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Matchamahfries/arabertv2-finetuned | Matchamahfries | 2024-07-02T11:39:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T10:18:04Z | Entry not found |
Kavin1701/whisper-small-tamil-adapters | Kavin1701 | 2024-07-02T10:18:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T10:18:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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Kaushikn07/wav2vec2-large-xls-r-300m-turkish-colab | Kaushikn07 | 2024-07-02T10:51:05Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T10:19:00Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Dorjkhnd/Large_main | Dorjkhnd | 2024-07-02T10:19:42Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:19:42Z | Entry not found |
aks1s/15-flow-2 | aks1s | 2024-07-02T10:21:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:20:48Z | Entry not found |
edithram23/Paraphrase-v3 | edithram23 | 2024-07-02T13:26:26Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain",
"base_model:google-t5/t5-base",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-07-02T10:21:13Z |
---
tags:
- autotrain
- text2text-generation
base_model: google-t5/t5-base
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Seq2Seq
## Validation Metrics
loss: 1.9262617826461792
rouge1: 51.5538
rouge2: 25.7728
rougeL: 47.3812
rougeLsum: 47.3727
gen_len: 13.125
runtime: 201.5062
samples_per_second: 99.253
steps_per_second: 1.553
: 9.0
|
dippatel2506/agribrain-v1 | dippatel2506 | 2024-07-02T10:21:25Z | 0 | 1 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T10:21:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
RachidAR/Phi-3-mini-4k-ins-June2024-Q5_K_M-imat-GGUF | RachidAR | 2024-07-02T10:23:56Z | 0 | 0 | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | text-generation | 2024-07-02T10:21:33Z | ---
base_model: microsoft/Phi-3-mini-4k-instruct
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
- llama-cpp
- gguf-my-repo
inference:
parameters:
temperature: 0.0
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
# RachidAR/Phi-3-mini-4k-ins-June2024-Q5_K_M-imat-GGUF (June 2024 Update)
This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model.
## Release Notes
This is an update over the original instruction-tuned Phi-3-mini release based on valuable customer feedback.
The model used additional post-training data leading to substantial gains on instruction following and structure output.
We also **improve multi-turn conversation quality**, **explicitly support <|system|> tag**, and **significantly improve reasoning capability**.
We believe most use cases will benefit from this release, but we encourage users to test in their particular AI applications.
We appreciate the enthusiastic adoption of the Phi-3 model family, and continue to welcome all feedback from the community.
The table below highlights improvements on instruction following, structure output, and reasoning of the new release on publich and internal benchmark datasets.
| Benchmarks | Original | June 2024 Update |
|:------------|:----------|:------------------|
| Instruction Extra Hard | 5.7 | 6.0 |
| Instruction Hard | 4.9 | 5.1 |
| Instructions Challenge | 24.6 | 42.3 |
| JSON Structure Output | 11.5 | 52.3 |
| XML Structure Output | 14.4 | 49.8 |
| GPQA | 23.7 | 30.6 |
| MMLU | 68.8 | 70.9 |
| **Average** | **21.9** | **36.7** |
### Chat Format
Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows.
You can provide the prompt as a question with a generic template as follow:
```markdown
<|system|>
You are a helpful assistant.<|end|>
<|user|>
Question?<|end|>
<|assistant|>
```
For example:
```markdown
<|system|>
You are a helpful assistant.<|end|>
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
```
where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following:
```markdown
<|system|>
You are a helpful travel assistant.<|end|>
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
```
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo RachidAR/Phi-3-mini-4k-instruct-Q6_K-GGUF --hf-file phi-3-mini-4k-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo RachidAR/Phi-3-mini-4k-instruct-Q6_K-GGUF --hf-file phi-3-mini-4k-instruct-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo RachidAR/Phi-3-mini-4k-instruct-Q6_K-GGUF --hf-file phi-3-mini-4k-instruct-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo RachidAR/Phi-3-mini-4k-instruct-Q6_K-GGUF --hf-file phi-3-mini-4k-instruct-q6_k.gguf -c 2048
```
|
Wenboz/phi-3-dpo-noise-0.2 | Wenboz | 2024-07-02T13:51:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"custom_code",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:21:44Z | ---
license: mit
base_model: microsoft/Phi-3-mini-4k-instruct
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
model-index:
- name: phi-3-dpo-noise-0.2
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/causal/huggingface/runs/szdt533p)
# phi-3-dpo-noise-0.2
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) 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: 5e-07
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
|
h-d-h/unit2-taxi | h-d-h | 2024-07-02T10:23:11Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-07-02T10:23:08Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: unit2-taxi
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.48 +/- 2.66
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="h-d-h/unit2-taxi", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
benghoula/stt_V2 | benghoula | 2024-07-02T10:26:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T10:23:53Z | ---
library_name: transformers
tags: []
---
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aks1s/16-flow-2 | aks1s | 2024-07-02T10:25:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:24:10Z | Entry not found |
Trelis/multi-qa-MiniLM-L6-dot-v1-ft-pairs | Trelis | 2024-07-02T10:24:21Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:188",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-dot-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T10:24:16Z | ---
base_model: sentence-transformers/multi-qa-MiniLM-L6-dot-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:188
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What is the minimum force that players of both defending and attacking
teams should use when making a touch?
sentences:
- 'touch football australia 2020 11 13. 5. 5 when possession changes after the half
is touched or when the half places the ball on or over the try line ; or 13. 5.
6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.
13. 6 a player is to perform a rollball seven ( 7 ) metres in - field under the
following circumstances : 13. 6. 1 when a change of possession takes place due
to a player in possession making contact with the sideline or any ground outside
the field of play, prior to a touch being made ; or 13. 6. 2 when the ball not
in possession of a player makes contact with the sideline or any ground outside
the field of play. 13. 7 a player may not perform a tap in replacement of a rollball.
ruling = the offending team must return to the mark and perform the rollball.
