 | 0.09 | 10 | 0.7849 |
| 0.5929 | 0.13 | 15 | 0.7851 |
| 0.691 | 0.18 | 20 | 0.7549 |
| 0.6383 | 0.22 | 25 | 0.7199 |
| 0.3216 | 0.26 | 30 | 0.7625 |
| 0.3273 | 0.31 | 35 | 0.8644 |
| 0.5909 | 0.35 | 40 | 0.7117 |
| 0.2556 | 0.39 | 45 | 0.6681 |
| 0.6896 | 0.44 | 50 | 0.7138 |
| 0.6066 | 0.48 | 55 | 0.6614 |
| 0.2602 | 0.53 | 60 | 0.6791 |
| 0.4034 | 0.57 | 65 | 0.7168 |
| 0.5511 | 0.61 | 70 | 0.7783 |
| 0.6313 | 0.66 | 75 | 0.7269 |
| 0.261 | 0.7 | 80 | 0.7106 |
| 0.4904 | 0.75 | 85 | 0.6735 |
| 0.4706 | 0.79 | 90 | 0.6370 |
| 0.4174 | 0.83 | 95 | 0.6355 |
| 0.3762 | 0.88 | 100 | 0.6356 |
| 0.5128 | 0.92 | 105 | 0.6429 |
| 0.553 | 0.96 | 110 | 0.6451 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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hung200504/bert-covidqa-3 | 2023-10-23T03:32:33.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:covid_qa_deepset",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | hung200504 | null | null | hung200504/bert-covidqa-3 | 0 | 2 | transformers | 2023-10-23T03:32:12 | ---
license: cc-by-4.0
base_model: deepset/bert-base-uncased-squad2
tags:
- generated_from_trainer
datasets:
- covid_qa_deepset
model-index:
- name: bert-covidqa-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-covidqa-3
This model is a fine-tuned version of [deepset/bert-base-uncased-squad2](https://huggingface.co/deepset/bert-base-uncased-squad2) on the covid_qa_deepset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3717
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6653 | 0.04 | 5 | 0.4879 |
| 0.2392 | 0.09 | 10 | 0.4815 |
| 0.4918 | 0.13 | 15 | 0.4405 |
| 0.3634 | 0.18 | 20 | 0.4156 |
| 0.6494 | 0.22 | 25 | 0.3953 |
| 0.2573 | 0.26 | 30 | 0.3845 |
| 0.3645 | 0.31 | 35 | 0.3737 |
| 0.5168 | 0.35 | 40 | 0.3656 |
| 0.5341 | 0.39 | 45 | 0.3680 |
| 0.4362 | 0.44 | 50 | 0.3774 |
| 0.5495 | 0.48 | 55 | 0.3692 |
| 0.5316 | 0.53 | 60 | 0.3496 |
| 0.4068 | 0.57 | 65 | 0.3414 |
| 0.4793 | 0.61 | 70 | 0.3470 |
| 0.7173 | 0.66 | 75 | 0.3517 |
| 0.5335 | 0.7 | 80 | 0.3646 |
| 0.7152 | 0.75 | 85 | 0.3848 |
| 0.7003 | 0.79 | 90 | 0.3962 |
| 0.2466 | 0.83 | 95 | 0.3971 |
| 0.415 | 0.88 | 100 | 0.3879 |
| 0.4797 | 0.92 | 105 | 0.3767 |
| 0.7039 | 0.96 | 110 | 0.3717 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
jl0414/pokemon-lora | 2023-10-23T08:32:50.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | jl0414 | null | null | jl0414/pokemon-lora | 0 | 2 | diffusers | 2023-10-23T03:38:44 |
---
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 - jl0414/pokemon-lora
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/pokemon-blip-captions dataset. You can find some example images in the following.
| 540 | [
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Rahul-G/my_awesome_model | 2023-10-23T08:20:44.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Rahul-G | null | null | Rahul-G/my_awesome_model | 0 | 2 | transformers | 2023-10-23T05:10:45 | ---
license: apache-2.0
base_model: bert-base-multilingual-cased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_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_model
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5676
- Accuracy: 0.7170
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 8 | 0.5722 | 0.6981 |
| No log | 2.0 | 16 | 0.5664 | 0.6981 |
| No log | 3.0 | 24 | 0.5584 | 0.6981 |
| No log | 4.0 | 32 | 0.5621 | 0.6981 |
| No log | 5.0 | 40 | 0.5593 | 0.6981 |
| No log | 6.0 | 48 | 0.5627 | 0.6981 |
| No log | 7.0 | 56 | 0.5641 | 0.7170 |
| No log | 8.0 | 64 | 0.5528 | 0.7170 |
| No log | 9.0 | 72 | 0.5593 | 0.7170 |
| No log | 10.0 | 80 | 0.5676 | 0.7170 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 1.13.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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kriszhou/finetuning-sentiment-model-3000-samples | 2023-10-23T07:22:09.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | kriszhou | null | null | kriszhou/finetuning-sentiment-model-3000-samples | 0 | 2 | transformers | 2023-10-23T06:15:16 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-3000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: test
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.86
- name: F1
type: f1
value: 0.8636363636363636
---
<!-- This model card 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 the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3528
- Accuracy: 0.86
- F1: 0.8636
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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
### Training results
### Framework versions
- Transformers 4.34.0
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.14.1
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thanhnew2001/bloom7b1 | 2023-10-23T07:34:57.000Z | [
"transformers",
"text-generation",
"ak",
"ar",
"as",
"bm",
"bn",
"ca",
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"arxiv:1909.08053",
"arxiv:2110.02861",
"arxiv:2108.12409",
"license:bigscience-bloom-rail-1.0",
"endpoints_compatible",
"region:us"
] | text-generation | thanhnew2001 | null | null | thanhnew2001/bloom7b1 | 0 | 2 | transformers | 2023-10-23T07:08:33 | ---
license: bigscience-bloom-rail-1.0
language:
- ak
- ar
- as
- bm
- bn
- ca
- code
- en
- es
- eu
- fon
- fr
- gu
- hi
- id
- ig
- ki
- kn
- lg
- ln
- ml
- mr
- ne
- nso
- ny
- or
- pa
- pt
- rn
- rw
- sn
- st
- sw
- ta
- te
- tn
- ts
- tum
- tw
- ur
- vi
- wo
- xh
- yo
- zh
- zhs
- zht
- zu
pipeline_tag: text-generation
---
<h1 style='text-align: center '>BLOOM LM</h1>
<h2 style='text-align: center '><em>BigScience Large Open-science Open-access Multilingual Language Model</em> </h2>
<h3 style='text-align: center '>Model Card</h3>
<img src="https://s3.amazonaws.com/moonup/production/uploads/1657124309515-5f17f0a0925b9863e28ad517.png" alt="BigScience Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
Version 1.0 / 26.May.2022
## Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Data](#training-data)
4. [Risks and Limitations](#risks-and-limitations)
5. [Evaluation](#evaluation)
6. [Recommendations](#recommendations)
7. [Glossary and Calculations](#glossary-and-calculations)
8. [More Information](#more-information)
9. [Model Card Authors](#model-card-authors)
## Model Details
### Basics
*This section provides information for anyone who wants to know about the model.*
<details>
<summary>Click to expand</summary> <br/>
**Developed by:** BigScience ([website](https://bigscience.huggingface.co))
* All collaborators are either volunteers or have an agreement with their employer. *(Further breakdown of participants forthcoming.)*
**Model Type:** Transformer-based Language Model
**Version:** 1.0.0
**Languages:** Multiple; see [training data](#training-data)
**License:** RAIL License v1.0 ([link](https://huggingface.co/spaces/bigscience/license))
**Release Date Estimate:** Monday, 11.July.2022
**Send Questions to:** bigscience-contact@googlegroups.com
**Cite as:** BigScience, _BigScience Language Open-science Open-access Multilingual (BLOOM) Language Model_. International, May 2021-May 2022
**Funded by:**
* The French government.
* Hugging Face ([website](https://huggingface.co)).
* Organizations of contributors. *(Further breakdown of organizations forthcoming.)*
</details>
### Technical Specifications
*This section provides information for people who work on model development.*
<details>
<summary>Click to expand</summary><br/>
Please see [the BLOOM training README](https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml#readme) for full details on replicating training.
**Model Architecture:** Modified from Megatron-LM GPT2 (see [paper](https://arxiv.org/abs/1909.08053), [BLOOM Megatron code](https://github.com/bigscience-workshop/Megatron-DeepSpeed)):
* Decoder-only architecture
* Layer normalization applied to word embeddings layer (`StableEmbedding`; see [code](https://github.com/facebookresearch/bitsandbytes), [paper](https://arxiv.org/pdf/2110.02861.pdf))
* ALiBI positional encodings (see [paper](https://arxiv.org/pdf/2108.12409.pdf)), with GeLU activation functions
* 7,069,016,064 parameters:
* 1,027,604,480 embedding parameters
* 30 layers, 32 attention heads
* Hidden layers are 4096-dimensional
* Sequence length of 2048 tokens used (see [BLOOM tokenizer](https://huggingface.co/bigscience/tokenizer), [tokenizer description](#tokenization))
**Objective Function:** Cross Entropy with mean reduction (see [API documentation](https://pytorch.org/docs/stable/generated/torch.nn.CrossEntropyLoss.html#torch.nn.CrossEntropyLoss)).
**Compute infrastructure:** Jean Zay Public Supercomputer, provided by the French government (see [announcement](https://www.enseignementsup-recherche.gouv.fr/fr/signature-du-marche-d-acquisition-de-l-un-des-supercalculateurs-les-plus-puissants-d-europe-46733)).
* Hardware: 384 A100 80GB GPUs (48 nodes):
* Additional 32 A100 80GB GPUs (4 nodes) in reserve
* 8 GPUs per node Using NVLink 4 inter-gpu connects, 4 OmniPath links
* CPU: AMD
* CPU memory: 512GB per node
* GPU memory: 640GB per node
* Inter-node connect: Omni-Path Architecture (OPA)
* NCCL-communications network: a fully dedicated subnet
* Disc IO network: shared network with other types of nodes
* Software:
* Megatron-DeepSpeed ([Github link](https://github.com/bigscience-workshop/Megatron-DeepSpeed))
* DeepSpeed ([Github link](https://github.com/microsoft/DeepSpeed))
* PyTorch (pytorch-1.11 w/ CUDA-11.5; see [Github link](https://github.com/pytorch/pytorch))
* apex ([Github link](https://github.com/NVIDIA/apex))
#### **Training**
Training logs: [Tensorboard link](https://huggingface.co/tensorboard/bigscience/tr11c-2B5-logs)
- Number of epochs: 1 (*current target*)
- Dates:
- Started 11th March, 2022 11:42am PST
- Ended 5th July, 2022
- Estimated cost of training: Equivalent of $2-5M in cloud computing (including preliminary experiments)
- Server training location: Île-de-France, France
#### **Tokenization**
The BLOOM tokenizer ([link](https://huggingface.co/bigscience/tokenizer)) is a learned subword tokenizer trained using:
- A byte-level Byte Pair Encoding (BPE) algorithm
- A simple pre-tokenization rule, no normalization
- A vocabulary size of 250,680
It was trained on a subset of a preliminary version of the corpus using alpha-weighting per language.
</details>
### Environmental Impact
<details>
<summary>Click to expand</summary><br/>
The training supercomputer, Jean Zay ([website](http://www.idris.fr/eng/jean-zay/jean-zay-presentation-eng.html)), uses mostly nuclear energy. The heat generated by it is reused for heating campus housing.
**Estimated carbon emissions:** *(Forthcoming upon completion of training.)*
**Estimated electricity usage:** *(Forthcoming upon completion of training.)*
</details>
<p> </p>
## Uses
*This section addresses questions around how the model is intended to be used, discusses the foreseeable users of the model (including those affected by the model), and describes uses that are considered out of scope or misuse of the model.
It provides information for anyone considering using the model or who is affected by the model.*
<details>
<summary>Click to expand</summary><br/>
### Intended Use
This model is being created in order to enable public research on large language models (LLMs). LLMs are intended to be used for language generation or as a pretrained base model that can be further fine-tuned for specific tasks. Use cases below are not exhaustive.
#### **Direct Use**
- Text generation
- Exploring characteristics of language generated by a language model
- Examples: Cloze tests, counterfactuals, generations with reframings
#### **Downstream Use**
- Tasks that leverage language models include: Information Extraction, Question Answering, Summarization
### Misuse and Out-of-scope Use
*This section addresses what users ought not do with the model.*
See the [BLOOM License](https://huggingface.co/spaces/bigscience/license), Attachment A, for detailed usage restrictions. The below list is non-exhaustive, but lists some easily foreseeable problematic use cases.
#### **Out-of-scope Uses**
Using the model in [high-stakes](#high-stakes) settings is out of scope for this model. The model is not designed for [critical decisions](#critical-decisions) nor uses with any material consequences on an individual's livelihood or wellbeing. The model outputs content that appears factual but is not correct.
##### Out-of-scope Uses Include:
- Usage in biomedical domains, political and legal domains, or finance domains
- Usage for evaluating or scoring individuals, such as for employment, education, or credit
- Applying the model for critical automatic decisions, generating factual content, creating reliable summaries, or generating predictions that must be correct
#### **Misuse**
Intentionally using the model for harm, violating [human rights](#human-rights), or other kinds of malicious activities, is a misuse of this model. This includes:
- Spam generation
- Disinformation and influence operations
- Disparagement and defamation
- Harassment and abuse
- [Deception](#deception)
- Unconsented impersonation and imitation
- Unconsented surveillance
- Generating content without attribution to the model, as specified in the [RAIL License, Use Restrictions](https://huggingface.co/spaces/bigscience/license)
### Intended Users
#### **Direct Users**
- General Public
- Researchers
- Students
- Educators
- Engineers/developers
- Non-commercial entities
- Community advocates, including human and civil rights groups
#### Indirect Users
- Users of derivatives created by Direct Users, such as those using software with an [intended use](#intended-use)
- Users of [Derivatives of the Model, as described in the License](https://huggingface.co/spaces/bigscience/license)
#### Others Affected (Parties Prenantes)
- People and groups referred to by the LLM
- People and groups exposed to outputs of, or decisions based on, the LLM
- People and groups whose original work is included in the LLM
</details>
<p> </p>
## Training Data
*This section provides a high-level overview of the training data. It is relevant for anyone who wants to know the basics of what the model is learning.*
<details>
<summary>Click to expand</summary><br/>
Details for each dataset are provided in individual [Data Cards](https://huggingface.co/spaces/bigscience/BigScienceCorpus).
Training data includes:
- 45 natural languages
- 12 programming languages
- In 1.5TB of pre-processed text, converted into 350B unique tokens (see [the tokenizer section](#tokenization) for more.)
#### **Languages**
The pie chart shows the distribution of languages in training data.
The following table shows the further distribution of Niger-Congo and Indic languages in the training data.