13. 8 an attacking player, other than the player performing the rollball, may
receive the ball at the rollball and shall do so without delay. that player is
referred to as the half. 13. 9 the half may control the ball with a foot prior
to picking up the ball. 13. 10 a player'
- or passed and goes to ground during play, a change of possession results. ruling
= the mark for the change of possession is where the ball makes initial contact
with the ground. 9. 5 if the ball, while still under the control of the half,
contacts the ground in the in - goal area, possession is lost. ruling = play will
restart with a rollball at the nearest point on the seven ( 7 ) metre line. fit
playing rules - 5th edition 8 copyright © touch football australia 2020 9. 6 if
a player mishandles the ball and even if in an effort to gain control, the ball
is accidentally knocked forward into any other player, a change of possession
results. 10 the touch 10. 1 a touch may be made by either a defending player or
a player in possession. 10. 2 a defending player may not claim a touch if contact
has not been made. if a player claims a touch has been made, but the referee is
unsure the touch will count. ruling = a penalty to the attacking team at the point
of the infringement and the offending player sent to the sin bin. 10. 3 players
of both defending and attacking teams are to use the minimum force necessary to
make a touch. players must ensure that the
- is normally forty - five minutes, inclusive of a five ( 5 ) minute half time.
end of play when the referee indicates completion of the match. exclusion when
a player is sent to the nearest sin bin area following three ( 3 ) penalties by
the defending team upon entering their seven metre zone. the player is counted
as a player on the field of play and cannot be replaced or interchanged. fit playing
rules - 5th edition copyright © touch football australia 2020 1 fit federation
of international touch field of play the playing area bounded by the sidelines
and dead ball lines, both of which are out of bounds. see appendix 1. forced interchange
when a player is required to undertake a compulsory interchange for an infringement
ruled more serious than a penalty but less serious than a permanent interchange,
sin bin or dismissal. forward a position or direction towards the dead ball line
beyond the team ’ s attacking try line. full time the expiration of the second
period of time allowed for play. half the player who takes possession following
a rollball. half time the break in play between the two halves of a match. imminent
about to occur, it is almost certain to occur. infringement the action of a player
contrary to the rules of the game. in - goal area the area
- source_sentence: What was the name of the convention where TFA presented the TFA
8th edition playing rules?
sentences:
- 5th edition rules touch football tion rules touch football touch football australia
( tfa ) undertook an extensive internal review of their domestic playing rules
throughout 2018 and 2019. the review was led by an vastly experienced group of
current and past players, coaches, referees and administrators of the sport from
community competitions to the elite international game. this group consulted broadly
within the australian community to develop a set of playing rules that could be
applied across all levels of the sport. the result was the tfa 8th edition playing
rules. at the federation of international touch paris convention held in october
2019 touch football australia presented the tfa 8th edition playing rules and
subsequently offered fit and all national touch associations ( ntas ) royalty
free rights to use the newly developed rules. consequently, the fit board resolved
to adopt the tfa 8th edition playing rules as the 5th edition fit playing rules
to be used across all levels of the game internationally. fit and its members
acknowledge and thank touch football australia for the rights to use these rules.
whilst consistency in the application of the rules of the game is important, fit
encourages its members to offer features in local competition rules to ensure
that all participants enjoy a high quality experience. these rules in no way restrict
any nta or their authorised competition providers from having
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- ( 1 ). 6 team coach and team officials 6. 1 the team coach ( s ) and team officials
may be permitted inside the perimeter but shall be required to be positioned either
in the interchange area or at the end of the field of play for the duration of
the match. 6. 2 the team coach ( s ) and team officials may move from one position
to the other but shall do so without delay. while in a position at the end of
the field of play, the team coach ( s ) or team official must remain no closer
than five ( 5 ) metres from the dead ball line and must not coach or communicate
( verbal or non - verbal ) with either team or the referees. 7 commencement and
recommencement of play 7. 1 team captains are to toss a coin in the presence of
the referee ( s ) with the winning captain ’ s team having the choice of the direction
the team wishes to run in the first half ; the choice of interchange areas for
the duration of the match, including any extra time ; and the choice of which
team will commence the match in possession. 7. 2 a player of the attacking team
is to commence the match with a tap at the centre of the halfway line following
the indication to
- source_sentence: What is the minimum number of defending players required to be
in an onside position for a tap to be taken?
sentences:
- ( 4 ) defending players are in an onside position or unless directed to so by
the referee. where the number of players on the field from the defending team
falls below four ( 4 ), all players must be in an onside position for a tap to
be taken unless directed to do so by the referee. ruling = the player will be
directed to return to the mark and to take the tap again. 7. 7 the tap to commence
or recommence play must be performed without delay. ruling = a penalty to the
non - offending team at the centre of the halfway line. 8 match duration 8. 1
a match is 40 minutes in duration, consisting of two ( 2 ) x 20 minute halves
with a half time break. 8. 1. 1 there is no time off for injury during a match.
8. 2 local competition and tournament conditions may vary the duration of a match.
8. 3 when time expires, play is to continue until the next touch or dead ball
and end of play is signaled by the referee. 8. 3. 1 should a penalty be awarded
during this period, the penalty is to be taken. 8. 4 if a match is abandoned in
any circumstances other than those referred to in
- players from the defending team must retire a distance of ten ( 10 ) metres from
the mark or to the defending try line as indicated by the referee. ruling = a
penalty to the attacking team at the point of the infringement or on the ten (
10 ) metre line directly forward of the infringement. 15. 3 at a rollball or tap,
players from the defending team must not retire an unreasonable distance beyond
the defending try line. ruling = a penalty to the attacking team at the point
of the infringement or on the seven ( 7 ) metre line directly forward of the infringement.
15. 4 when a rollball occurs within defending team ’ s seven metre zone or a penalty
tap within ten ( 10 ) metres of the defending team ’ s try line, all players from
the defending team must have both feet on or behind their try line and no other
part of the body in contact with the ground forward of their try line. ruling
= a penalty to the attacking team at the seven ( 7 ) metre line directly forward
of the point of the infringement. 15. 5 after effecting the touch, the defending
player must retire the required seven ( 7 ) metres or to the defending try line
as indicated by the referee without interfering with the attacking team. ruling
=
- '##s as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately
makes contact with the ball in flight and the ball is retrieved by an attacking
player, without touching the ground, play continues and the next touch is zero
( 0 ) touch. 12. 3 if a player from the defending team deliberately makes contact
with the ball in flight, propelling it forward and an attacking player, in an
attempt to regain possession, drops the ball, the attacking team retains possession
and the fit playing rules - 5th edition 10 copyright © touch football australia
2020 touch count restarts as zero ( 0 ) touch. 12. 4 if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team ’ s dead ball line and an attacking player, in an attempt to
regain possession drops the ball, a change of possession occurs. 12. 5 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball goes to ground, a change of possession occurs. 12. 6 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball is retrieved by an attacking player, play and the touch count continues.'