<details>
<summary>Click to expand</summary><br/>
| Niger Congo | Percentage | | Indic | Percentage |
|----------------|------------ |------ |-----------|------------|
| Chi Tumbuka | 0.00002 | | Assamese | 0.01 |
| Kikuyu | 0.00004 | | Odia | 0.04 |
| Bambara | 0.00004 | | Gujarati | 0.04 |
| Akan | 0.00007 | | Marathi | 0.05 |
| Xitsonga | 0.00007 | | Punjabi | 0.05 |
| Sesotho | 0.00007 | | Kannada | 0.06 |
| Chi Chewa | 0.0001 | | Nepali | 0.07 |
| Setswana | 0.0002 | | Telugu | 0.09 |
| Northern Sotho | 0.0002 | | Malayalam | 0.10 |
| Fon | 0.0002 | | Urdu | 0.10 |
| Kirundi | 0.0003 | | Tamil | 0.20 |
| Wolof | 0.0004 | | Bengali | 0.50 |
| Kuganda | 0.0004 | | Hindi | 0.70 |
| Chi Shona | 0.001 |
| Isi Zulu | 0.001 |
| Igbo | 0.001 |
| Xhosa | 0.001 |
| Kinyarwanda | 0.003 |
| Yoruba | 0.006 |
| Swahili | 0.02 |
</details>
The following table shows the distribution of programming languages.
<details>
<summary>Click to expand</summary><br/>
| Extension | Language | Number of files |
|----------------|------------|-----------------|
| java | Java | 5,407,724 |
| php | PHP | 4,942,186 |
| cpp | C++ | 2,503,930 |
| py | Python | 2,435,072 |
| js | JavaScript | 1,905,518 |
| cs | C# | 1,577,347 |
| rb | Ruby | 6,78,413 |
| cc | C++ | 443,054 |
| hpp | C++ | 391,048 |
| lua | Lua | 352,317 |
| go | GO | 227,763 |
| ts | TypeScript | 195,254 |
| C | C | 134,537 |
| scala | Scala | 92,052 |
| hh | C++ | 67,161 |
| H | C++ | 55,899 |
| tsx | TypeScript | 33,107 |
| rs | Rust | 29,693 |
| phpt | PHP | 9,702 |
| c++ | C++ | 1,342 |
| h++ | C++ | 791 |
| php3 | PHP | 540 |
| phps | PHP | 270 |
| php5 | PHP | 166 |
| php4 | PHP | 29 |
</details>
</details>
<p> </p>
## Risks and Limitations
*This section identifies foreseeable harms and misunderstandings.*
<details>
<summary>Click to expand</summary><br/>
Model may:
- Overrepresent some viewpoints and underrepresent others
- Contain stereotypes
- Contain [personal information](#personal-data-and-information)
- Generate:
- Hateful, abusive, or violent language
- Discriminatory or prejudicial language
- Content that may not be appropriate for all settings, including sexual content
- Make errors, including producing incorrect information as if it were factual
- Generate irrelevant or repetitive outputs
</details>
<p> </p>
## Evaluation
*This section describes the evaluation protocols and provides the results.*
<details>
<summary>Click to expand</summary><br/>
### Metrics
*This section describes the different ways performance is calculated and why.*
Includes:
| Metric | Why chosen |
|--------------------|--------------------------------------------------------------------|
| [Perplexity](#perplexity) | Standard metric for quantifying model improvements during training |
| Cross Entropy [Loss](#loss) | Standard objective for language models. |
And multiple different metrics for specific tasks. _(More evaluation metrics forthcoming upon completion of evaluation protocol.)_
### Factors
*This section lists some different aspects of BLOOM models. Its focus is on those aspects that are likely to give rise to high variance in model behavior.*
- Language, such as English or Yoruba
- Domain, such as newswire or stories
- Demographic characteristics, such as gender or nationality
### Results
*Results are based on the [Factors](#factors) and [Metrics](#metrics).*
**Train-time Evaluation:**
As of 25.May.2022, 15:00 PST:
- Training Loss: 2.3
- Validation Loss: 2.9
- Perplexity: 16
</details>
<p> </p>
## Recommendations
*This section provides information on warnings and potential mitigations.*
<details>
<summary>Click to expand</summary><br/>
- Indirect users should be made aware when the content they're working with is created by the LLM.
- Users should be aware of [Risks and Limitations](#risks-and-limitations), and include an appropriate age disclaimer or blocking interface as necessary.
- Models pretrained with the LLM should include an updated Model Card.
- Users of the model should provide mechanisms for those affected to provide feedback, such as an email address for comments.
</details>
<p> </p>
## Glossary and Calculations
*This section defines common terms and how metrics are calculated.*
<details>
<summary>Click to expand</summary><br/>
- <a name="loss">**Loss:**</a> A calculation of the difference between what the model has learned and what the data shows ("groundtruth"). The lower the loss, the better. The training process aims to minimize the loss.
- <a name="perplexity">**Perplexity:**</a> This is based on what the model estimates the probability of new data is. The lower the perplexity, the better. If the model is 100% correct at predicting the next token it will see, then the perplexity is 1. Mathematically this is calculated using entropy.
- <a name="high-stakes">**High-stakes settings:**</a> Such as those identified as "high-risk AI systems" and "unacceptable risk AI systems" in the European Union's proposed [Artificial Intelligence (AI) Act](https://artificialintelligenceact.eu/annexes/).
- <a name="critical-decisions">**Critical decisions:**</a> Such as those defined in [the United States' proposed Algorithmic Accountability Act](https://www.congress.gov/117/bills/s3572/BILLS-117s3572is.pdf).
- <a name="human-rights">**Human rights:**</a> Includes those rights defined in the [Universal Declaration of Human Rights](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf).
- <a name="personal-data-and-information">**Personal Data and Personal Information:**</a> Personal data and information is defined in multiple data protection regulations, such as "[personal data](https://gdpr-info.eu/issues/personal-data/)" in the [European Union's General Data Protection Regulation](https://gdpr-info.eu); and "personal information" in the Republic of South Africa's [Protection of Personal Information Act](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf), The People's Republic of China's [Personal information protection law](http://en.npc.gov.cn.cdurl.cn/2021-12/29/c_694559.htm).
- <a name="sensitive-characteristics">**Sensitive characteristics:**</a> This includes specifically protected categories in human rights (see [UHDR, Article 2](https://www.un.org/sites/un2.un.org/files/2021/03/udhr.pdf)) and personal information regulation (see GDPR, [Article 9; Protection of Personal Information Act, Chapter 1](https://www.gov.za/sites/default/files/gcis_document/201409/3706726-11act4of2013popi.pdf))
- <a name="deception">**Deception:**</a> Doing something to intentionally mislead individuals to believe something that is false, such as by creating deadbots or chatbots on social media posing as real people, or generating text documents without making consumers aware that the text is machine generated.
</details>
<p> </p>
## More Information
<details>
<summary>Click to expand</summary><br/>
### Dataset Creation
Blog post detailing the design choices during the dataset creation: https://bigscience.huggingface.co/blog/building-a-tb-scale-multilingual-dataset-for-language-modeling
### Technical Specifications
Blog post summarizing how the architecture, size, shape, and pre-training duration where selected: https://bigscience.huggingface.co/blog/what-language-model-to-train-if-you-have-two-million-gpu-hours
More details on the architecture/optimizer: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Blog post on the hardware/engineering side: https://bigscience.huggingface.co/blog/which-hardware-to-train-a-176b-parameters-model
Details on the distributed setup used for the training: https://github.com/bigscience-workshop/bigscience/tree/master/train/tr11-176B-ml
Tensorboard updated during the training: https://huggingface.co/bigscience/tr11-176B-ml-logs/tensorboard#scalars&tagFilter=loss
Insights on how to approach training, negative results: https://github.com/bigscience-workshop/bigscience/blob/master/train/lessons-learned.md
Details on the obstacles overcome during the preparation on the engineering side (instabilities, optimization of training throughput, so many technical tricks and questions): https://github.com/bigscience-workshop/bigscience/blob/master/train/tr11-176B-ml/chronicles.md
### Initial Results
Initial prompting experiments using interim checkpoints: https://huggingface.co/spaces/bigscience/bloom-book
</details>
<p> </p>
## Model Card Authors
*Ordered roughly chronologically and by amount of time spent.*
Margaret Mitchell, Giada Pistilli, Yacine Jernite, Ezinwanne Ozoani, Marissa Gerchick, Nazneen Rajani, Sasha Luccioni, Irene Solaiman, Maraim Masoud, Somaieh Nikpoor, Carlos Muñoz Ferrandis, Stas Bekman, Christopher Akiki, Danish Contractor, David Lansky, Angelina McMillan-Major, Tristan Thrush, Suzana Ilić, Gérard Dupont, Shayne Longpre, Manan Dey, Stella Biderman, Douwe Kiela, Emi Baylor, Teven Le Scao, Aaron Gokaslan, Julien Launay, Niklas Muennighoff
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thanhnew2001/falcon7b | 2023-10-23T08:07:43.000Z | [
"transformers",
"en",
"dataset:tiiuae/falcon-refinedweb",
"arxiv:2205.14135",
"arxiv:1911.02150",
"arxiv:2101.00027",
"arxiv:2005.14165",
"arxiv:2104.09864",
"arxiv:2306.01116",
"license:apache-2.0",
"region:us"
] | null | thanhnew2001 | null | null | thanhnew2001/falcon7b | 0 | 2 | transformers | 2023-10-23T07:48:28 | ---
datasets:
- tiiuae/falcon-refinedweb
language:
- en
inference: false
license: apache-2.0
---
# 🚀 Falcon-7B
**Falcon-7B is a 7B parameters causal decoder-only model built by [TII](https://www.tii.ae) and trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. It is made available under the Apache 2.0 license.**
*Paper coming soon* 😊.
🤗 To get started with Falcon (inference, finetuning, quantization, etc.), we recommend reading [this great blogpost fron HF](https://huggingface.co/blog/falcon)!
## Why use Falcon-7B?
* **It outperforms comparable open-source models** (e.g., [MPT-7B](https://huggingface.co/mosaicml/mpt-7b), [StableLM](https://github.com/Stability-AI/StableLM), [RedPajama](https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-7B-v0.1) etc.), thanks to being trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora. See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard).
* **It features an architecture optimized for inference**, with FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135)) and multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)).
* **It is made available under a permissive Apache 2.0 license allowing for commercial use**, without any royalties or restrictions.
⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.** If you are looking for a version better suited to taking generic instructions in a chat format, we recommend taking a look at [Falcon-7B-Instruct](https://huggingface.co/tiiuae/falcon-7b-instruct).
🔥 **Looking for an even more powerful model?** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) is Falcon-7B's big brother!
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
💥 **Falcon LLMs require PyTorch 2.0 for use with `transformers`!**
For fast inference with Falcon, check-out [Text Generation Inference](https://github.com/huggingface/text-generation-inference)! Read more in this [blogpost]((https://huggingface.co/blog/falcon).
You will need **at least 16GB of memory** to swiftly run inference with Falcon-7B.
# Model Card for Falcon-7B
## Model Details
### Model Description
- **Developed by:** [https://www.tii.ae](https://www.tii.ae);
- **Model type:** Causal decoder-only;
- **Language(s) (NLP):** English, German, Spanish, French (and limited capabilities in Italian, Portuguese, Polish, Dutch, Romanian, Czech, Swedish);
- **License:** Apache 2.0.
### Model Source
- **Paper:** *coming soon*.
## Uses
### Direct Use
Research on large language models; as a foundation for further specialization and finetuning for specific usecases (e.g., summarization, text generation, chatbot, etc.)
### Out-of-Scope Use
Production use without adequate assessment of risks and mitigation; any use cases which may be considered irresponsible or harmful.
## Bias, Risks, and Limitations
Falcon-7B is trained on English and French data only, and will not generalize appropriately to other languages. Furthermore, as it is trained on a large-scale corpora representative of the web, it will carry the stereotypes and biases commonly encountered online.
### Recommendations
We recommend users of Falcon-7B to consider finetuning it for the specific set of tasks of interest, and for guardrails and appropriate precautions to be taken for any production use.
## How to Get Started with the Model
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import transformers
import torch
model = "tiiuae/falcon-7b"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
torch_dtype=torch.bfloat16,
trust_remote_code=True,
device_map="auto",
)
sequences = pipeline(
"Girafatron is obsessed with giraffes, the most glorious animal on the face of this Earth. Giraftron believes all other animals are irrelevant when compared to the glorious majesty of the giraffe.\nDaniel: Hello, Girafatron!\nGirafatron:",
max_length=200,
do_sample=True,
top_k=10,
num_return_sequences=1,
eos_token_id=tokenizer.eos_token_id,
)
for seq in sequences:
print(f"Result: {seq['generated_text']}")
```
## Training Details
### Training Data
Falcon-7B was trained on 1,500B tokens of [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb), a high-quality filtered and deduplicated web dataset which we enhanced with curated corpora. Significant components from our curated copora were inspired by The Pile ([Gao et al., 2020](https://arxiv.org/abs/2101.00027)).
| **Data source** | **Fraction** | **Tokens** | **Sources** |
|--------------------|--------------|------------|-----------------------------------|
| [RefinedWeb-English](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) | 79% | 1,185B | massive web crawl |
| Books | 7% | 110B | |
| Conversations | 6% | 85B | Reddit, StackOverflow, HackerNews |
| Code | 3% | 45B | |
| RefinedWeb-French | 3% | 45B | massive web crawl |
| Technical | 2% | 30B | arXiv, PubMed, USPTO, etc. |
The data was tokenized with the Falcon-[7B](https://huggingface.co/tiiuae/falcon-7b)/[40B](https://huggingface.co/tiiuae/falcon-40b) tokenizer.
### Training Procedure
Falcon-7B was trained on 384 A100 40GB GPUs, using a 2D parallelism strategy (PP=2, DP=192) combined with ZeRO.
#### Training Hyperparameters
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|------------|-------------------------------------------|
| Precision | `bfloat16` | |
| Optimizer | AdamW | |
| Learning rate | 6e-4 | 4B tokens warm-up, cosine decay to 1.2e-5 |
| Weight decay | 1e-1 | |
| Z-loss | 1e-4 | |
| Batch size | 2304 | 30B tokens ramp-up |
#### Speeds, Sizes, Times
Training happened in early March 2023 and took about two weeks.
## Evaluation
*Paper coming soon*.
See the [OpenLLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) for early results.
## Technical Specifications
### Model Architecture and Objective
Falcon-7B is a causal decoder-only model trained on a causal language modeling task (i.e., predict the next token).
The architecture is broadly adapted from the GPT-3 paper ([Brown et al., 2020](https://arxiv.org/abs/2005.14165)), with the following differences:
* **Positionnal embeddings:** rotary ([Su et al., 2021](https://arxiv.org/abs/2104.09864));
* **Attention:** multiquery ([Shazeer et al., 2019](https://arxiv.org/abs/1911.02150)) and FlashAttention ([Dao et al., 2022](https://arxiv.org/abs/2205.14135));
* **Decoder-block:** parallel attention/MLP with a single layer norm.
| **Hyperparameter** | **Value** | **Comment** |
|--------------------|-----------|----------------------------------------|
| Layers | 32 | |
| `d_model` | 4544 | Increased to compensate for multiquery |
| `head_dim` | 64 | Reduced to optimise for FlashAttention |
| Vocabulary | 65024 | |
| Sequence length | 2048 | |
### Compute Infrastructure
#### Hardware
Falcon-7B was trained on AWS SageMaker, on 384 A100 40GB GPUs in P4d instances.
#### Software
Falcon-7B was trained a custom distributed training codebase, Gigatron. It uses a 3D parallelism approach combined with ZeRO and high-performance Triton kernels (FlashAttention, etc.)