- source_sentence: How many times can a player interchange during a match?
sentences:
- ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get
touched while in possession of the ball. ruling = a change of possession occurs
at the point of the touch, or if in - goal the nearest point on the seven ( 7
) metre line. 10. 8 if a touch is made in the in - goal area before the ball is
grounded, the player in possession is to perform a rollball seven ( 7 ) metres
from the team ’ s attacking try line, provided it is not the sixth touch and the
player is not half. 10. 9 if a player in possession is touched while on or behind
their defending try line, the touch counts and once the referee sets the mark
seven ( 7 ) metres directly forward of the contact point from the defending team
’ s try line, a rollball is performed. 10. 10 if a player in possession intentionally
makes a touch on an offside defender who is making every effort to retire and
remain out of play, the touch counts. fit playing rules - 5th edition copyright
© touch football australia 2020 9 10. 11 if a touch is made on a player in possession
while the player is juggling the ball in an attempt to maintain control of it,
- '##ommencement of play 7 08 i match duration 8 09 i possession 8 10 i the touch
9 11 i passing 10 12 i ball touched in flight 10 13 i the rollball 11 14 i scoring
13 15 i offside 13 16 i obstruction 14 17 i interchange 14 18 i penalty 15 19
i advantage 16 20 i misconduct 16 21 i forced interchange 16 22 i sin bin 16 23
i dismissal 17 24 i drop - off 17 25 i match officials 18 fit playing rules -
5th edition copyright © touch football australia 2020 fit playing rules - 5th
edition copyright © touch football australia 2020 definitions and terminology
unless the contrary intention appears, the following definitions and terminology
apply to the game of touch : term / phrase definition / description advantage
the period of time after an infringement in which the non - offending side has
the opportunity to gain advantage either territorial, tactical or in the form
of a try. attacking try line the line on or over which a player has to place the
ball to score a try. attacking team the team which has or is gaining possession.
behind a position or direction towards a team ’ s defending try line. change of
possession the act of moving control of the ball from one team to the other. dead
/ dead ball when the'
- at the mark where the interference occurred and the touch count remains unchanged.
17 interchange 17. 1 players may interchange at any time. 17. 2 there is no limit
to the number of times a player may interchange. 17. 3 interchange players must
remain in their interchange area for the duration of the match. 17. 4 interchanges
may only occur after the player leaving the field of play has entered the interchange
area. 17. 5 players leaving or entering the field of play shall not hinder or
obstruct play. ruling = a penalty to the non - offending team at the point of
the infringement. 17. 6 players entering the field of play must take up an onside
position before becoming involved in play. fit playing rules - 5th edition 14
copyright © touch football australia 2020 ruling = a penalty to the non - offending
team at the point of the infringement. 17. 7 when an intercept has occurred or
a line break made, players are not permitted to interchange until the next touch
has been made or ball becomes dead. ruling a = if a player enters the field of
play and prevents the scoring of a try, a penalty try will be awarded and the
offending player sent to the sin bin. ruling b = if a player
- source_sentence: What is the ruling if a player in possession holds or impedes a
defending player?
sentences:
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- . attacking team the team which has or is gaining possession. behind a position
or direction towards a team ’ s defending try line. change of possession the act
of moving control of the ball from one team to the other. dead / dead ball when
the ball is out of play including the period following a try and until the match
is recommenced and when the ball goes to ground and / or outside the boundaries
of the field of play prior to the subsequent rollball. dead ball line the end
boundaries of the field of play. there is one at each end of the field of play.
see appendix 1. defending try line the line which a team has to defend to prevent
a try. defending team the team without or which is losing possession. dismissal
when a player is sent from the field of play for the rest of the match. drop -
off a procedure used to determine a winner following equal scores at the expiration
of normal duration. duration the length of time a competition match lasts, which
is normally forty - five minutes, inclusive of a five ( 5 ) minute half time.
end of play when the referee indicates completion of the match. exclusion when
a player is sent to the nearest sin bin area following three ( 3 ) penalties by
the
- directly forward of the point of the infringement. 15. 5 after effecting the touch,
the defending player must retire the required seven ( 7 ) metres or to the defending
try line as indicated by the referee without interfering with the attacking team.
ruling = a penalty to the attacking team ten ( 10 ) metres forward of the infringement
or if on the defensive try line, on the seven ( 7 ) metre line. fit playing rules
- 5th edition copyright © touch football australia 2020 13 16 obstruction 16.
1 a player in possession must not run or otherwise move behind other attacking
players or the referee in an attempt to avoid an imminent touch. ruling = a penalty
to the non - offending team at the point of the infringement. 16. 2 the player
in possession is not to hold or otherwise impede a defending player in any way.
ruling = a penalty to the non - offending team at the point of the infringement.
16. 3 an attacking player in support of the player in possession may move as necessary
to achieve a supporting position but must not grab, hold, push or otherwise deliberately
interfere with a defending player attempting to make a touch ruling = a penalty
to the non - offending team at the point of the infringement or on the seven (
7
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-dot-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-dot-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-dot-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-dot-v1) <!-- at revision c3bdeb02464bc83f9b85156a3386a50bfbf3e6a8 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Dot Product
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-dot-v1-ft-pairs")
# Run inference
sentences = [
'What is the ruling if a player in possession holds or impedes a defending player?',
'directly forward of the point of the infringement. 15. 5 after effecting the touch, the defending player must retire the required seven ( 7 ) metres or to the defending try line as indicated by the referee without interfering with the attacking team. ruling = a penalty to the attacking team ten ( 10 ) metres forward of the infringement or if on the defensive try line, on the seven ( 7 ) metre line. fit playing rules - 5th edition copyright © touch football australia 2020 13 16 obstruction 16. 1 a player in possession must not run or otherwise move behind other attacking players or the referee in an attempt to avoid an imminent touch. ruling = a penalty to the non - offending team at the point of the infringement. 16. 2 the player in possession is not to hold or otherwise impede a defending player in any way. ruling = a penalty to the non - offending team at the point of the infringement. 16. 3 an attacking player in support of the player in possession may move as necessary to achieve a supporting position but must not grab, hold, push or otherwise deliberately interfere with a defending player attempting to make a touch ruling = a penalty to the non - offending team at the point of the infringement or on the seven ( 7',