## Citation
*Paper coming soon* 😊. In the meanwhile, you can use the following information to cite:
```
@article{falcon40b,
title={{Falcon-40B}: an open large language model with state-of-the-art performance},
author={Almazrouei, Ebtesam and Alobeidli, Hamza and Alshamsi, Abdulaziz and Cappelli, Alessandro and Cojocaru, Ruxandra and Debbah, Merouane and Goffinet, Etienne and Heslow, Daniel and Launay, Julien and Malartic, Quentin and Noune, Badreddine and Pannier, Baptiste and Penedo, Guilherme},
year={2023}
}
```
To learn more about the pretraining dataset, see the 📓 [RefinedWeb paper](https://arxiv.org/abs/2306.01116).
```
@article{refinedweb,
title={The {R}efined{W}eb dataset for {F}alcon {LLM}: outperforming curated corpora with web data, and web data only},
author={Guilherme Penedo and Quentin Malartic and Daniel Hesslow and Ruxandra Cojocaru and Alessandro Cappelli and Hamza Alobeidli and Baptiste Pannier and Ebtesam Almazrouei and Julien Launay},
journal={arXiv preprint arXiv:2306.01116},
eprint={2306.01116},
eprinttype = {arXiv},
url={https://arxiv.org/abs/2306.01116},
year={2023}
}
```
## License
Falcon-7B is made available under the Apache 2.0 license.
## Contact
falconllm@tii.ae | 10,292 | [
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mateiaass/albert-base-qa-2-k-fold-1 | 2023-10-23T14:24:07.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-2-k-fold-1 | 0 | 2 | transformers | 2023-10-23T09:37:55 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-qa-2-k-fold-1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-2-k-fold-1
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9306
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8688 | 1.0 | 4602 | 0.8533 |
| 0.6826 | 2.0 | 9204 | 0.8624 |
| 0.4738 | 3.0 | 13806 | 0.9306 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.5
- Tokenizers 0.14.1
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queuemin/bert-finetuned-squad | 2023-10-24T01:11:46.000Z | [
"transformers",
"pytorch",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | queuemin | null | null | queuemin/bert-finetuned-squad | 0 | 2 | transformers | 2023-10-23T10:02:41 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the squad 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: 3
### Training results
### Framework versions
- Transformers 4.32.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.2
- Tokenizers 0.13.3
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] |
sainteye/ifoodie-rating-reset-v7 | 2023-10-23T12:05:26.000Z | [
"transformers",
"pytorch",
"tensorboard",
"swin",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | sainteye | null | null | sainteye/ifoodie-rating-reset-v7 | 0 | 2 | transformers | 2023-10-23T12:05:22 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: ifoodie-rating-reset-v7
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9685534834861755
---
# ifoodie-rating-reset-v7
['優質', '差', '普通']
## Example Images
# #### 優質
# 
#
# #### 差
# 
#
# #### 普通
# 
# | 468 | [
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Pollathorn/food_classifier | 2023-10-23T13:26:51.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | Pollathorn | null | null | Pollathorn/food_classifier | 0 | 2 | transformers | 2023-10-23T13:07:30 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: Pollathorn/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Pollathorn/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9782
- Validation Loss: 1.2511
- Train Accuracy: 0.849
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9782 | 1.2511 | 0.849 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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mimunto/food_classifier | 2023-10-23T13:26:59.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | mimunto | null | null | mimunto/food_classifier | 0 | 2 | transformers | 2023-10-23T13:17:16 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: mimunto/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mimunto/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 1.9400
- Validation Loss: 1.2381
- Train Accuracy: 0.86
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 1.9400 | 1.2381 | 0.86 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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aikidoaikido115/food_classifier | 2023-10-23T13:26:54.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | aikidoaikido115 | null | null | aikidoaikido115/food_classifier | 0 | 2 | transformers | 2023-10-23T13:19:23 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: aikidoaikido115/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# aikidoaikido115/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.7880
- Validation Loss: 1.6485
- Train Accuracy: 0.826
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7880 | 1.6485 | 0.826 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.13.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
nlplabtdtu/distil-sbert-base-uncased | 2023-10-23T13:22:33.000Z | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | nlplabtdtu | null | null | nlplabtdtu/distil-sbert-base-uncased | 0 | 2 | sentence-transformers | 2023-10-23T13:21:31 | ---
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 768 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 14004 with parameters:
```
{'batch_size': 32, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 1500,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 500,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,882 | [
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koobear/wbc-50-no-pretrain-20-epoch | 2023-10-23T14:12:17.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-50-no-pretrain-20-epoch | 0 | 2 | transformers | 2023-10-23T14:12:03 | ---
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-50-no-pretrain-20-epoch
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9241898148148148
- name: F1
type: f1
value: 0.9260816032912371
- name: Precision
type: precision
value: 0.9300483556500446
- name: Recall
type: recall
value: 0.9241898148148148
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-50-no-pretrain-20-epoch
This model is a fine-tuned version of [](https://huggingface.co/) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2332
- Accuracy: 0.9242
- F1: 0.9261
- Precision: 0.9300
- Recall: 0.9242
- Balanced Acc: 0.9151
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.4968 | 1.0 | 33 | 1.4491 | 0.1944 | 0.1423 | 0.5006 | 0.1944 | 0.4518 |
| 1.1574 | 2.0 | 66 | 1.3673 | 0.1412 | 0.1404 | 0.5307 | 0.1412 | 0.4260 |
| 1.033 | 3.0 | 99 | 0.9817 | 0.5463 | 0.5675 | 0.6190 | 0.5463 | 0.5766 |
| 0.9143 | 4.0 | 132 | 0.9104 | 0.5804 | 0.5876 | 0.6191 | 0.5804 | 0.5774 |
| 0.8368 | 5.0 | 165 | 0.8826 | 0.6296 | 0.6329 | 0.6712 | 0.6296 | 0.6500 |
| 0.7829 | 6.0 | 198 | 0.7040 | 0.6939 | 0.6594 | 0.7159 | 0.6939 | 0.6571 |
| 0.7056 | 7.0 | 231 | 0.6170 | 0.7859 | 0.7851 | 0.7890 | 0.7859 | 0.7280 |
| 0.6557 | 8.0 | 264 | 0.6008 | 0.7882 | 0.7983 | 0.8202 | 0.7882 | 0.7753 |
| 0.5582 | 9.0 | 297 | 0.5804 | 0.7911 | 0.8025 | 0.8314 | 0.7911 | 0.7998 |
| 0.4719 | 10.0 | 330 | 0.5979 | 0.7737 | 0.7951 | 0.8400 | 0.7737 | 0.7876 |
| 0.4114 | 11.0 | 363 | 0.3667 | 0.8611 | 0.8680 | 0.8892 | 0.8611 | 0.8331 |
| 0.3405 | 12.0 | 396 | 0.3542 | 0.8692 | 0.8756 | 0.8903 | 0.8692 | 0.8530 |
| 0.2789 | 13.0 | 429 | 0.5196 | 0.8027 | 0.8138 | 0.8585 | 0.8027 | 0.8586 |
| 0.2626 | 14.0 | 462 | 0.2900 | 0.9034 | 0.9068 | 0.9140 | 0.9034 | 0.8909 |
| 0.2267 | 15.0 | 495 | 0.3343 | 0.8686 | 0.8768 | 0.8966 | 0.8686 | 0.8881 |
| 0.2126 | 16.0 | 528 | 0.2933 | 0.8929 | 0.8986 | 0.9117 | 0.8929 | 0.8908 |
| 0.1987 | 17.0 | 561 | 0.2587 | 0.9190 | 0.9206 | 0.9236 | 0.9190 | 0.8970 |
| 0.1617 | 18.0 | 594 | 0.2382 | 0.9190 | 0.9215 | 0.9269 | 0.9190 | 0.9127 |
| 0.1433 | 19.0 | 627 | 0.2195 | 0.9248 | 0.9265 | 0.9305 | 0.9248 | 0.9068 |
| 0.1294 | 20.0 | 660 | 0.2332 | 0.9242 | 0.9261 | 0.9300 | 0.9242 | 0.9151 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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quastrinos/TF-40k-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2 | 2023-10-23T15:49:28.000Z | [
"transformers",
"tf",
"deberta-v2",
"multiple-choice",
"generated_from_keras_callback",
"license:mit",
"endpoints_compatible",
"region:us"
] | multiple-choice | quastrinos | null | null | quastrinos/TF-40k-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2 | 0 | 2 | transformers | 2023-10-23T15:42:05 | ---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_keras_callback
model-index:
- name: TF-40k-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# TF-40k-openbook-finetuned-deberta-v3-large-mcqa-TPU-v2
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6671
- Validation Loss: 0.9061
- Train Map@3: 0.8095
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': 0.01, 'clipnorm': 1, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'class_name': 'CosineDecay', 'config': {'initial_learning_rate': 2e-06, 'decay_steps': 2826, 'alpha': 5e-09, 'name': None}}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False}
- training_precision: mixed_bfloat16
### Training results
| Train Loss | Validation Loss | Train Map@3 | Epoch |
|:----------:|:---------------:|:-----------:|:-----:|
| 0.9561 | 0.8899 | 0.8073 | 0 |
| 0.7125 | 0.8513 | 0.8244 | 1 |
| 0.6671 | 0.9061 | 0.8095 | 2 |
### Framework versions
- Transformers 4.35.0.dev0
- TensorFlow 2.12.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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] |
milaO/classification | 2023-10-23T15:50:27.000Z | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | milaO | null | null | milaO/classification | 0 | 2 | transformers | 2023-10-23T15:50:20 | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: classification
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.9253731369972229
---
# classification
Autogenerated by Mila Omrani🤗🖼️
Report any issues with the demo at the [github repo](https://github.com/milaomrani/WebHug).
## Example Images
#### Cat
#### Fire
#### Person
 | 578 | [
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FranzderPapst/ZGBot | 2023-10-23T17:06:41.000Z | [
"transformers",
"pytorch",
"distilbert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | FranzderPapst | null | null | FranzderPapst/ZGBot | 0 | 2 | transformers | 2023-10-23T16:01:00 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: ZGBot
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. -->
# ZGBot
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: 3e-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
- training_steps: 3000
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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Mahmoud8/distilbert-base-uncased-Nv | 2023-10-23T16:24:51.000Z | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-classification | Mahmoud8 | null | null | Mahmoud8/distilbert-base-uncased-Nv | 0 | 2 | transformers | 2023-10-23T16:12:22 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-Nv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-Nv
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2888
- Accuracy: 0.9659
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2177 | 1.0 | 3492 | 0.1981 | 0.9573 |
| 0.1254 | 2.0 | 6984 | 0.1780 | 0.9662 |
| 0.0688 | 3.0 | 10476 | 0.2014 | 0.9696 |
| 0.0293 | 4.0 | 13968 | 0.2224 | 0.9674 |
| 0.0073 | 5.0 | 17460 | 0.2888 | 0.9659 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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koobear/wbc-1-no-pretrain-20-epoch | 2023-10-23T16:31:42.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-1-no-pretrain-20-epoch | 0 | 2 | transformers | 2023-10-23T16:31:27 | ---
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-1-no-pretrain-20-epoch
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.25289351851851855
- name: F1
type: f1
value: 0.3026093032070324
- name: Precision
type: precision
value: 0.46793981832473747
- name: Recall
type: recall
value: 0.25289351851851855
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-1-no-pretrain-20-epoch
This model is a fine-tuned version of [](https://huggingface.co/) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5206
- Accuracy: 0.2529
- F1: 0.3026
- Precision: 0.4679
- Recall: 0.2529
- Balanced Acc: 0.3164
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.8139 | 1.0 | 1 | 2.3212 | 0.2390 | 0.0941 | 0.6697 | 0.2390 | 0.1999 |
| 2.599 | 2.0 | 2 | 1.8564 | 0.0758 | 0.0157 | 0.4431 | 0.0758 | 0.2009 |
| 2.1237 | 3.0 | 3 | 1.4615 | 0.4201 | 0.3957 | 0.4963 | 0.4201 | 0.1818 |
| 1.6982 | 4.0 | 4 | 1.4610 | 0.1916 | 0.2228 | 0.3845 | 0.1916 | 0.2388 |
| 1.3638 | 5.0 | 5 | 1.6983 | 0.1233 | 0.1453 | 0.3596 | 0.1233 | 0.2732 |
| 1.3708 | 6.0 | 6 | 1.9058 | 0.0706 | 0.0829 | 0.3547 | 0.0706 | 0.2399 |
| 1.4827 | 7.0 | 7 | 1.8382 | 0.0978 | 0.1119 | 0.4003 | 0.0978 | 0.2636 |
| 1.4045 | 8.0 | 8 | 1.6737 | 0.1597 | 0.1662 | 0.4549 | 0.1597 | 0.3207 |
| 1.266 | 9.0 | 9 | 1.5555 | 0.2020 | 0.2343 | 0.4641 | 0.2020 | 0.3286 |
| 1.1897 | 10.0 | 10 | 1.4769 | 0.2847 | 0.3255 | 0.4437 | 0.2847 | 0.2450 |
| 1.1834 | 11.0 | 11 | 1.4604 | 0.3073 | 0.3425 | 0.4739 | 0.3073 | 0.2310 |
| 1.1725 | 12.0 | 12 | 1.4715 | 0.3073 | 0.3469 | 0.4774 | 0.3073 | 0.2475 |
| 1.1325 | 13.0 | 13 | 1.5005 | 0.2616 | 0.3081 | 0.4430 | 0.2616 | 0.2695 |
| 1.087 | 14.0 | 14 | 1.5360 | 0.2176 | 0.2553 | 0.4695 | 0.2176 | 0.3180 |
| 1.0599 | 15.0 | 15 | 1.5429 | 0.2066 | 0.2298 | 0.4766 | 0.2066 | 0.3217 |
| 1.0476 | 16.0 | 16 | 1.5366 | 0.2002 | 0.2256 | 0.4671 | 0.2002 | 0.3151 |
| 1.0375 | 17.0 | 17 | 1.5279 | 0.2153 | 0.2502 | 0.4666 | 0.2153 | 0.3278 |
| 1.026 | 18.0 | 18 | 1.5222 | 0.2315 | 0.2762 | 0.4551 | 0.2315 | 0.3192 |
| 1.0153 | 19.0 | 19 | 1.5205 | 0.2477 | 0.2966 | 0.4627 | 0.2477 | 0.3187 |
| 1.0075 | 20.0 | 20 | 1.5206 | 0.2529 | 0.3026 | 0.4679 | 0.2529 | 0.3164 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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] |
koobear/wbc-10-no-pretrain-20-epoch | 2023-10-23T16:39:48.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-10-no-pretrain-20-epoch | 0 | 2 | transformers | 2023-10-23T16:39:32 | ---
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-10-no-pretrain-20-epoch
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.5341435185185185
- name: F1
type: f1
value: 0.5593691220340565
- name: Precision
type: precision
value: 0.6149615540522702
- name: Recall
type: recall