'. attacking team the team which has or is gaining possession. behind a position or direction towards a team ’ s defending try line. change of possession the act of moving control of the ball from one team to the other. dead / dead ball when the ball is out of play including the period following a try and until the match is recommenced and when the ball goes to ground and / or outside the boundaries of the field of play prior to the subsequent rollball. dead ball line the end boundaries of the field of play. there is one at each end of the field of play. see appendix 1. defending try line the line which a team has to defend to prevent a try. defending team the team without or which is losing possession. dismissal when a player is sent from the field of play for the rest of the match. drop - off a procedure used to determine a winner following equal scores at the expiration of normal duration. duration the length of time a competition match lasts, which is normally forty - five minutes, inclusive of a five ( 5 ) minute half time. end of play when the referee indicates completion of the match. exclusion when a player is sent to the nearest sin bin area following three ( 3 ) penalties by the',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `lr_scheduler_type`: constant
- `warmup_ratio`: 0.3
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: constant
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.3
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.1667 | 2 | 1.5903 | 1.1222 |
| 0.3333 | 4 | 1.3817 | 1.1956 |
| 0.5 | 6 | 0.8451 | 1.1244 |
| 0.6667 | 8 | 1.4958 | 1.1055 |
| 0.8333 | 10 | 1.3135 | 1.0260 |
| 1.0 | 12 | 1.7283 | 0.9734 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Boostaro155/ManUpMale4855 | Boostaro155 | 2024-07-02T10:26:07Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:25:03Z | # ManUp Male Enhancement Gummies New Zealand Reviews, Experiences Official Price, Buy Now
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## **[Click Here To Buy Now From Official Website Of ManUp Male](https://slim-gummies-deutschland.de/manup-nz)**
## Testimonials
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## Safety and Side Effects
### Natural Ingredients
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### Potential Side Effects
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### General Safety Guidelines
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## **[Click Here To Buy Now From Official Website Of ManUp Male](https://slim-gummies-deutschland.de/manup-nz)** |
DOGEKATIORO/patria | DOGEKATIORO | 2024-07-02T10:27:55Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-02T10:26:30Z | ---
license: openrail
---
|
Jellywibble/temp_3k_samples | Jellywibble | 2024-07-02T10:29:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-07-02T10:26:56Z | ---
library_name: transformers
tags:
- trl
- dpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
duyntnet/Silicon-Maid-7B-imatrix-GGUF | duyntnet | 2024-07-02T13:51:42Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"imatrix",
"Silicon-Maid-7B",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | 2024-07-02T10:27:14Z | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- Silicon-Maid-7B
---
Quantizations of https://huggingface.co/SanjiWatsuki/Silicon-Maid-7B
### Experiment
Quants **ending in "_X"** are experimental quants. These quants are the same as normal quants, but their token embedding weights are set to Q8_0 except for Q6_K and Q8_0 which are set to F16. The change will make these experimental quants larger but in theory, should result in improved performance.
List of experimental quants:
* Q2_K_X
* Q4_K_M_X
* Q5_K_M_X
* Q6_K_X
* Q8_0_X
---
### Inference Clients/UIs
* [llama.cpp](https://github.com/ggerganov/llama.cpp)
* [JanAI](https://github.com/janhq/jan)
* [KoboldCPP](https://github.com/LostRuins/koboldcpp)
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
* [ollama](https://github.com/ollama/ollama)
---
# From original readme
Silicon-Maid-7B is another model targeted at being both strong at RP **and** being a smart cookie that can follow character cards very well. As of right now, Silicon-Maid-7B outscores both of my previous 7B RP models in my RP benchmark and I have been impressed by this model's creativity. It is suitable for RP/ERP and general use.
### Prompt Template (Alpaca)
I found the best SillyTavern results from using the Noromaid template but please try other templates! Let me know if you find anything good.
SillyTavern config files: [Context](https://files.catbox.moe/ifmhai.json), [Instruct](https://files.catbox.moe/ttw1l9.json).
Additionally, here is my highly recommended [Text Completion preset](https://huggingface.co/SanjiWatsuki/Loyal-Macaroni-Maid-7B/blob/main/Characters/MinP.json). You can tweak this by adjusting temperature up or dropping min p to boost creativity or raise min p to increase stability. You shouldn't need to touch anything else!
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
``` |
Dilwolf/Product_Review | Dilwolf | 2024-07-02T11:29:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T10:27:35Z | ---
license: mit
---
|
aks1s/17-flow-2 | aks1s | 2024-07-02T10:28:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:27:35Z | Entry not found |
AuraDiffusion/16ch-vae | AuraDiffusion | 2024-07-02T14:09:09Z | 0 | 32 | diffusers | [
"diffusers",
"safetensors",
"art",
"arxiv:2403.03206",
"license:cc",
"model-index",
"region:us"
] | null | 2024-07-02T10:30:04Z | ---
license: cc
library_name: diffusers
tags:
- art
model-index:
- name: 16ch-VAE
results:
- task:
type: encoder-loss
dataset:
name: yerevann/coco-karpathy
type: image
metrics:
- name: PSNR
type: PSNR
value: 31.5151
---
## 16ch-VAE
> Disclaimer: this VAE is not intended to be a replacement for SD3's VAE since the latent spaces are entirely different.
A fully open source 16ch VAE reproduction for the [SD3](https://arxiv.org/abs/2403.03206). Useful for people who are building their own image generation models and need an off-the-shelf VAE
| VAE | rFID | PSNR | LPIPS |
|------------------|--------|---------|--------|
| SD1.5 VAE | 0.3131 | 26.4332 | 0.0328 |
| SDXL VAE | 0.3511 | 26.7577 | 0.032 |
| SD3 VAE | 0.0257 | 30.3231 | 0.0132 |
| [16ch-VAE](https://huggingface.co/AuraDiffusion/16ch-vae) | 0.0667 | 31.5151 | 0.0136 |
| [16ch-VAE with FFT](https://huggingface.co/AuraDiffusion/16ch-vae)* | 0.1584 | 31.0542 | 0.0281 |
### Usage
Awaiting https://github.com/huggingface/diffusers/pull/8769 in diffusers! |
aks1s/18-flow-2 | aks1s | 2024-07-02T10:31:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:30:47Z | Entry not found |
gglabs/Gemma-kiosk-scenario-4-epoch | gglabs | 2024-07-02T10:32:53Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:gemmathon/gemma-2b-ko-dev-pbmt192",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T10:30:52Z | ---
base_model: gemmathon/gemma-2b-ko-dev-pbmt192
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
---
# Uploaded model
- **Developed by:** gglabs
- **License:** apache-2.0
- **Finetuned from model :** gemmathon/gemma-2b-ko-dev-pbmt192
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
NicklasMatzulla/Leon | NicklasMatzulla | 2024-07-02T10:47:23Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-07-02T10:31:00Z | ---
license: mit
---
|
QuantFactory/Llama-3-Swallow-8B-Instruct-v0.1-GGUF | QuantFactory | 2024-07-02T14:05:52Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T10:33:53Z | Entry not found |
QuantFactory/Solar-Ko-Recovery-11B-GGUF | QuantFactory | 2024-07-02T11:52:44Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T10:34:00Z | Entry not found |
benghoula/stt_V3 | benghoula | 2024-07-02T10:41:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T10:34:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
O3S/wazuh_chatbot_copilot_gguf | O3S | 2024-07-02T10:35:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T10:35:20Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** O3S
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nerualdreming/model_of_shame2 | nerualdreming | 2024-07-02T10:50:03Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"en",
"arxiv:2403.03206",
"license:other",
"diffusers:StableDiffusion3Pipeline",
"region:us"
] | text-to-image | 2024-07-02T10:37:57Z | ---
license: other
license_name: stabilityai-nc-research-community
license_link: LICENSE
tags:
- text-to-image
- stable-diffusion
extra_gated_prompt: >-
By clicking "Agree", you agree to the [License
Agreement](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE)
and acknowledge Stability AI's [Privacy
Policy](https://stability.ai/privacy-policy).
extra_gated_fields:
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options:
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I acknowledge that this model is for non-commercial use only unless I acquire a separate license from Stability AI: checkbox
language:
- en
pipeline_tag: text-to-image
---
# Stable Diffusion 3 Medium
## Model
[Stable Diffusion 3 Medium](stability.ai/news/stable-diffusion-3-medium) is a Multimodal Diffusion Transformer (MMDiT) text-to-image model that features greatly improved performance in image quality, typography, complex prompt understanding, and resource-efficiency.
For more technical details, please refer to the [Research paper](https://stability.ai/news/stable-diffusion-3-research-paper).
Please note: this model is released under the Stability Non-Commercial Research Community License. For a Creator License or an Enterprise License visit Stability.ai or [contact us](https://stability.ai/license) for commercial licensing details.
### Model Description
- **Developed by:** Stability AI
- **Model type:** MMDiT text-to-image generative model
- **Model Description:** This is a model that can be used to generate images based on text prompts. It is a Multimodal Diffusion Transformer
(https://arxiv.org/abs/2403.03206) that uses three fixed, pretrained text encoders
([OpenCLIP-ViT/G](https://github.com/mlfoundations/open_clip), [CLIP-ViT/L](https://github.com/openai/CLIP/tree/main) and [T5-xxl](https://huggingface.co/google/t5-v1_1-xxl))
### License
- **Non-commercial Use:** Stable Diffusion 3 Medium is released under the [Stability AI Non-Commercial Research Community License](https://huggingface.co/stabilityai/stable-diffusion-3-medium/blob/main/LICENSE). The model is free to use for non-commercial purposes such as academic research.
- **Commercial Use**: This model is not available for commercial use without a separate commercial license from Stability. We encourage professional artists, designers, and creators to use our Creator License. Please visit https://stability.ai/license to learn more.
### Model Sources
For local or self-hosted use, we recommend [ComfyUI](https://github.com/comfyanonymous/ComfyUI) for inference.
Stable Diffusion 3 Medium is available on our [Stability API Platform](https://platform.stability.ai/docs/api-reference#tag/Generate/paths/~1v2beta~1stable-image~1generate~1sd3/post).
Stable Diffusion 3 models and workflows are available on [Stable Assistant](https://stability.ai/stable-assistant) and on Discord via [Stable Artisan](https://stability.ai/stable-artisan).
- **ComfyUI:** https://github.com/comfyanonymous/ComfyUI
- **StableSwarmUI:** https://github.com/Stability-AI/StableSwarmUI
- **Tech report:** https://stability.ai/news/stable-diffusion-3-research-paper
- **Demo:** https://huggingface.co/spaces/stabilityai/stable-diffusion-3-medium
## Training Dataset
We used synthetic data and filtered publicly available data to train our models. The model was pre-trained on 1 billion images. The fine-tuning data includes 30M high-quality aesthetic images focused on specific visual content and style, as well as 3M preference data images.
## Using with Diffusers
Make sure you upgrade to the latest version of `diffusers`: `pip install -U diffusers`. And then you can run:
```python
import torch
from diffusers import StableDiffusion3Pipeline
pipe = StableDiffusion3Pipeline.from_pretrained("stabilityai/stable-diffusion-3-medium-diffusers", torch_dtype=torch.float16)
pipe = pipe.to("cuda")
image = pipe(
"A cat holding a sign that says hello world",
negative_prompt="",
num_inference_steps=28,
guidance_scale=7.0,
).images[0]
image
```
Refer to [the documentation](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion/stable_diffusion_3) for more details on optimization and image-to-image support.
## Uses
### Intended Uses
Intended uses include the following:
* Generation of artworks and use in design and other artistic processes.
* Applications in educational or creative tools.
* Research on generative models, including understanding the limitations of generative models.
All uses of the model should be in accordance with our [Acceptable Use Policy](https://stability.ai/use-policy).
### Out-of-Scope Uses
The model was not trained to be factual or true representations of people or events. As such, using the model to generate such content is out-of-scope of the abilities of this model.
## Safety
As part of our safety-by-design and responsible AI deployment approach, we implement safety measures throughout the development of our models, from the time we begin pre-training a model to the ongoing development, fine-tuning, and deployment of each model. We have implemented a number of safety mitigations that are intended to reduce the risk of severe harms, however we recommend that developers conduct their own testing and apply additional mitigations based on their specific use cases.
For more about our approach to Safety, please visit our [Safety page](https://stability.ai/safety).
### Evaluation Approach
Our evaluation methods include structured evaluations and internal and external red-teaming testing for specific, severe harms such as child sexual abuse and exploitation, extreme violence, and gore, sexually explicit content, and non-consensual nudity. Testing was conducted primarily in English and may not cover all possible harms. As with any model, the model may, at times, produce inaccurate, biased or objectionable responses to user prompts.
### Risks identified and mitigations:
* Harmful content: We have used filtered data sets when training our models and implemented safeguards that attempt to strike the right balance between usefulness and preventing harm. However, this does not guarantee that all possible harmful content has been removed. The model may, at times, generate toxic or biased content. All developers and deployers should exercise caution and implement content safety guardrails based on their specific product policies and application use cases.
* Misuse: Technical limitations and developer and end-user education can help mitigate against malicious applications of models. All users are required to adhere to our Acceptable Use Policy, including when applying fine-tuning and prompt engineering mechanisms. Please reference the Stability AI Acceptable Use Policy for information on violative uses of our products.