value: 0.5341435185185185
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-10-no-pretrain-20-epoch
This model is a fine-tuned version of [](https://huggingface.co/) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0394
- Accuracy: 0.5341
- F1: 0.5594
- Precision: 0.6150
- Recall: 0.5341
- Balanced Acc: 0.5953
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.9691 | 1.0 | 7 | 1.7584 | 0.0602 | 0.0728 | 0.4161 | 0.0602 | 0.2178 |
| 1.56 | 2.0 | 14 | 1.8865 | 0.0897 | 0.0247 | 0.0939 | 0.0897 | 0.3678 |
| 1.33 | 3.0 | 21 | 1.3672 | 0.2963 | 0.2522 | 0.5156 | 0.2963 | 0.4141 |
| 1.2723 | 4.0 | 28 | 1.4817 | 0.2228 | 0.2416 | 0.4965 | 0.2228 | 0.4325 |
| 1.1859 | 5.0 | 35 | 1.1677 | 0.5718 | 0.5096 | 0.5195 | 0.5718 | 0.4399 |
| 1.1096 | 6.0 | 42 | 1.5286 | 0.1962 | 0.1350 | 0.5681 | 0.1962 | 0.4552 |
| 1.0883 | 7.0 | 49 | 1.1433 | 0.5023 | 0.4934 | 0.4923 | 0.5023 | 0.4510 |
| 1.0607 | 8.0 | 56 | 1.4241 | 0.1568 | 0.1094 | 0.5564 | 0.1568 | 0.4480 |
| 1.1037 | 9.0 | 63 | 1.1371 | 0.5145 | 0.5089 | 0.5204 | 0.5145 | 0.4713 |
| 1.0329 | 10.0 | 70 | 1.3295 | 0.2593 | 0.2175 | 0.5928 | 0.2593 | 0.4888 |
| 0.9997 | 11.0 | 77 | 1.1507 | 0.4797 | 0.5002 | 0.5376 | 0.4797 | 0.5066 |
| 0.9617 | 12.0 | 84 | 1.2754 | 0.3368 | 0.3414 | 0.5459 | 0.3368 | 0.4881 |
| 0.9426 | 13.0 | 91 | 1.1981 | 0.4028 | 0.4538 | 0.5986 | 0.4028 | 0.5212 |
| 0.8711 | 14.0 | 98 | 1.2266 | 0.3900 | 0.4039 | 0.6016 | 0.3900 | 0.5217 |
| 0.8657 | 15.0 | 105 | 1.1702 | 0.4323 | 0.4733 | 0.5906 | 0.4323 | 0.5490 |
| 0.8563 | 16.0 | 112 | 1.0296 | 0.5463 | 0.5644 | 0.5987 | 0.5463 | 0.5711 |
| 0.8169 | 17.0 | 119 | 1.1235 | 0.4635 | 0.4948 | 0.6174 | 0.4635 | 0.5841 |
| 0.8108 | 18.0 | 126 | 1.0102 | 0.5527 | 0.5731 | 0.6144 | 0.5527 | 0.5840 |
| 0.7864 | 19.0 | 133 | 1.1249 | 0.4797 | 0.5137 | 0.6134 | 0.4797 | 0.5878 |
| 0.7608 | 20.0 | 140 | 1.0394 | 0.5341 | 0.5594 | 0.6150 | 0.5341 | 0.5953 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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lucas-meyer/xls-r-asr_af-run3 | 2023-10-23T19:34:57.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | lucas-meyer | null | null | lucas-meyer/xls-r-asr_af-run3 | 0 | 2 | transformers | 2023-10-23T17:19:57 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-asr_af-run3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-xls-r-300m-asr_af-run3
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5217
- Wer: 0.4069
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log | 1.1 | 250 | 2.9664 | 1.0 |
| 3.6929 | 2.2 | 500 | 1.1311 | 0.8448 |
| 3.6929 | 3.3 | 750 | 0.6159 | 0.5333 |
| 0.5976 | 4.41 | 1000 | 0.5717 | 0.4752 |
| 0.5976 | 5.51 | 1250 | 0.4962 | 0.4306 |
| 0.2559 | 6.61 | 1500 | 0.5146 | 0.4137 |
| 0.2559 | 7.71 | 1750 | 0.5407 | 0.4123 |
| 0.1545 | 8.81 | 2000 | 0.5217 | 0.4069 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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jovanlopez32/vit_model | 2023-10-23T20:19:20.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:beans",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | jovanlopez32 | null | null | jovanlopez32/vit_model | 0 | 2 | transformers | 2023-10-23T19:06:48 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- beans
metrics:
- accuracy
model-index:
- name: vit_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9924812030075187
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# vit_model
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 beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0261
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.1441 | 3.85 | 500 | 0.0261 | 0.9925 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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viditnaik/SecBERT-finetuned-imdb | 2023-10-23T19:55:26.000Z | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | viditnaik | null | null | viditnaik/SecBERT-finetuned-imdb | 0 | 2 | transformers | 2023-10-23T19:50:40 | ---
license: apache-2.0
base_model: jackaduma/SecBERT
tags:
- generated_from_keras_callback
model-index:
- name: viditnaik/SecBERT-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# viditnaik/SecBERT-finetuned-imdb
This model is a fine-tuned version of [jackaduma/SecBERT](https://huggingface.co/jackaduma/SecBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 6.5351
- Validation Loss: 5.2817
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 6.5351 | 5.2817 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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viditnaik/bert-base-uncased-finetuned-imdb | 2023-10-23T20:27:07.000Z | [
"transformers",
"tf",
"bert",
"fill-mask",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | viditnaik | null | null | viditnaik/bert-base-uncased-finetuned-imdb | 0 | 2 | transformers | 2023-10-23T20:20:38 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_keras_callback
model-index:
- name: viditnaik/bert-base-uncased-finetuned-imdb
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# viditnaik/bert-base-uncased-finetuned-imdb
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.6501
- Validation Loss: 2.3464
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'transformers.optimization_tf', 'class_name': 'WarmUp', 'config': {'initial_learning_rate': 2e-05, 'decay_schedule_fn': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': -688, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'warmup_steps': 1000, 'power': 1.0, 'name': None}, 'registered_name': 'WarmUp'}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: mixed_float16
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 2.6501 | 2.3464 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| 1,816 | [
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] |
toobiza/MT-swept-armadillo-86 | 2023-10-23T23:16:21.000Z | [
"transformers",
"pytorch",
"table-transformer",
"object-detection",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | object-detection | toobiza | null | null | toobiza/MT-swept-armadillo-86 | 0 | 2 | transformers | 2023-10-23T22:53:36 | ---
base_model: toobiza/table-transformer-stellar-vortex-81
tags:
- generated_from_trainer
model-index:
- name: MT-swept-armadillo-86
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. -->
# MT-swept-armadillo-86
This model is a fine-tuned version of [toobiza/table-transformer-stellar-vortex-81](https://huggingface.co/toobiza/table-transformer-stellar-vortex-81) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.2387
- eval_loss_ce: 0.0000
- eval_loss_bbox: 0.0330
- eval_cardinality_error: 1.0
- eval_giou: 96.3021
- eval_runtime: 102.3581
- eval_samples_per_second: 2.618
- eval_steps_per_second: 0.655
- epoch: 0.27
- step: 110
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.33.2
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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asmallgreenpotato/falcon-7b-ft-miia2-adapters | 2023-10-23T23:08:21.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | asmallgreenpotato | null | null | asmallgreenpotato/falcon-7b-ft-miia2-adapters | 0 | 2 | peft | 2023-10-23T23:07:45 | ---
library_name: peft
base_model: tiiuae/falcon-7b
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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#### Training Hyperparameters
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
## 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 0.6.0.dev0
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Technonia/mistral-instruct-7b-cot-neftune | 2023-10-24T02:18:32.000Z | [
"peft",
"region:us"
] | null | Technonia | null | null | Technonia/mistral-instruct-7b-cot-neftune | 0 | 2 | peft | 2023-10-24T02:13:52 | ---
library_name: peft
---
## Overview
Finetuned the mistralai/Mistral-7B-Instruct-v0.1 model on kaist-ai/CoT-Collection dataset.
## 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: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.5.0 | 569 | [
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thrunlab/t5-base_cola_dense_epochs-1 | 2023-10-24T18:20:57.000Z | [
"transformers",
"pytorch",
"t5",
"text-classification",
"generated_from_trainer",
"dataset:glue",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-classification | thrunlab | null | null | thrunlab/t5-base_cola_dense_epochs-1 | 0 | 2 | transformers | 2023-10-24T02:51:49 | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
datasets:
- glue
metrics:
- accuracy
model-index:
- name: t5-base_cola_dense_epochs-1
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: glue
type: glue
config: cola
split: validation
args: cola
metrics:
- name: Accuracy
type: accuracy
value: 0.7976989453499521
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base_cola_dense_epochs-1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on the glue dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4850
- Accuracy: 0.7977
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 0
- 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
- lr_scheduler_warmup_steps: 20
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5604 | 0.37 | 50 | 0.5631 | 0.6913 |
| 0.4593 | 0.75 | 100 | 0.4787 | 0.7919 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.0.1+cu117
- Datasets 2.9.0
- Tokenizers 0.14.1
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] |
dvijay/llama-2-7b-chat-hf-guanaco-1k | 2023-10-24T07:12:25.000Z | [
"peft",
"region:us"
] | null | dvijay | null | null | dvijay/llama-2-7b-chat-hf-guanaco-1k | 0 | 2 | peft | 2023-10-24T04:54:44 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
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koobear/wbc-100-pretrain-20-epoch | 2023-10-24T07:16:09.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-100-pretrain-20-epoch | 0 | 2 | transformers | 2023-10-24T07:15:53 | ---
license: apache-2.0
base_model: koobear/masked-pretraining-20-epoch
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-100-pretrain-20-epoch
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9884259259259259
- name: F1
type: f1
value: 0.9884018822005058
- name: Precision
type: precision
value: 0.9884289797847587
- name: Recall
type: recall
value: 0.9884259259259259
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-100-pretrain-20-epoch
This model is a fine-tuned version of [koobear/masked-pretraining-20-epoch](https://huggingface.co/koobear/masked-pretraining-20-epoch) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0493
- Accuracy: 0.9884
- F1: 0.9884
- Precision: 0.9884
- Recall: 0.9884
- Balanced Acc: 0.9762
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 0.5865 | 1.0 | 132 | 0.2601 | 0.9606 | 0.9609 | 0.9634 | 0.9606 | 0.9431 |
| 0.2703 | 2.0 | 264 | 0.1383 | 0.9821 | 0.9821 | 0.9822 | 0.9821 | 0.9750 |
| 0.1952 | 3.0 | 396 | 0.1149 | 0.9809 | 0.9812 | 0.9818 | 0.9809 | 0.9811 |
| 0.18 | 4.0 | 528 | 0.0914 | 0.9873 | 0.9872 | 0.9873 | 0.9873 | 0.9771 |
| 0.1475 | 5.0 | 660 | 0.0819 | 0.9873 | 0.9874 | 0.9878 | 0.9873 | 0.9837 |
| 0.1556 | 6.0 | 792 | 0.0796 | 0.9855 | 0.9857 | 0.9861 | 0.9855 | 0.9830 |
| 0.1276 | 7.0 | 924 | 0.0746 | 0.9878 | 0.9878 | 0.9879 | 0.9878 | 0.9728 |
| 0.1205 | 8.0 | 1056 | 0.0689 | 0.9873 | 0.9872 | 0.9874 | 0.9873 | 0.9702 |
| 0.1196 | 9.0 | 1188 | 0.0680 | 0.9855 | 0.9856 | 0.9862 | 0.9855 | 0.9775 |
| 0.1111 | 10.0 | 1320 | 0.0605 | 0.9873 | 0.9873 | 0.9875 | 0.9873 | 0.9832 |
| 0.1052 | 11.0 | 1452 | 0.0522 | 0.9902 | 0.9902 | 0.9902 | 0.9902 | 0.9845 |
| 0.1015 | 12.0 | 1584 | 0.0545 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9809 |
| 0.1044 | 13.0 | 1716 | 0.0529 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9780 |
| 0.097 | 14.0 | 1848 | 0.0521 | 0.9878 | 0.9878 | 0.9879 | 0.9878 | 0.9708 |
| 0.0943 | 15.0 | 1980 | 0.0468 | 0.9902 | 0.9902 | 0.9905 | 0.9902 | 0.9875 |
| 0.0884 | 16.0 | 2112 | 0.0454 | 0.9919 | 0.9919 | 0.9920 | 0.9919 | 0.9889 |
| 0.0767 | 17.0 | 2244 | 0.0465 | 0.9896 | 0.9896 | 0.9896 | 0.9896 | 0.9816 |
| 0.0834 | 18.0 | 2376 | 0.0512 | 0.9896 | 0.9895 | 0.9896 | 0.9896 | 0.9731 |
| 0.0819 | 19.0 | 2508 | 0.0497 | 0.9890 | 0.9890 | 0.9890 | 0.9890 | 0.9767 |
| 0.0822 | 20.0 | 2640 | 0.0493 | 0.9884 | 0.9884 | 0.9884 | 0.9884 | 0.9762 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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koobear/wbc-50-pretrain-20-epochs | 2023-10-24T09:28:43.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-50-pretrain-20-epochs | 0 | 2 | transformers | 2023-10-24T07:23:21 | ---
license: apache-2.0
base_model: koobear/masked-50-pretraining-20-epoch
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-50-pretrain-20-epochs
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9826388888888888
- name: F1
type: f1
value: 0.9827070698401582
- name: Precision
type: precision
value: 0.9828705519548487
- name: Recall
type: recall
value: 0.9826388888888888
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-50-pretrain-20-epochs
This model is a fine-tuned version of [koobear/masked-50-pretraining-20-epoch](https://huggingface.co/koobear/masked-50-pretraining-20-epoch) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1141
- Accuracy: 0.9826
- F1: 0.9827
- Precision: 0.9829
- Recall: 0.9826
- Balanced Acc: 0.9737
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.2239 | 1.0 | 33 | 0.8645 | 0.9219 | 0.9268 | 0.9404 | 0.9219 | 0.9370 |
| 0.7434 | 2.0 | 66 | 0.5164 | 0.9549 | 0.9560 | 0.9589 | 0.9549 | 0.9614 |
| 0.4834 | 3.0 | 99 | 0.3333 | 0.9711 | 0.9713 | 0.9718 | 0.9711 | 0.9645 |
| 0.3492 | 4.0 | 132 | 0.2470 | 0.9797 | 0.9797 | 0.9797 | 0.9797 | 0.9683 |
| 0.292 | 5.0 | 165 | 0.2056 | 0.9792 | 0.9793 | 0.9795 | 0.9792 | 0.9758 |
| 0.2612 | 6.0 | 198 | 0.1960 | 0.9745 | 0.9746 | 0.9750 | 0.9745 | 0.9659 |
| 0.2424 | 7.0 | 231 | 0.1660 | 0.9792 | 0.9796 | 0.9809 | 0.9792 | 0.9801 |
| 0.2157 | 8.0 | 264 | 0.1528 | 0.9803 | 0.9805 | 0.9813 | 0.9803 | 0.9793 |
| 0.2087 | 9.0 | 297 | 0.1511 | 0.9774 | 0.9776 | 0.9781 | 0.9774 | 0.9763 |
| 0.1967 | 10.0 | 330 | 0.1359 | 0.9809 | 0.9810 | 0.9814 | 0.9809 | 0.9749 |
| 0.1851 | 11.0 | 363 | 0.1353 | 0.9821 | 0.9821 | 0.9821 | 0.9821 | 0.9743 |
| 0.1859 | 12.0 | 396 | 0.1317 | 0.9815 | 0.9815 | 0.9816 | 0.9815 | 0.9738 |
| 0.1679 | 13.0 | 429 | 0.1291 | 0.9815 | 0.9816 | 0.9820 | 0.9815 | 0.9769 |
| 0.1583 | 14.0 | 462 | 0.1195 | 0.9832 | 0.9833 | 0.9836 | 0.9832 | 0.9791 |
| 0.1429 | 15.0 | 495 | 0.1191 | 0.9815 | 0.9816 | 0.9819 | 0.9815 | 0.9749 |
| 0.1624 | 16.0 | 528 | 0.1177 | 0.9826 | 0.9828 | 0.9831 | 0.9826 | 0.9825 |
| 0.1515 | 17.0 | 561 | 0.1159 | 0.9821 | 0.9822 | 0.9824 | 0.9821 | 0.9781 |
| 0.1517 | 18.0 | 594 | 0.1158 | 0.9826 | 0.9827 | 0.9828 | 0.9826 | 0.9737 |
| 0.1389 | 19.0 | 627 | 0.1140 | 0.9832 | 0.9833 | 0.9835 | 0.9832 | 0.9758 |
| 0.1325 | 20.0 | 660 | 0.1141 | 0.9826 | 0.9827 | 0.9829 | 0.9826 | 0.9737 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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koobear/masked-10-pretraining-20-epoch | 2023-10-24T08:12:12.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/masked-10-pretraining-20-epoch | 0 | 2 | transformers | 2023-10-24T08:11:56 | ---
license: apache-2.0
base_model: koobear/cam16-no-train-mask-final-50-epochs
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: masked-10-pretraining-20-epoch
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.8571428571428571
- name: F1
type: f1
value: 0.888888888888889
- name: Precision
type: precision
value: 0.9285714285714286
- name: Recall
type: recall
value: 0.8571428571428571
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# masked-10-pretraining-20-epoch
This model is a fine-tuned version of [koobear/cam16-no-train-mask-final-50-epochs](https://huggingface.co/koobear/cam16-no-train-mask-final-50-epochs) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3976
- Accuracy: 0.8571
- F1: 0.8889
- Precision: 0.9286
- Recall: 0.8571
- Balanced Acc: 0.6
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.645 | 1.0 | 1 | 1.6272 | 0.1429 | 0.1905 | 0.2857 | 0.1429 | 0.0833 |
| 1.557 | 2.0 | 2 | 1.6087 | 0.2143 | 0.2637 | 0.3429 | 0.2143 | 0.125 |
| 1.4815 | 3.0 | 3 | 1.5879 | 0.2857 | 0.4048 | 0.7857 | 0.2857 | 0.1917 |
| 1.4203 | 4.0 | 4 | 1.5646 | 0.4286 | 0.5272 | 0.8333 | 0.4286 | 0.2750 |
| 1.3708 | 5.0 | 5 | 1.5429 | 0.5 | 0.5762 | 0.8469 | 0.5 | 0.3167 |
| 1.3266 | 6.0 | 6 | 1.5260 | 0.5714 | 0.6612 | 0.8469 | 0.5714 | 0.3833 |
| 1.2864 | 7.0 | 7 | 1.5133 | 0.5714 | 0.6612 | 0.8469 | 0.5714 | 0.3833 |
| 1.2511 | 8.0 | 8 | 1.5016 | 0.6429 | 0.7250 | 0.8469 | 0.6429 | 0.45 |
| 1.2195 | 9.0 | 9 | 1.4893 | 0.6429 | 0.7250 | 0.8469 | 0.6429 | 0.45 |
| 1.1908 | 10.0 | 10 | 1.4755 | 0.6429 | 0.7250 | 0.8469 | 0.6429 | 0.45 |
| 1.1644 | 11.0 | 11 | 1.4614 | 0.7143 | 0.7746 | 0.8469 | 0.7143 | 0.5167 |
| 1.1407 | 12.0 | 12 | 1.4481 | 0.7143 | 0.8073 | 0.9286 | 0.7143 | 0.5167 |
| 1.1199 | 13.0 | 13 | 1.4363 | 0.7143 | 0.8073 | 0.9286 | 0.7143 | 0.5167 |
| 1.1017 | 14.0 | 14 | 1.4266 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0858 | 15.0 | 15 | 1.4186 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0719 | 16.0 | 16 | 1.4119 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0602 | 17.0 | 17 | 1.4064 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0508 | 18.0 | 18 | 1.4022 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0437 | 19.0 | 19 | 1.3992 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
| 1.0388 | 20.0 | 20 | 1.3976 | 0.8571 | 0.8889 | 0.9286 | 0.8571 | 0.6 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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adorno/audiogen-medium | 2023-10-24T08:22:55.000Z | [
"arxiv:2209.15352",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"region:us"
] | null | adorno | null | null | adorno/audiogen-medium | 0 | 2 | null | 2023-10-24T08:22:55 | ---
license: cc-by-nc-4.0
---
# AudioGen - Medium - 1.5B
AudioGen is an autoregressive transformer LM that synthesizes general audio conditioned on text (Text-to-Audio).
Internally, AudioGen operates over discrete representations learnt from the raw waveform, using an EnCodec tokenizer.
AudioGen was presented at [AudioGen: Textually Guided Audio Generation](https://arxiv.org/abs/2209.15352) by *Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer, Alexandre Défossez, Jade Copet, Devi Parikh, Yaniv Taigman, Yossi Adi*.
AudioGen 1.5B is a variant of the original AudioGen model that follows [MusicGen](https://arxiv.org/abs/2306.05284) architecture.
More specifically, it is trained over a 16kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz with a delay pattern between the codebooks.
Having only 50 auto-regressive steps per second of audio, this AudioGen model allows faster generation while reaching similar performances to the original AudioGen model introduced in the paper.
## Audiocraft Usage
You can run AudioGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
```
pip install git+https://github.com/facebookresearch/audiocraft.git
```
2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed:
```
apt get install ffmpeg
```
3. Run the following Python code:
```py
import torchaudio
from audiocraft.models import AudioGen
from audiocraft.data.audio import audio_write
model = AudioGen.get_pretrained('facebook/audiogen-medium')
model.set_generation_params(duration=5) # generate 5 seconds.
descriptions = ['dog barking', 'sirenes of an emergency vehicule', 'footsteps in a corridor']
wav = model.generate(descriptions) # generates 3 samples.
for idx, one_wav in enumerate(wav):
# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
```
## Model details
See [AudioGen's model card](https://github.com/facebookresearch/audiocraft/blob/main/model_cards/AUDIOGEN_MODEL_CARD.md). | 2,209 | [
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lilpotato03/aarons_mental_health_chatbot_v1 | 2023-10-26T05:38:57.000Z | [
"peft",
"text-generation",
"arxiv:1910.09700",
"region:us"
] | text-generation | lilpotato03 | null | null | lilpotato03/aarons_mental_health_chatbot_v1 | 0 | 2 | peft | 2023-10-24T09:05:52 | ---
library_name: peft
base_model: TinyPixel/Llama-2-7B-bf16-sharded
pipeline_tag: text-generation
---
# Model Card for Model ID
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## Model Details
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.0.dev0 | 5,485 | [
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Kolibri753/falcon-7b-instruct-workouts-json | 2023-10-24T09:32:28.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Kolibri753 | null | null | Kolibri753/falcon-7b-instruct-workouts-json | 0 | 2 | peft | 2023-10-24T09:32:23 | ---
library_name: peft
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **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]
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## Evaluation
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## 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]
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.0.dev0
## 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 0.6.0.dev0
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54data/distilbert-base-uncased-finetuned-clinc | 2023-10-24T09:55:31.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:clinc_oos",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | 54data | null | null | 54data/distilbert-base-uncased-finetuned-clinc | 0 | 2 | transformers | 2023-10-24T09:49:24 | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- clinc_oos
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-clinc
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: clinc_oos
type: clinc_oos
config: plus
split: validation
args: plus
metrics:
- name: Accuracy
type: accuracy
value: 0.9164516129032259
---
<!-- This model card 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-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the clinc_oos dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7725
- Accuracy: 0.9165
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 4.2924 | 1.0 | 318 | 3.2763 | 0.7284 |
| 2.6141 | 2.0 | 636 | 1.8625 | 0.8365 |
| 1.5389 | 3.0 | 954 | 1.1513 | 0.8984 |
| 1.0087 | 4.0 | 1272 | 0.8540 | 0.9135 |
| 0.793 | 5.0 | 1590 | 0.7725 | 0.9165 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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matthieulel/my_awesome_food_model | 2023-10-24T14:21:42.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:food101",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | matthieulel | null | null | matthieulel/my_awesome_food_model | 0 | 2 | transformers | 2023-10-24T13:53:33 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
datasets:
- food101
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: food101
type: food101
config: default
split: train[:5000]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.922
---
<!-- This model card 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_food_model
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 food101 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4362
- Accuracy: 0.922
## Model description
More information needed
## Intended uses & 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2229 | 0.99 | 62 | 1.2187 | 0.896 |
| 0.7939 | 2.0 | 125 | 0.8669 | 0.899 |
| 0.6067 | 2.99 | 187 | 0.6797 | 0.909 |
| 0.4863 | 4.0 | 250 | 0.5495 | 0.919 |
| 0.4002 | 4.99 | 312 | 0.5279 | 0.904 |
| 0.3306 | 6.0 | 375 | 0.4693 | 0.912 |
| 0.3163 | 6.99 | 437 | 0.4206 | 0.929 |
| 0.3294 | 7.94 | 496 | 0.4362 | 0.922 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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trustyai/gminus | 2023-10-24T14:15:30.000Z | [
"transformers",
"pytorch",
"safetensors",
"bart",
"text2text-generation",
"en",
"dataset:jigsaw_toxicity_pred",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | trustyai | null | null | trustyai/gminus | 0 | 2 | transformers | 2023-10-24T14:15:30 | ---
license: apache-2.0
datasets:
- jigsaw_toxicity_pred
language:
- en
metrics:
- perplexity
---
# Model Card for `gminus`
This model is a `facebook/bart-large` fine-tuned on toxic comments from `jigsaw_toxicity_pred` dataset.
## Model Details
This model is not intended to be used for plain inference as it is very likely to predict toxic content.
It is intended to be used instead as "utility model" for detecting and fixing toxic content as its token probability distributions will likely differ from comparable models not trained/fine-tuned over toxic data.
Its name `gminus` refers to the _G-_ model in [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
### Model Description
- **Developed by:** [tteofili]
- **Shared by :** [tteofili]
<!--- **Model type:** [More Information Needed]-->
<!--- **Language(s) (NLP):** [More Information Needed]-->
- **License:** [apache-2.0]
- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
<!--
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## Uses
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### Direct Use
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[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 model is fine-tuned over toxic comments from `jigsaw_toxicity_pred` and it is very likely to produce toxic content.
For this reason this model should only be used in combination with other models for the sake of detecting / fixing toxic content, see for example [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
<!--
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.
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## Evaluation
This section describes the evaluation protocols and provides the results.
### Testing Data, Factors & Metrics
#### Testing Data
This model was tested on `jigsaw_toxic_pred` testset.
<!--
#### Factors
These are the things the evaluation is disaggregating by, e.g., subpopulations or domains.
[More Information Needed]
-->
#### Metrics
Model was evaluated using `perplexity` (on the MLM task).
### Results
Perplexity: _1.03_
<!--
#### 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.
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**APA:**
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## Glossary [optional]
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## Model Card Contact
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] |
trustyai/gplus | 2023-10-24T14:15:50.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"en",
"dataset:jigsaw_toxicity_pred",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | trustyai | null | null | trustyai/gplus | 0 | 2 | transformers | 2023-10-24T14:15:49 | ---
license: apache-2.0
datasets:
- jigsaw_toxicity_pred
language:
- en
metrics:
- perplexity
---
# Model Card for `gplus`
This model is a `facebook/bart-large` fine-tuned on non-toxic comments from `jigsaw_toxicity_pred` dataset.
Only a subset (20%) of the non-toxic comments were used for training this dataset.
## Model Details
This model is not intended to be used for plain inference, even though it is unlikely to predict toxic content.
It is intended to be used as "utility model" for detecting and fixing toxic content as its token probability distributions will likely differ from comparable models trained/fine-tuned over toxic data.
Its name `gplus` refers to the _G+_ model in [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
### Model Description
- **Developed by:** [tteofili]
- **Shared by :** [tteofili]
<!--- **Model type:** [More Information Needed]-->
<!--- **Language(s) (NLP):** [More Information Needed]-->
- **License:** [apache-2.0]
- **Finetuned from model :** [facebook/bart-large](https://huggingface.co/facebook/bart-large)
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## Uses
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### Direct Use
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[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]
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## Bias, Risks, and Limitations
This model is fine-tuned over non-toxic comments from `jigsaw_toxicity_pred`, it is unlikely to produce toxic content.
Nevertheless, this model should only be used in combination with other models for the sake of detecting / fixing toxic content, see for example [Detoxifying Text with MARCO: Controllable Revision with Experts and Anti-Experts](https://aclanthology.org/2023.acl-short.21.pdf).
<!--
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.
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## Evaluation
This section describes the evaluation protocols and provides the results.
### Testing Data, Factors & Metrics
#### Testing Data
This model was tested on `jigsaw_toxic_pred` testset.
<!--
#### Factors
These are the things the evaluation is disaggregating by, e.g., subpopulations or domains.
[More Information Needed]
-->
#### Metrics
Model was evaluated using `perplexity` (on the MLM task).
### Results
Perplexity: _1.02_
<!--
#### 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
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- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section.