* Privacy violations: Developers and deployers are encouraged to adhere to privacy regulations with techniques that respect data privacy.
### Contact
Please report any issues with the model or contact us:
* Safety issues: [email protected]
* Security issues: [email protected]
* Privacy issues: [email protected]
* License and general: https://stability.ai/license
* Enterprise license: https://stability.ai/enterprise
|
RefalMachine/mistral_darulm_20_05_24_part1-2_32000_bpe_mean_init_03_07_24 | RefalMachine | 2024-07-02T10:41:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T10:38:08Z | Entry not found |
whizzzzkid/whizzzzkid_403_5 | whizzzzkid | 2024-07-02T10:39:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:39:12Z | Entry not found |
hydaitw/llm-compiler-13b-ftd-gguf | hydaitw | 2024-07-02T12:21:40Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T10:39:57Z | Entry not found |
kheopss/kheops_fr_en_epoch1_4bits_GPTQ | kheopss | 2024-07-02T10:41:59Z | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T10:40:05Z | Entry not found |
whizzzzkid/whizzzzkid_404_3 | whizzzzkid | 2024-07-02T10:40:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:40:16Z | Entry not found |
khyat/llama3-cpt-checkpoints | khyat | 2024-07-02T10:52:45Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-02T10:41:09Z | Entry not found |
whizzzzkid/whizzzzkid_405_4 | whizzzzkid | 2024-07-02T10:41:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:41:16Z | Entry not found |
whizzzzkid/whizzzzkid_406_1 | whizzzzkid | 2024-07-02T10:42:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:42:30Z | Entry not found |
0x7o/g-large | 0x7o | 2024-07-02T10:44:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T10:43:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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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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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[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
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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]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF | joaomascarenhas00 | 2024-07-02T10:43:31Z | 0 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | text-generation | 2024-07-02T10:43:07Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
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? By clicking Submit below I accept the terms of the license and acknowledge that
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truthfully.
- role: user
content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please,
respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
---
# joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo joaomascarenhas00/Meta-Llama-3-8B-Instruct-Q4_K_M-GGUF --hf-file meta-llama-3-8b-instruct-q4_k_m.gguf -c 2048
```
|
benghoula/stt_V5 | benghoula | 2024-07-02T10:45:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T10:43:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
whizzzzkid/whizzzzkid_407_7 | whizzzzkid | 2024-07-02T10:44:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:43:47Z | Entry not found |
Pclanglais/French-TV-transcript-NER | Pclanglais | 2024-07-02T15:21:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-07-02T10:44:22Z | **French-TV-transcript-NER** is a named-entity recognition model trained specifically on French TV headlines and transcript.
Given the format specificities, generalist multilingual or French model were unperforming. Additionally, the new model also provide additional set of entities useful in production (such as distinction between first name and last name).
## Entities
The model covers twelve entities:
* First name (prenom)
* Last name (nom)
* Location (lieu)
* Country (pays)
* Organization (organisation)
* Event (evenement)
* Nationality (nationalite)
* Broadcast name (emission)
* Product (produit), such as technological production, medicine, etc.
* Law (loi)
* Cultural creation (creation), such as movie titles, novels, etc.
* Disease (maladie)
|
whizzzzkid/whizzzzkid_408_6 | whizzzzkid | 2024-07-02T10:45:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:44:52Z | Entry not found |
RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf | RichardErkhov | 2024-07-02T16:21:12Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T10:45:08Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
SOLAR-10.7B-dpo-instruct-tuned-v0.1 - GGUF
- Model creator: https://huggingface.co/pinkyponky/
- Original model: https://huggingface.co/pinkyponky/SOLAR-10.7B-dpo-instruct-tuned-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q2_K.gguf) | Q2_K | 3.73GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K.gguf) | Q3_K | 4.84GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_0.gguf) | Q4_0 | 5.66GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K.gguf) | Q4_K | 6.02GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q4_1.gguf) | Q4_1 | 6.27GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_0.gguf) | Q5_0 | 6.89GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K.gguf) | Q5_K | 7.08GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q5_1.gguf) | Q5_1 | 7.51GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q6_K.gguf) | Q6_K | 8.2GB |
| [SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q8_0.gguf](https://huggingface.co/RichardErkhov/pinkyponky_-_SOLAR-10.7B-dpo-instruct-tuned-v0.1-gguf/blob/main/SOLAR-10.7B-dpo-instruct-tuned-v0.1.Q8_0.gguf) | Q8_0 | 10.62GB |
Original model description:
---
license: cc-by-nc-4.0
---
Description to load and test will be added soon. More details on training and data will be added aswell.