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## Glossary [optional]
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lltala/bert-base-cased-ner | 2023-10-24T14:16:52.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | lltala | null | null | lltala/bert-base-cased-ner | 0 | 2 | transformers | 2023-10-24T14:16:33 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-cased-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0894
- Overall Precision: 0.5187
- Overall Recall: 0.5814
- Overall F1: 0.5483
- Org Precision: 0.5127
- Org Recall: 0.5277
- Org F1: 0.5201
- Per Precision: 0.7294
- Per Recall: 0.8052
- Per F1: 0.7654
- Loc Precision: 0.4329
- Loc Recall: 0.7474
- Loc F1: 0.5483
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Overall Precision | Overall Recall | Overall F1 | Org Precision | Org Recall | Org F1 | Per Precision | Per Recall | Per F1 | Loc Precision | Loc Recall | Loc F1 |
|:-------------:|:-----:|:----:|:---------------:|:-----------------:|:--------------:|:----------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|:-------------:|:----------:|:------:|
| No log | 1.0 | 53 | 0.1227 | 0.3066 | 0.3206 | 0.3134 | 0.3084 | 0.4104 | 0.3522 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| No log | 2.0 | 106 | 0.1055 | 0.3967 | 0.4224 | 0.4091 | 0.3829 | 0.3860 | 0.3844 | 0.6964 | 0.5065 | 0.5865 | 0.3457 | 0.5895 | 0.4358 |
| No log | 3.0 | 159 | 0.0897 | 0.4867 | 0.5598 | 0.5207 | 0.4883 | 0.5098 | 0.4988 | 0.7011 | 0.7922 | 0.7439 | 0.375 | 0.6947 | 0.4871 |
| No log | 4.0 | 212 | 0.0901 | 0.5179 | 0.5712 | 0.5433 | 0.5227 | 0.5261 | 0.5244 | 0.6988 | 0.7532 | 0.7250 | 0.4096 | 0.7158 | 0.5211 |
| No log | 5.0 | 265 | 0.0894 | 0.5187 | 0.5814 | 0.5483 | 0.5127 | 0.5277 | 0.5201 | 0.7294 | 0.8052 | 0.7654 | 0.4329 | 0.7474 | 0.5483 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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ahmadmooktaree/food_classifier | 2023-10-24T16:23:53.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | ahmadmooktaree | null | null | ahmadmooktaree/food_classifier | 0 | 2 | transformers | 2023-10-24T15:45:43 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: ahmadmooktaree/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# ahmadmooktaree/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 2.8192
- Validation Loss: 1.6728
- Train Accuracy: 0.825
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.8192 | 1.6728 | 0.825 | 0 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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bjacob/distilbert-base-uncased-finetuned-emotion | 2023-10-24T17:02:54.000Z | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | bjacob | null | null | bjacob/distilbert-base-uncased-finetuned-emotion | 0 | 2 | transformers | 2023-10-24T16:46:01 | ---
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: 0.921
- name: F1
type: f1
value: 0.9211869496144807
---
<!-- This model card 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: 0.2137
- Accuracy: 0.921
- F1: 0.9212
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7964 | 1.0 | 250 | 0.2979 | 0.911 | 0.9109 |
| 0.2406 | 2.0 | 500 | 0.2137 | 0.921 | 0.9212 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
ML4SE2023-G1-WizardCoder/ML4SE23_G1_WizardCoder-SCoT-350M-V1.0 | 2023-10-24T16:50:28.000Z | [
"transformers",
"pytorch",
"codegen",
"text-generation",
"code",
"en",
"dataset:ML4SE2023-G1-WizardCoder/EvolInstruct-SCoT-1k",
"endpoints_compatible",
"region:us"
] | text-generation | ML4SE2023-G1-WizardCoder | null | null | ML4SE2023-G1-WizardCoder/ML4SE23_G1_WizardCoder-SCoT-350M-V1.0 | 0 | 2 | transformers | 2023-10-24T16:46:40 | ---
datasets:
- ML4SE2023-G1-WizardCoder/EvolInstruct-SCoT-1k
language:
- en
tags:
- code
---
# WizardCoder 350M Version
Based on https://huggingface.co/Salesforce/codegen-350M-nl | 182 | [
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PratapB/mistral-7b-chat-hf-instruct-dbahn-assistant_v10 | 2023-10-24T20:48:06.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"de",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | PratapB | null | null | PratapB/mistral-7b-chat-hf-instruct-dbahn-assistant_v10 | 0 | 2 | transformers | 2023-10-24T16:58:12 | ---
license: apache-2.0
language:
- de
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a chat bot for Dbahn PratapB/mistral-7b-chat-hf-instruct-dbahn-assistant_v10 (3E)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** OTSI https://otsi-global.com/
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Mistral-7b
- **Language(s) (NLP):** German
- **License:** Meta
- **Finetuned from model [optional]:** flozi00/Mistral-7B-german-assistant-v3
### Model Sources [optional]
flozi00/Mistral-7B-german-assistant-v3
## Training Details:
num_train_epochs = 10,
max_steps = -1,
bf16 = False,
fp16 = True,
per_device_train_batch_size = 4,
per_device_eval_batch_size = 4,
gradient_accumulation_steps = 1,
max_grad_norm = 0.3,
optim = "paged_adamw_32bit",
learning_rate = 3e-5,
lr_scheduler_type = "constant",
warmup_ratio = 0.03,
weight_decay = 0.001,
group_by_length = False,
gradient_checkpointing = True,
save_steps = 100,
logging_steps = 100
## Notice
"### Instruction:" Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem digitaler KI-Assistenten von die "Deutsche Bahn AG" und einem Nutzer. Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten. "### User:" frage: Was ist BahnBohnus? "### Assistant:"
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] |
yujiepan/bloom-tiny-random | 2023-10-24T17:27:37.000Z | [
"transformers",
"pytorch",
"openvino",
"bloom",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | yujiepan | null | null | yujiepan/bloom-tiny-random | 0 | 2 | transformers | 2023-10-24T17:26:18 | ---
pipeline_tag: text-generation
inference: true
widget:
- text: 'Hello!'
example_title: Hello world
group: Python
library_name: transformers
---
This model is randomly initialized, using the config from [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) but with smaller size.
Note the model is in float16.
Codes:
```python
from huggingface_hub import create_repo, upload_folder
import torch
import transformers
import os
model_id = 'bigscience/bloom-560m'
save_path = '/tmp/yujiepan/bloom-tiny-random'
repo_id = 'yujiepan/bloom-tiny-random'
config = transformers.AutoConfig.from_pretrained(model_id)
config.hidden_size = 8
config.num_attention_heads = 2
config.n_head = 2
config.n_layer = 2
config.pretraining_tp = 1
print(config)
model = transformers.AutoModelForCausalLM.from_config(config, torch_dtype=torch.float16)
model.save_pretrained(save_path)
tokenizer = transformers.AutoTokenizer.from_pretrained(model_id)
tokenizer.save_pretrained(save_path)
from optimum.intel.openvino import OVModelForCausalLM
ovmodel = OVModelForCausalLM.from_pretrained(save_path, export=True)
ovmodel = ovmodel.half()
ovmodel.save_pretrained(save_path)
os.system(f'ls -alh {save_path}')
create_repo(repo_id, exist_ok=True)
upload_folder(repo_id=repo_id, folder_path=save_path)
``` | 1,301 | [
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skr1125/ddpm-celebahq-finetuned-butterflies-2epochs-2nd | 2023-10-24T18:03:17.000Z | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | skr1125 | null | null | skr1125/ddpm-celebahq-finetuned-butterflies-2epochs-2nd | 0 | 2 | diffusers | 2023-10-24T18:03:00 | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
pretrained celebahq model fine tuned on butterflies 2 epochs
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('skr1125/ddpm-celebahq-finetuned-butterflies-2epochs-2nd')
image = pipeline().images[0]
image
```
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abh1nav/wav2vec2-large-xls-r-300m-hindi | 2023-10-24T18:24:57.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | abh1nav | null | null | abh1nav/wav2vec2-large-xls-r-300m-hindi | 0 | 2 | transformers | 2023-10-24T18:09:51 | ---
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- common_voice
model-index:
- name: wav2vec2-large-xls-r-300m-hindi
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hindi
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the common_voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 1.18.3
- Tokenizers 0.14.1
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PratapB/llama-2-7b-chat-hf-instruct-dbahn-assistant_v10_3E | 2023-10-24T20:50:21.000Z | [
"transformers",
"pytorch",
"llama",
"text-generation",
"de",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | PratapB | null | null | PratapB/llama-2-7b-chat-hf-instruct-dbahn-assistant_v10_3E | 0 | 2 | transformers | 2023-10-24T19:31:49 | ---
license: apache-2.0
language:
- de
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a chat bot for Dbahn PratapB/mistral-7b-chat-hf-instruct-dbahn-assistant_v10 (3E)
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** OTSI https://otsi-global.com/
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Mistral-7b
- **Language(s) (NLP):** German
- **License:** Meta
- **Finetuned from model [optional]:** flozi00/Mistral-7B-german-assistant-v3
### Model Sources [optional]
### Training Details:
num_train_epochs = 10,
max_steps = -1,
bf16 = False,
fp16 = True,
per_device_train_batch_size = 4,
per_device_eval_batch_size = 4,
gradient_accumulation_steps = 1,
max_grad_norm = 0.3,
optim = "paged_adamw_32bit",
learning_rate = 3e-5,
lr_scheduler_type = "constant",
warmup_ratio = 0.03,
weight_decay = 0.001,
group_by_length = False,
gradient_checkpointing = True,
save_steps = 100,
logging_steps = 100
### Notice
"### User:" Als ein intelligenter, hilfreicher digitaler KI-Assistent der "Deutsche Bahn AG", antworten Sie auf diese Benutzeranfrage: Kann ich mein Fahrrad kostenfrei mitnehmen? "### Assistant:" | 1,336 | [
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Mike239/msmarco-distilbert-base-tas-b-fine-tunned | 2023-10-24T20:03:56.000Z | [
"sentence-transformers",
"pytorch",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | Mike239 | null | null | Mike239/msmarco-distilbert-base-tas-b-fine-tunned | 0 | 2 | sentence-transformers | 2023-10-24T20:03:21 | ---
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 768 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
def cls_pooling(model_output, attention_mask):
return model_output[0][:,0]
# 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, cls pooling.
sentence_embeddings = cls_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 97 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.CosineSimilarityLoss.CosineSimilarityLoss`
Parameters of the fit()-Method:
```
{
"epochs": 10,
"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": null,
"warmup_steps": 10000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | 3,353 | [
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edgolyakova/t5-small-fr-title-generation | 2023-10-24T21:32:23.000Z | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | edgolyakova | null | null | edgolyakova/t5-small-fr-title-generation | 0 | 2 | transformers | 2023-10-24T21:07:48 | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
model-index:
- name: t5-small-fr-title-generation
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. -->
# t5-small-fr-title-generation
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 21 | 3.0220 | 34.5252 | 24.3664 | 33.0025 | 33.235 | 19.0 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.13.3
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Buseak/spellcorrector_2410_v14_canine-s | 2023-10-25T00:57:46.000Z | [
"transformers",
"pytorch",
"tensorboard",
"canine",
"token-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | Buseak | null | null | Buseak/spellcorrector_2410_v14_canine-s | 0 | 2 | transformers | 2023-10-24T21:26:12 | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: spellcorrector_2410_v14_canine-s
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. -->
# spellcorrector_2410_v14_canine-s
This model is a fine-tuned version of [google/canine-s](https://huggingface.co/google/canine-s) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0043
- Precision: 0.9996
- Recall: 0.9994
- F1: 0.9995
- Accuracy: 0.9989
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 25
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.19 | 1.0 | 1951 | 0.1535 | 0.9437 | 0.9787 | 0.9609 | 0.9670 |
| 0.1486 | 2.0 | 3902 | 0.1187 | 0.9591 | 0.9774 | 0.9682 | 0.9716 |
| 0.126 | 3.0 | 5853 | 0.1029 | 0.9671 | 0.9792 | 0.9731 | 0.9739 |
| 0.1082 | 4.0 | 7804 | 0.0888 | 0.9740 | 0.9785 | 0.9763 | 0.9769 |
| 0.0992 | 5.0 | 9755 | 0.0719 | 0.9762 | 0.9852 | 0.9807 | 0.9813 |
| 0.0871 | 6.0 | 11706 | 0.0624 | 0.9805 | 0.9861 | 0.9833 | 0.9832 |
| 0.0782 | 7.0 | 13657 | 0.0527 | 0.9835 | 0.9885 | 0.9860 | 0.9858 |
| 0.0693 | 8.0 | 15608 | 0.0446 | 0.9866 | 0.9898 | 0.9882 | 0.9876 |
| 0.0604 | 9.0 | 17559 | 0.0375 | 0.9888 | 0.9906 | 0.9897 | 0.9893 |
| 0.0543 | 10.0 | 19510 | 0.0318 | 0.9915 | 0.9926 | 0.9921 | 0.9914 |
| 0.046 | 11.0 | 21461 | 0.0272 | 0.9932 | 0.9940 | 0.9936 | 0.9925 |
| 0.0425 | 12.0 | 23412 | 0.0217 | 0.9942 | 0.9950 | 0.9946 | 0.9939 |
| 0.0378 | 13.0 | 25363 | 0.0188 | 0.9953 | 0.9963 | 0.9958 | 0.9946 |
| 0.0333 | 14.0 | 27314 | 0.0160 | 0.9963 | 0.9962 | 0.9962 | 0.9954 |
| 0.0286 | 15.0 | 29265 | 0.0140 | 0.9972 | 0.9970 | 0.9971 | 0.9960 |
| 0.0261 | 16.0 | 31216 | 0.0121 | 0.9977 | 0.9978 | 0.9978 | 0.9966 |
| 0.0235 | 17.0 | 33167 | 0.0104 | 0.9984 | 0.9979 | 0.9982 | 0.9972 |
| 0.021 | 18.0 | 35118 | 0.0090 | 0.9987 | 0.9986 | 0.9986 | 0.9976 |
| 0.0196 | 19.0 | 37069 | 0.0073 | 0.9990 | 0.9988 | 0.9989 | 0.9980 |
| 0.0166 | 20.0 | 39020 | 0.0064 | 0.9992 | 0.9991 | 0.9991 | 0.9983 |
| 0.0158 | 21.0 | 40971 | 0.0059 | 0.9994 | 0.9991 | 0.9992 | 0.9984 |
| 0.0136 | 22.0 | 42922 | 0.0053 | 0.9995 | 0.9994 | 0.9994 | 0.9986 |
| 0.0134 | 23.0 | 44873 | 0.0047 | 0.9996 | 0.9993 | 0.9994 | 0.9988 |
| 0.0125 | 24.0 | 46824 | 0.0045 | 0.9996 | 0.9993 | 0.9995 | 0.9989 |
| 0.0116 | 25.0 | 48775 | 0.0043 | 0.9996 | 0.9994 | 0.9995 | 0.9989 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.13.3
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] |
daniel5984/lora-trained-xl | 2023-10-24T22:53:41.000Z | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | daniel5984 | null | null | daniel5984/lora-trained-xl | 0 | 2 | diffusers | 2023-10-24T21:52:08 |
---
license: creativeml-openrail-m
base_model: segmind/SSD-1B
dataset: /notebooks/dataset/folder
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - daniel5984/lora-trained-xl
These are LoRA adaption weights for segmind/SSD-1B. The weights were fine-tuned on the /notebooks/dataset/folder dataset. You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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VoidZeroe/llama4-model | 2023-10-25T00:21:29.000Z | [
"peft",
"region:us"
] | null | VoidZeroe | null | null | VoidZeroe/llama4-model | 0 | 2 | peft | 2023-10-25T00:19:42 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
- PEFT 0.4.0
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lucas-meyer/xls-r-fleurs_nl-run1 | 2023-10-25T02:35:49.000Z | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:audiofolder",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | lucas-meyer | null | null | lucas-meyer/xls-r-fleurs_nl-run1 | 0 | 2 | transformers | 2023-10-25T00:22:58 | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: xls-r-fleurs_nl-run1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: audiofolder
type: audiofolder
config: default
split: validation
args: default
metrics:
- name: Wer
type: wer
value: 0.4336403033586132
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xls-r-fleurs_nl-run1
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the audiofolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5696
- Wer: 0.4336
## Model description
More information needed
## Intended uses & 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 3
- total_train_batch_size: 12
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 4.9153 | 1.03 | 250 | 2.9252 | 1.0 |
| 2.6885 | 2.05 | 500 | 1.2362 | 0.8548 |
| 0.7443 | 3.08 | 750 | 0.6072 | 0.5436 |