### **Loading the Model**
Use the following Python code to load the model:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("pinkyponky/SOLAR-10.7B-dpo-instruct-tuned-v0.1")
model = AutoModelForCausalLM.from_pretrained(
"Upstage/SOLAR-10.7B-v1.0",
device_map="auto",
torch_dtype=torch.bfloat16,
)
```
### **Generating Text**
To generate text, use the following Python code:
```python
text = "Hi, my name is "
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=64)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF | victorbur | 2024-07-02T10:46:15Z | 0 | 0 | null | [
"gguf",
"roleplay",
"llama3",
"sillytavern",
"idol",
"llama-cpp",
"gguf-my-repo",
"en",
"ja",
"zh",
"base_model:aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K",
"license:llama3",
"region:us"
] | null | 2024-07-02T10:45:47Z | ---
base_model: aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K
language:
- en
- ja
- zh
license: llama3
tags:
- roleplay
- llama3
- sillytavern
- idol
- llama-cpp
- gguf-my-repo
---
# victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF
This model was converted to GGUF format from [`aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K`](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/aifeifei798/llama3-8B-DarkIdol-2.1-Uncensored-32K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF --hf-file llama3-8b-darkidol-2.1-uncensored-32k-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF --hf-file llama3-8b-darkidol-2.1-uncensored-32k-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF --hf-file llama3-8b-darkidol-2.1-uncensored-32k-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo victorbur/llama3-8B-DarkIdol-2.1-Uncensored-32K-Q5_K_M-GGUF --hf-file llama3-8b-darkidol-2.1-uncensored-32k-q5_k_m.gguf -c 2048
```
|
RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf | RichardErkhov | 2024-07-02T16:09:40Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T10:46:27Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp - GGUF
- Model creator: https://huggingface.co/invalid-coder/
- Original model: https://huggingface.co/invalid-coder/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q2_K.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q2_K.gguf) | Q2_K | 3.73GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_XS.gguf) | IQ3_XS | 4.14GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_S.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_S.gguf) | IQ3_S | 4.37GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_S.gguf) | Q3_K_S | 4.34GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_M.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ3_M.gguf) | IQ3_M | 4.51GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K.gguf) | Q3_K | 4.84GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_M.gguf) | Q3_K_M | 4.84GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q3_K_L.gguf) | Q3_K_L | 5.26GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ4_XS.gguf) | IQ4_XS | 5.43GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_0.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_0.gguf) | Q4_0 | 5.66GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.IQ4_NL.gguf) | IQ4_NL | 5.72GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K_S.gguf) | Q4_K_S | 5.7GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K.gguf) | Q4_K | 6.02GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_K_M.gguf) | Q4_K_M | 6.02GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_1.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q4_1.gguf) | Q4_1 | 6.27GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_0.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_0.gguf) | Q5_0 | 6.89GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K_S.gguf) | Q5_K_S | 6.89GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K.gguf) | Q5_K | 7.08GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_K_M.gguf) | Q5_K_M | 7.08GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_1.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q5_1.gguf) | Q5_1 | 7.51GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q6_K.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q6_K.gguf) | Q6_K | 8.2GB |
| [Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q8_0.gguf](https://huggingface.co/RichardErkhov/invalid-coder_-_Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp-gguf/blob/main/Sakura-SOLAR-Instruct-CarbonVillain-en-10.7B-v2-slerp.Q8_0.gguf) | Q8_0 | 10.62GB |
Original model description:
---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- jeonsworld/CarbonVillain-en-10.7B-v2
- kyujinpy/Sakura-SOLAR-Instruct
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [jeonsworld/CarbonVillain-en-10.7B-v2](https://huggingface.co/jeonsworld/CarbonVillain-en-10.7B-v2)
* [kyujinpy/Sakura-SOLAR-Instruct](https://huggingface.co/kyujinpy/Sakura-SOLAR-Instruct)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: jeonsworld/CarbonVillain-en-10.7B-v2
layer_range: [0, 48]
- model: kyujinpy/Sakura-SOLAR-Instruct
layer_range: [0, 48]
merge_method: slerp
base_model: jeonsworld/CarbonVillain-en-10.7B-v2
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5 # fallback for rest of tensors
tokenizer_source: union
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "invalid-coder/NeuralPipe-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
|
sdfgqsfcxqsd/9-children-killed-in-paramilitary-attack-in-Sudan-e2-updated | sdfgqsfcxqsd | 2024-07-02T10:47:32Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:47:32Z | Entry not found |
HARSHU550/Grammer | HARSHU550 | 2024-07-02T10:57:50Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"en",
"dataset:facebook/asset",
"dataset:wi_locness",
"dataset:GEM/wiki_auto_asset_turk",
"dataset:discofuse",
"dataset:zaemyung/IteraTeR_plus",
"dataset:jfleg",
"dataset:grammarly/coedit",
"arxiv:2305.09857",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text2text-generation | 2024-07-02T10:48:41Z | ---
language:
- en
license: cc-by-nc-4.0
datasets:
- facebook/asset
- wi_locness
- GEM/wiki_auto_asset_turk
- discofuse
- zaemyung/IteraTeR_plus
- jfleg
- grammarly/coedit
metrics:
- sari
- bleu
- accuracy
widget:
- text: 'Fix the grammar: When I grow up, I start to understand what he said is quite
right.'
example_title: Fluency
- text: 'Make this text coherent: Their flight is weak. They run quickly through the
tree canopy.'
example_title: Coherence
- text: 'Rewrite to make this easier to understand: A storm surge is what forecasters
consider a hurricane''s most treacherous aspect.'
example_title: Simplification
- text: 'Paraphrase this: Do you know where I was born?'
example_title: Paraphrase
- text: 'Write this more formally: omg i love that song im listening to it right now'
example_title: Formalize
- text: 'Write in a more neutral way: The authors'' exposé on nutrition studies.'
example_title: Neutralize
---
# Model Card for CoEdIT-Large
This model was obtained by fine-tuning the corresponding `google/flan-t5-large` model on the CoEdIT dataset. Details of the dataset can be found in our paper and repository.
**Paper:** CoEdIT: Text Editing by Task-Specific Instruction Tuning
**Authors:** Vipul Raheja, Dhruv Kumar, Ryan Koo, Dongyeop Kang
## Model Details
### Model Description
- **Language(s) (NLP)**: English
- **Finetuned from model:** google/flan-t5-large
### Model Sources
- **Repository:** https://github.com/vipulraheja/coedit
- **Paper:** https://arxiv.org/abs/2305.09857
## How to use
We make available the models presented in our paper.
<table>
<tr>
<th>Model</th>
<th>Number of parameters</th>
</tr>
<tr>
<td>CoEdIT-large</td>
<td>770M</td>
</tr>
<tr>
<td>CoEdIT-xl</td>
<td>3B</td>
</tr>
<tr>
<td>CoEdIT-xxl</td>
<td>11B</td>
</tr>
</table>
## Uses
## Text Revision Task
Given an edit instruction and an original text, our model can generate the edited version of the text.<br>
## Usage
```python
from transformers import AutoTokenizer, T5ForConditionalGeneration
tokenizer = AutoTokenizer.from_pretrained("grammarly/coedit-large")
model = T5ForConditionalGeneration.from_pretrained("grammarly/coedit-large")
input_text = 'Fix grammatical errors in this sentence: When I grow up, I start to understand what he said is quite right.'
input_ids = tokenizer(input_text, return_tensors="pt").input_ids
outputs = model.generate(input_ids, max_length=256)
edited_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
```
#### Software
https://github.com/vipulraheja/coedit
## Citation
**BibTeX:**
```
@article{raheja2023coedit,
title={CoEdIT: Text Editing by Task-Specific Instruction Tuning},
author={Vipul Raheja and Dhruv Kumar and Ryan Koo and Dongyeop Kang},
year={2023},
eprint={2305.09857},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
**APA:**
Raheja, V., Kumar, D., Koo, R., & Kang, D. (2023). CoEdIT: Text Editing by Task-Specific Instruction Tuning. ArXiv. /abs/2305.09857 |
qsdcfqsdfcxqfqs/Lifting-children-out-of-poverty-must-be-top-priority-for-next-government-bd-updated | qsdcfqsdfcxqfqs | 2024-07-02T10:49:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:49:12Z | Entry not found |
bulkbeings/kk | bulkbeings | 2024-07-02T10:50:47Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-02T10:50:47Z | ---
license: mit
---
|
SidXXD/cos_1-eps_10-alpha_5e-2-person | SidXXD | 2024-07-02T11:10:00Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"custom-diffusion",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-07-02T10:50:51Z |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: photo of a <v1*> person
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- custom-diffusion
inference: true
---
# Custom Diffusion - SidXXD/cos_1-eps_10-alpha_5e-2-person
These are Custom Diffusion adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on photo of a <v1*> person using [Custom Diffusion](https://www.cs.cmu.edu/~custom-diffusion). You can find some example images in the following.