| 0.3497 | 4.11 | 1000 | 0.5776 | 0.4664 |
| 0.2228 | 5.14 | 1250 | 0.5482 | 0.4361 |
| 0.1626 | 6.16 | 1500 | 0.5538 | 0.4166 |
| 0.1301 | 7.19 | 1750 | 0.5696 | 0.4336 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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] |
intanm/xlmrlarge-idkmrc | 2023-10-26T02:50:37.000Z | [
"transformers",
"pytorch",
"xlm-roberta",
"question-answering",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | intanm | null | null | intanm/xlmrlarge-idkmrc | 0 | 2 | transformers | 2023-10-25T00:25:35 | ---
license: mit
base_model: xlm-roberta-large
tags:
- generated_from_trainer
model-index:
- name: xlmrlarge-idkmrc
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. -->
# xlmrlarge-idkmrc
This model is a fine-tuned version of [xlm-roberta-large](https://huggingface.co/xlm-roberta-large) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1300
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9886 | 1.0 | 1167 | 0.9066 |
| 0.5954 | 2.0 | 2334 | 0.8620 |
| 0.3285 | 3.0 | 3501 | 1.1300 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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choidf/finetuning-sentiment-model-bert-base-25000-samples | 2023-10-25T08:46:51.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:imdb",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | choidf | null | null | choidf/finetuning-sentiment-model-bert-base-25000-samples | 0 | 2 | transformers | 2023-10-25T02:38:38 | ---
base_model: choidf/finetuning-sentiment-model-bert-base-25000-samples
tags:
- generated_from_trainer
datasets:
- imdb
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-bert-base-25000-samples
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: imdb
type: imdb
config: plain_text
split: train
args: plain_text
metrics:
- name: Accuracy
type: accuracy
value: 0.9308
- name: F1
type: f1
value: 0.9325009754194303
---
<!-- This model card 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-bert-base-25000-samples
This model is a fine-tuned version of [choidf/finetuning-sentiment-model-bert-base-25000-samples](https://huggingface.co/choidf/finetuning-sentiment-model-bert-base-25000-samples) on the imdb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5129
- Accuracy: 0.9308
- F1: 0.9325
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.0535 | 1.0 | 1407 | 0.4188 | 0.9224 | 0.9222 |
| 0.0324 | 2.0 | 2814 | 0.4382 | 0.928 | 0.9288 |
| 0.0201 | 3.0 | 4221 | 0.4542 | 0.928 | 0.9308 |
| 0.0202 | 4.0 | 5628 | 0.4747 | 0.9296 | 0.9321 |
| 0.0057 | 5.0 | 7035 | 0.5129 | 0.9308 | 0.9325 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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colingao/bert-base-uncased_emotion_ft | 2023-10-25T03:25:04.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | colingao | null | null | colingao/bert-base-uncased_emotion_ft | 0 | 2 | transformers | 2023-10-25T03:12:49 | ---
tags:
- generated_from_trainer
datasets:
- emotion
metrics:
- accuracy
- f1
- precision
model-index:
- name: bert-base-uncased_emotion_ft
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: 0.9355
- name: F1
type: f1
value: 0.9356618127644594
- name: Precision
type: precision
value: 0.9107946719559769
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-base-uncased_emotion_ft
This model was trained from scratch on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1553
- Accuracy: 0.9355
- F1: 0.9357
- Precision: 0.9108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|
| 0.82 | 1.0 | 250 | 0.2697 | 0.9105 | 0.9112 | 0.8759 |
| 0.2002 | 2.0 | 500 | 0.1846 | 0.9325 | 0.9331 | 0.9059 |
| 0.1237 | 3.0 | 750 | 0.1562 | 0.9365 | 0.9368 | 0.9120 |
| 0.097 | 4.0 | 1000 | 0.1553 | 0.9355 | 0.9357 | 0.9108 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.14.4
- Tokenizers 0.13.3
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] |
PratapB/LeoMistral-7b-chat-hf-instruct-dbahn-assistant_v10_3E | 2023-10-25T04:10:30.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"de",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | PratapB | null | null | PratapB/LeoMistral-7b-chat-hf-instruct-dbahn-assistant_v10_3E | 0 | 2 | transformers | 2023-10-25T04:01:47 | ---
license: apache-2.0
language:
- de
library_name: transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This is a chat bot for Dbahn PratapB/LeoMistral-7b-chat-hf-instruct-dbahn-assistant_v10_3E
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** OTSI https://otsi-global.com/
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** Mistral-7b
- **Language(s) (NLP):** German
- **License:** Meta
- **Finetuned from model [optional]:** jphme/em_german_leo_mistral
### Model Sources [optional]
### Training Details:
num_train_epochs = 10,
max_steps = -1,
bf16 = False,
fp16 = True,
per_device_train_batch_size = 4,
per_device_eval_batch_size = 4,
gradient_accumulation_steps = 1,
max_grad_norm = 0.3,
optim = "paged_adamw_32bit",
learning_rate = 3e-5,
lr_scheduler_type = "constant",
warmup_ratio = 0.03,
weight_decay = 0.001,
group_by_length = False,
gradient_checkpointing = True,
save_steps = 100,
logging_steps = 100
### Notice
Du bist ein hilfreicher Assistent. USER: Als ein intelligenter, hilfreicher digitaler KI-Assistent der "Deutsche Bahn AG", antworten Sie auf diese Benutzeranfrage: Kann ich mein Fahrrad kostenfrei mitnehmen? ASSISTANT: | 1,349 | [
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Bandika/Dollyka | 2023-10-25T04:31:01.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Bandika | null | null | Bandika/Dollyka | 0 | 2 | peft | 2023-10-25T04:30:59 | ---
library_name: peft
base_model: EleutherAI/gpt-j-6B
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
Use the code below to get started with the model.
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
| 5,442 | [
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koobear/wbc-10-pretrain-20-epochsss | 2023-10-25T04:49:53.000Z | [
"transformers",
"pytorch",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:image_folder",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | koobear | null | null | koobear/wbc-10-pretrain-20-epochsss | 0 | 2 | transformers | 2023-10-25T04:49:41 | ---
license: apache-2.0
base_model: koobear/masked-10-pretraining-20-epoch
tags:
- generated_from_trainer
datasets:
- image_folder
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: wbc-10-pretrain-20-epochsss
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: image_folder
type: image_folder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.96875
- name: F1
type: f1
value: 0.969269358298248
- name: Precision
type: precision
value: 0.9708341040890965
- name: Recall
type: recall
value: 0.96875
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wbc-10-pretrain-20-epochsss
This model is a fine-tuned version of [koobear/masked-10-pretraining-20-epoch](https://huggingface.co/koobear/masked-10-pretraining-20-epoch) on the image_folder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4309
- Accuracy: 0.9688
- F1: 0.9693
- Precision: 0.9708
- Recall: 0.9688
- Balanced Acc: 0.9599
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 128
- eval_batch_size: 128
- 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 | F1 | Precision | Recall | Balanced Acc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------------:|
| 1.5411 | 1.0 | 7 | 1.3790 | 0.8895 | 0.8985 | 0.9194 | 0.8895 | 0.8473 |
| 1.3881 | 2.0 | 14 | 1.2476 | 0.9242 | 0.9189 | 0.9241 | 0.9242 | 0.8315 |
| 1.2796 | 3.0 | 21 | 1.1089 | 0.9334 | 0.9301 | 0.9382 | 0.9334 | 0.8724 |
| 1.158 | 4.0 | 28 | 1.0077 | 0.9468 | 0.9476 | 0.9530 | 0.9468 | 0.9180 |
| 1.0356 | 5.0 | 35 | 0.9019 | 0.9485 | 0.9495 | 0.9550 | 0.9485 | 0.9220 |
| 0.9418 | 6.0 | 42 | 0.8120 | 0.9514 | 0.9530 | 0.9585 | 0.9514 | 0.9324 |
| 0.8675 | 7.0 | 49 | 0.7336 | 0.9554 | 0.9563 | 0.9591 | 0.9554 | 0.9366 |
| 0.8061 | 8.0 | 56 | 0.6802 | 0.9566 | 0.9574 | 0.9595 | 0.9566 | 0.9457 |
| 0.7419 | 9.0 | 63 | 0.6255 | 0.9525 | 0.9541 | 0.9582 | 0.9525 | 0.9409 |
| 0.6886 | 10.0 | 70 | 0.5781 | 0.9618 | 0.9618 | 0.9634 | 0.9618 | 0.9391 |
| 0.655 | 11.0 | 77 | 0.5521 | 0.9560 | 0.9573 | 0.9607 | 0.9560 | 0.9487 |
| 0.6299 | 12.0 | 84 | 0.5187 | 0.9624 | 0.9628 | 0.9639 | 0.9624 | 0.9465 |
| 0.5987 | 13.0 | 91 | 0.4983 | 0.9624 | 0.9628 | 0.9639 | 0.9624 | 0.9498 |
| 0.5626 | 14.0 | 98 | 0.4799 | 0.9641 | 0.9647 | 0.9661 | 0.9641 | 0.9573 |
| 0.5531 | 15.0 | 105 | 0.4634 | 0.9676 | 0.9680 | 0.9691 | 0.9676 | 0.9565 |
| 0.5037 | 16.0 | 112 | 0.4493 | 0.9676 | 0.9679 | 0.9691 | 0.9676 | 0.9540 |
| 0.5202 | 17.0 | 119 | 0.4415 | 0.9664 | 0.9670 | 0.9685 | 0.9664 | 0.9548 |
| 0.5053 | 18.0 | 126 | 0.4346 | 0.9682 | 0.9686 | 0.9702 | 0.9682 | 0.9558 |
| 0.5102 | 19.0 | 133 | 0.4317 | 0.9699 | 0.9704 | 0.9718 | 0.9699 | 0.9606 |
| 0.4942 | 20.0 | 140 | 0.4309 | 0.9688 | 0.9693 | 0.9708 | 0.9688 | 0.9599 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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] |
Capstone-lpx/mistral7b_instruct | 2023-10-25T05:08:29.000Z | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"finetuned",
"arxiv:2310.06825",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | Capstone-lpx | null | null | Capstone-lpx/mistral7b_instruct | 0 | 2 | transformers | 2023-10-25T05:08:29 | ---
license: apache-2.0
pipeline_tag: text-generation
tags:
- finetuned
inference:
parameters:
temperature: 0.7
---
# Model Card for Mistral-7B-Instruct-v0.1
The Mistral-7B-Instruct-v0.1 Large Language Model (LLM) is a instruct fine-tuned version of the [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) generative text model using a variety of publicly available conversation datasets.
For full details of this model please read our [paper](https://arxiv.org/abs/2310.06825) and [release blog post](https://mistral.ai/news/announcing-mistral-7b/).
## Instruction format
In order to leverage instruction fine-tuning, your prompt should be surrounded by `[INST]` and `[/INST]` tokens. The very first instruction should begin with a begin of sentence id. The next instructions should not. The assistant generation will be ended by the end-of-sentence token id.
E.g.
```
text = "<s>[INST] What is your favourite condiment? [/INST]"
"Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!</s> "
"[INST] Do you have mayonnaise recipes? [/INST]"
```
This format is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")
messages = [
{"role": "user", "content": "What is your favourite condiment?"},
{"role": "assistant", "content": "Well, I'm quite partial to a good squeeze of fresh lemon juice. It adds just the right amount of zesty flavour to whatever I'm cooking up in the kitchen!"},
{"role": "user", "content": "Do you have mayonnaise recipes?"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Model Architecture
This instruction model is based on Mistral-7B-v0.1, a transformer model with the following architecture choices:
- Grouped-Query Attention
- Sliding-Window Attention
- Byte-fallback BPE tokenizer
## Troubleshooting
- If you see the following error:
```
Traceback (most recent call last):
File "", line 1, in
File "/transformers/models/auto/auto_factory.py", line 482, in from_pretrained
config, kwargs = AutoConfig.from_pretrained(
File "/transformers/models/auto/configuration_auto.py", line 1022, in from_pretrained
config_class = CONFIG_MAPPING[config_dict["model_type"]]
File "/transformers/models/auto/configuration_auto.py", line 723, in getitem
raise KeyError(key)
KeyError: 'mistral'
```
Installing transformers from source should solve the issue
pip install git+https://github.com/huggingface/transformers
This should not be required after transformers-v4.33.4.
## Limitations
The Mistral 7B Instruct model is a quick demonstration that the base model can be easily fine-tuned to achieve compelling performance.
It does not have any moderation mechanisms. We're looking forward to engaging with the community on ways to
make the model finely respect guardrails, allowing for deployment in environments requiring moderated outputs.
## The Mistral AI Team
Albert Jiang, Alexandre Sablayrolles, Arthur Mensch, Chris Bamford, Devendra Singh Chaplot, Diego de las Casas, Florian Bressand, Gianna Lengyel, Guillaume Lample, Lélio Renard Lavaud, Lucile Saulnier, Marie-Anne Lachaux, Pierre Stock, Teven Le Scao, Thibaut Lavril, Thomas Wang, Timothée Lacroix, William El Sayed. | 3,848 | [
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astrid01052/cognition-4-noisy | 2023-10-25T07:13:52.000Z | [
"peft",
"region:us"
] | null | astrid01052 | null | null | astrid01052/cognition-4-noisy | 0 | 2 | peft | 2023-10-25T07:12:17 | ---
library_name: peft
---
## 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 0.4.0
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astrid01052/guanaco-3-noisy | 2023-10-25T07:19:18.000Z | [
"peft",
"region:us"
] | null | astrid01052 | null | null | astrid01052/guanaco-3-noisy | 0 | 2 | peft | 2023-10-25T07:14:19 | ---
library_name: peft
---
## 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 0.4.0
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] |
mateiaass/albert-base-qa-coQA-2-k-fold-3 | 2023-10-27T11:46:02.000Z | [
"transformers",
"pytorch",
"albert",
"question-answering",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | question-answering | mateiaass | null | null | mateiaass/albert-base-qa-coQA-2-k-fold-3 | 0 | 2 | transformers | 2023-10-25T07:49:20 | ---
license: apache-2.0
base_model: albert-base-v2
tags:
- generated_from_trainer
model-index:
- name: albert-base-qa-coQA-2-k-fold-3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# albert-base-qa-coQA-2-k-fold-3
This model is a fine-tuned version of [albert-base-v2](https://huggingface.co/albert-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.7332
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.6892 | 1.0 | 5468 | 2.6897 |
| 2.3597 | 2.0 | 10936 | 2.6560 |
| 2.0666 | 3.0 | 16404 | 2.7332 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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A-Funakoshi/bert-wrime-base | 2023-10-27T12:42:44.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"ja",
"endpoints_compatible",
"region:us"
] | text-classification | A-Funakoshi | null | null | A-Funakoshi/bert-wrime-base | 0 | 2 | transformers | 2023-10-25T07:53:05 | ---
language:
- ja
metrics:
- accuracy
- f1
---
# wrime-sentimentデータセットをbertベースのモデルでfinetuningしたもの
- ベースモデル：cl-tohoku/bert-base-japanese-whole-word-masking
- データセット：llm-book/wrime-sentiment
- 学習率スケジュールタイプ(lr_scheduler_type): constant
- 学習率(learning rate): 2e-5
- 勾配累積ステップ(gradient_accumulation_steps): なし
- 正則化(weight_decay): なし
- Epoch: 100
- EarlyStopping: early_stopping_patience=3 | 385 | [
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Supersaiyan1729/mistrail_26_31_1 | 2023-10-25T08:03:07.000Z | [
"peft",
"arxiv:1910.09700",
"region:us"
] | null | Supersaiyan1729 | null | null | Supersaiyan1729/mistrail_26_31_1 | 0 | 2 | peft | 2023-10-25T08:03:02 | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
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## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
| 5,072 | [
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] |
timlim123/al_13b_best | 2023-10-25T08:38:44.000Z | [
"peft",
"region:us"
] | null | timlim123 | null | null | timlim123/al_13b_best | 0 | 2 | peft | 2023-10-25T08:37:55 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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] |
timlim123/13b_random | 2023-10-25T08:40:06.000Z | [
"peft",
"region:us"
] | null | timlim123 | null | null | timlim123/13b_random | 0 | 2 | peft | 2023-10-25T08:39:46 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.0.dev0
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] |
hiddenbox/dogs | 2023-10-25T14:22:26.000Z | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | hiddenbox | null | null | hiddenbox/dogs | 0 | 2 | diffusers | 2023-10-25T08:54:04 |
---
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 - hiddenbox/dogs
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the None dataset. You can find some example images in the following.
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anandNakat/bart_math_solver_2 | 2023-10-25T12:16:41.000Z | [
"transformers",
"pytorch",
"bart",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | anandNakat | null | null | anandNakat/bart_math_solver_2 | 0 | 2 | transformers | 2023-10-25T10:56:22 | ---
license: apache-2.0
base_model: facebook/bart-large
tags:
- generated_from_trainer
model-index:
- name: bart_math_solver_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. -->
# bart_math_solver_2
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6739
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6678 | 1.0 | 221 | 0.6366 |
| 0.6333 | 2.0 | 442 | 0.6897 |
| 0.612 | 3.0 | 663 | 0.6775 |
| 0.5361 | 4.0 | 884 | 0.6384 |
| 0.5411 | 5.0 | 1105 | 0.6976 |
| 0.5831 | 6.0 | 1326 | 0.6655 |
| 0.5733 | 7.0 | 1547 | 0.6790 |
| 0.5658 | 8.0 | 1768 | 0.6739 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
alexionby/output_dir | 2023-10-27T10:38:23.000Z | [
"diffusers",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"text-to-image",
"lora",
"license:openrail++",
"region:us"
] | text-to-image | alexionby | null | null | alexionby/output_dir | 0 | 2 | diffusers | 2023-10-25T11:23:22 |
---
license: openrail++
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: wrong
tags:
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - alexionby/output_dir
These are LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0. The weights were trained on wrong using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
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arieg/food_classifier_noaug | 2023-10-25T12:38:14.000Z | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | arieg | null | null | arieg/food_classifier_noaug | 0 | 2 | transformers | 2023-10-25T12:02:12 | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: arieg/food_classifier_noaug
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# arieg/food_classifier_noaug
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1400
- Validation Loss: 0.1328
- Train Accuracy: 0.969
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 4000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 0.1614 | 0.1377 | 0.971 | 0 |
| 0.1519 | 0.1422 | 0.968 | 1 |
| 0.1429 | 0.1329 | 0.968 | 2 |
| 0.1340 | 0.1328 | 0.969 | 3 |
| 0.1400 | 0.1328 | 0.969 | 4 |
### Framework versions
- Transformers 4.34.1
- TensorFlow 2.14.0
- Datasets 2.14.6
- Tokenizers 0.14.1
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bayerasif/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | 2023-10-25T16:49:33.000Z | [
"transformers",
"pytorch",
"audio-spectrogram-transformer",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:bsd-3-clause",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | bayerasif | null | null | bayerasif/ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan | 0 | 2 | transformers | 2023-10-25T12:41:14 | ---
license: bsd-3-clause
base_model: MIT/ast-finetuned-audioset-10-10-0.4593
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.91
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# ast-finetuned-audioset-10-10-0.4593-finetuned-gtzan
This model is a fine-tuned version of [MIT/ast-finetuned-audioset-10-10-0.4593](https://huggingface.co/MIT/ast-finetuned-audioset-10-10-0.4593) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3527
- Accuracy: 0.91
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2333 | 1.0 | 113 | 0.6532 | 0.82 |
| 0.4931 | 2.0 | 226 | 0.4572 | 0.84 |
| 0.3203 | 3.0 | 339 | 0.4593 | 0.87 |
| 0.0361 | 4.0 | 452 | 0.7718 | 0.85 |
| 0.0349 | 5.0 | 565 | 0.3855 | 0.9 |
| 0.0004 | 6.0 | 678 | 0.3959 | 0.9 |
| 0.0425 | 7.0 | 791 | 0.3581 | 0.9 |
| 0.0002 | 8.0 | 904 | 0.3671 | 0.89 |
| 0.1102 | 9.0 | 1017 | 0.3528 | 0.9 |
| 0.0001 | 10.0 | 1130 | 0.3527 | 0.91 |
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
iambestfeed/phobert_large_finetune_claim | 2023-10-25T13:11:11.000Z | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | sentence-similarity | iambestfeed | null | null | iambestfeed/phobert_large_finetune_claim | 0 | 2 | sentence-transformers | 2023-10-25T13:10:19 | ---
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 34866 with parameters:
```
{'batch_size': 4, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
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": 3e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 3487,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: RobertaModel
(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 --> | 3,872 | [
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cmwalker00/phi-1_5-finetuned-gsm8k | 2023-10-25T17:33:49.000Z | [
"transformers",
"pytorch",
"mixformer-sequential",
"text-generation",
"generated_from_trainer",
"custom_code",
"license:other",
"region:us"
] | text-generation | cmwalker00 | null | null | cmwalker00/phi-1_5-finetuned-gsm8k | 0 | 2 | transformers | 2023-10-25T13:34:49 | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-gsm8k
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-1_5-finetuned-gsm8k
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
TanmaySah/m4 | 2023-10-25T17:37:03.000Z | [
"peft",
"region:us"
] | null | TanmaySah | null | null | TanmaySah/m4 | 0 | 2 | peft | 2023-10-25T13:35:46 | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
- PEFT 0.5.0
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schubertcarvalho/bert-finetuned-ner | 2023-10-25T16:17:23.000Z | [
"transformers",
"pytorch",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | schubertcarvalho | null | null | schubertcarvalho/bert-finetuned-ner | 0 | 2 | transformers | 2023-10-25T14:52:32 | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9210439921208142
- name: Recall
type: recall
value: 0.9442948502187816
- name: F1
type: f1
value: 0.9325245138773475
- name: Accuracy
type: accuracy
value: 0.9857538117383882
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0561
- Precision: 0.9210
- Recall: 0.9443
- F1: 0.9325
- Accuracy: 0.9858
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training 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: 32
- 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 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 439 | 0.0758 | 0.8831 | 0.9192 | 0.9008 | 0.9789 |
| 0.1901 | 2.0 | 878 | 0.0572 | 0.9105 | 0.9399 | 0.9250 | 0.9846 |
| 0.0483 | 3.0 | 1317 | 0.0561 | 0.9210 | 0.9443 | 0.9325 | 0.9858 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0a0+29c30b1
- Datasets 2.14.5
- Tokenizers 0.14.1
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stefanosch/distilhubert-finetuned-gtzan | 2023-10-25T17:35:49.000Z | [
"transformers",
"pytorch",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | stefanosch | null | null | stefanosch/distilhubert-finetuned-gtzan | 0 | 2 | transformers | 2023-10-25T15:55:35 | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
config: all
split: train
args: all
metrics:
- name: Accuracy
type: accuracy
value: 0.79
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the GTZAN dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7113
- Accuracy: 0.79
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.972 | 1.0 | 113 | 1.7719 | 0.43 |
| 1.3321 | 2.0 | 226 | 1.2464 | 0.63 |
| 1.0383 | 3.0 | 339 | 0.9915 | 0.74 |
| 0.9103 | 4.0 | 452 | 0.8751 | 0.75 |
| 0.6181 | 5.0 | 565 | 0.7014 | 0.8 |
| 0.3695 | 6.0 | 678 | 0.7251 | 0.73 |
| 0.5254 | 7.0 | 791 | 0.6452 | 0.8 |
| 0.1551 | 8.0 | 904 | 0.6068 | 0.81 |
| 0.3354 | 9.0 | 1017 | 0.6334 | 0.83 |
| 0.122 | 10.0 | 1130 | 0.7113 | 0.79 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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] |
kerpr/deberta-em-large | 2023-10-25T17:38:50.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | kerpr | null | null | kerpr/deberta-em-large | 0 | 2 | transformers | 2023-10-25T16:49:59 | ---
license: mit
base_model: microsoft/deberta-v3-large
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: deberta-em-large
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# deberta-em-large
This model is a fine-tuned version of [microsoft/deberta-v3-large](https://huggingface.co/microsoft/deberta-v3-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1008
- F1: 0.9367
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
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kmc0003a/finetuned_detr-resnet-50-on-furniture | 2023-10-26T14:19:19.000Z | [
"transformers",
"pytorch",
"detr",
"object-detection",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | kmc0003a | null | null | kmc0003a/finetuned_detr-resnet-50-on-furniture | 0 | 2 | transformers | 2023-10-25T17:00:18 | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: finetuned_detr-resnet-50-on-furniture
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. -->
# finetuned_detr-resnet-50-on-furniture
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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c123ian/phi_test_mcq_v2 | 2023-10-25T17:11:25.000Z | [
"peft",
"pytorch",
"region:us"
] | null | c123ian | null | null | c123ian/phi_test_mcq_v2 | 0 | 2 | peft | 2023-10-25T17:06:40 | ---
library_name: peft
---
## 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: float16
### Framework versions
- PEFT 0.5.0
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thanhdeptrai2003/longt5-summarize | 2023-10-25T17:19:15.000Z | [
"transformers",
"pytorch",
"jax",
"safetensors",
"longt5",
"text2text-generation",
"en",
"arxiv:2112.07916",
"arxiv:1912.08777",
"arxiv:1910.10683",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | thanhdeptrai2003 | null | null | thanhdeptrai2003/longt5-summarize | 0 | 2 | transformers | 2023-10-25T17:17:59 | ---
license: apache-2.0
language: en
---
# LongT5 (local attention, base-sized model)
LongT5 model pre-trained on English language. The model was introduced in the paper [LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://arxiv.org/pdf/2112.07916.pdf) by Guo et al. and first released in [the LongT5 repository](https://github.com/google-research/longt5). All the model architecture and configuration can be found in [Flaxformer repository](https://github.com/google/flaxformer) which uses another Google research project repository [T5x](https://github.com/google-research/t5x).
Disclaimer: The team releasing LongT5 did not write a model card for this model so this model card has been written by the Hugging Face team.
## Model description
LongT5 model is an encoder-decoder transformer pre-trained in a text-to-text denoising generative setting ([Pegasus-like generation pre-training](https://arxiv.org/pdf/1912.08777.pdf)). LongT5 model is an extension of [T5 model](https://arxiv.org/pdf/1910.10683.pdf), and it enables using one of the two different efficient attention mechanisms - (1) Local attention, or (2) Transient-Global attention. The usage of attention sparsity patterns allows the model to efficiently handle input sequence.
LongT5 is particularly effective when fine-tuned for text generation (summarization, question answering) which requires handling long input sequences (up to 16,384 tokens).
## Intended uses & limitations
The model is mostly meant to be fine-tuned on a supervised dataset. See the [model hub](https://huggingface.co/models?search=longt5) to look for fine-tuned versions on a task that interests you.
### How to use
```python
from transformers import AutoTokenizer, LongT5Model
tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base")
model = LongT5Model.from_pretrained("google/long-t5-local-base")
inputs = tokenizer("Hello, my dog is cute", return_tensors="pt")
outputs = model(**inputs)
last_hidden_states = outputs.last_hidden_state
```
### BibTeX entry and citation info
```bibtex
@article{guo2021longt5,
title={LongT5: Efficient Text-To-Text Transformer for Long Sequences},
author={Guo, Mandy and Ainslie, Joshua and Uthus, David and Ontanon, Santiago and Ni, Jianmo and Sung, Yun-Hsuan and Yang, Yinfei},
journal={arXiv preprint arXiv:2112.07916},
year={2021}
}
``` | 2,371 | [
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jessica-ecosia/finetuning-llms-project-2 | 2023-10-25T22:51:02.000Z | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"dataset:financial_phrasebank",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | text-classification | jessica-ecosia | null | null | jessica-ecosia/finetuning-llms-project-2 | 0 | 2 | transformers | 2023-10-25T18:06:48 | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
datasets:
- financial_phrasebank
metrics:
- f1
- accuracy
model-index:
- name: finetuning-llms-project-2
results:
- task:
name: Text Classification
type: text-classification
dataset:
name: financial_phrasebank
type: financial_phrasebank
config: sentences_50agree
split: train
args: sentences_50agree
metrics:
- name: F1
type: f1
value: 0.8330949180475766
- name: Accuracy
type: accuracy
value: 0.8493810178817056
---
<!-- This model card 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-llms-project-2
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the financial_phrasebank dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5427
- F1: 0.8331
- Accuracy: 0.8494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 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 | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------:|
| 0.6137 | 0.94 | 100 | 0.5180 | 0.7614 | 0.8061 |
| 0.297 | 1.89 | 200 | 0.4018 | 0.8201 | 0.8425 |
| 0.1648 | 2.83 | 300 | 0.4641 | 0.8327 | 0.8521 |
| 0.0736 | 3.77 | 400 | 0.5427 | 0.8331 | 0.8494 |
### Framework versions
- Transformers 4.34.0
- Pytorch 2.1.0
- Datasets 2.14.5
- Tokenizers 0.14.1
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HarryYu31/test1 | 2023-10-26T00:14:35.000Z | [
"peft",
"region:us"
] | null | HarryYu31 | null | null | HarryYu31/test1 | 0 | 2 | peft | 2023-10-25T19:58:18 | ---
library_name: peft
---
## 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: float16
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: float16
### Framework versions
- PEFT 0.5.0
- PEFT 0.5.0
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HamdanXI/wav2vec2ctc-base-lj-speech-DifferentStructure | 2023-10-25T20:47:45.000Z | [
"transformers",
"pytorch",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | HamdanXI | null | null | HamdanXI/wav2vec2ctc-base-lj-speech-DifferentStructure | 0 | 2 | transformers | 2023-10-25T20:07:48 | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2ctc-base-lj-speech-DifferentStructure
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. -->
# wav2vec2ctc-base-lj-speech-DifferentStructure
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 0.0001
- train_batch_size: 32
- 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: 1000
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.34.1
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
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rafaelcarvalhoj/emotions_version_01 | 2023-10-25T21:13:33.000Z | [
"transformers",
"pytorch",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-classification | rafaelcarvalhoj | null | null | rafaelcarvalhoj/emotions_version_01 | 0 | 2 | transformers | 2023-10-25T21:13:15 | ---
license: mit
base_model: microsoft/deberta-v3-small
tags:
- generated_from_trainer
model-index:
- name: outputs
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# outputs
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1348
- Pearson: 0.9152
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 256
- eval_batch_size: 512
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Pearson |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log | 1.0 | 18 | 0.7166 | 0.2783 |
| No log | 2.0 | 36 | 0.4269 | 0.6371 |
| No log | 3.0 | 54 | 0.1903 | 0.8733 |
| No log | 4.0 | 72 | 0.1283 | 0.9118 |
| No log | 5.0 | 90 | 0.1348 | 0.9152 |
### Framework versions
- Transformers 4.33.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.13.3
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] |
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