For more details on the training, please follow [this link](https://github.com/huggingface/diffusers/blob/main/examples/custom_diffusion).
|
qsdcfqsdfcxqfqs/How-Politicizing-Migration-Harms-Health-hh-updated | qsdcfqsdfcxqfqs | 2024-07-02T10:52:07Z | 0 | 0 | null | [
"en",
"region:us"
] | null | 2024-07-02T10:50:53Z | ---
language:
- en
---
[]()
read the full article here : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_5414543232&Connector=https://unitedstatednews.com
Source : https://huggingface.co/sdfgqsfcxqsd/9-children-killed-in-paramilitary-attack-in-Sudan-e2-updated/new/main/?filename=README.md
Flash News : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_1111534523&Connector=https://unitedstatednews.com
Biden last Talk : https://www.wowace.com/paste/e60ac04f
Russian Ukrain Breaking News : https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4532455455&Connector=https://unitedstatednews.com
Other Sources :
https://paste2.org/nIN0FD9J
https://tempaste.com/iT0MsRMZXJi
https://snippet.host/tojxpk
https://www.sep.va.gov/sep/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=Games_Free_Generator_135&Connector=https://unitedstatednews.com
https://prod.pastebin.prod.webservices.mozgcp.net/DfDNWRip
https://paste.enginehub.org/PVr8hBMNc
https://tempaste.com/I3LuLQ5m1yv
https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_4525245233&Connector=https://unitedstatednews.com
https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_2351253222&Connector=https://unitedstatednews.com
https://tempaste.com/FdebmxowFgb
https://paste.imirhil.fr/?00681ab2db88f14d#SvJIzafTI96FWQwvhUFihi74HnGnd5zMXlzubWse3RM=
https://tempaste.com/eJd7UHgiPxb
https://cicytex.juntaex.es/html/js/editor/ckeditor/editor/filemanager/browser/liferay/browser.html?id=nuevo_hackear_cuenta_3322232213&Connector=https://unitedstatednews.com
https://www.wowace.com/paste/687fc464
Politicians around the world are increasingly mobilizing anti-immigrant sentiment to garner support and votes -- a trend that is especially evident as the US presidential election approaches.
While political rhetoric that stereotypes and scapegoats immigrants is well-documented, less attention has been given to the impact of these sentiments on immigrants themselves. In an article published in the Journal of the American Medical Association (JAMA) and in a recently published book, Migration Stigma (MIT Press), scholars identify "migration stigma" as a pervasive and destructive force that links responses to immigration -- such as prejudice and politics -- to the health of immigrants.
"This concept of 'migration stigma' for the first time pulls together phenomena like the politicization of immigration and goes beyond how the native born think of immigrants to consider how it influences physical and mental health," said Lawrence Yang, professor and chair of the Department of Social and Behavioral Sciences at NYU School of Global Public Health, first author of the JAMA article, and lead editor of Migration Stigma.
In the JAMA article, the authors write that being labeled as a migrant can set off a cascade of negative consequences: stereotyping, separation or "othering," discrimination, and loss of social status. In the context of power dynamics, these factors together result in stigma.
Stigma can take different forms, but all risk harming the health and mental health of immigrants. One form, structural stigma, occurs when groups are treated differently by laws or policies based on their status. For immigrants, this may mean worse access to education, housing, health care, and jobs -- all of which are powerful social determinants of health, or social and structural factors that influence health outcomes.
Other forms of stigma may be less obvious. For instance, immigrants and their descendants who are attuned to the negative political environment and stereotypes people hold about immigrants may feel shame and internalize these negative beliefs.
Internalized stigma can increase stress, which may lead to a host of mental health issues, including anxiety, depression, and sleep disorders, and can even exacerbate post-traumatic stress disorder (PTSD) among migrants who endured traumatic journeys across borders. Moreover, internalized stigma -- coupled with the fear of deportation -- may deter immigrants from seeking medical attention and other services that ultimately improve their health and life opportunities, including jobs and education.
"New immigrants who are aware that the US is not the most hospitable place right now may respond to this negative environment by inadvertently avoiding opportunities to maintain their health," said Yang, who is also the founding director of the Global Mental Health and Stigma Program at the NYU School of Global Public Health.
"A new focus on the intersection of migration and stigma creates an opportunity to break the cycle of harmful policies and rhetoric that fuel stigma and hurt the health of immigrants and others," Yang said. And because stigma involves many factors -- labeling, stereotyping, "othering," and loss of status -- interventions to reduce stigma can work to address any of these linkages.
"For instance, we can introduce new narratives to change a label or address stereotyping, or can encourage policymakers to enact anti-discrimination laws to preserve access to health care and education," added Yang.
The authors also write in JAMA that to avoid stigmatizing migrant patients, health professionals can recognize that health and illness stem from social, political, and economic structures.
The concept of migration stigma grew out of an international forum, convened by the Ernst Stüngmann Foundation, that brought together scholars in the fields of stigma and migration to explore connections between the two.
"Although both fields examine the causes and consequences of prejudice and discrimination, until recently there was little formal collaboration between stigma and migration scholars," said Yang.
Through this process, the scholars coined the concept of migration stigma and launched this new field of research. Future areas of study include the impact of the migrant label on different life domains, whether the label extends beyond migrants themselves to other generations or associated racial or ethnic groups, and whether stigma has long-term health consequences.
"By examining how the seemingly disparate phenomena of anti-immigrant politics and individuals' health are related, we enhance the possibilities for researchers and clinicians to understand, and ideally, intervene to promote public health," the authors write in JAMA.
The JAMA article was co-authored by Bruce Link of UC Riverside and the Columbia Mailman School of Public Health and Maureen Eger of Umeå University and the Center for Right-Wing Studies at UC Berkeley. Eger and Link are also the co-editors of the book..... |
rakeshreddy95/llama-7b-doctor-v0.1 | rakeshreddy95 | 2024-07-02T10:51:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:51:48Z | Entry not found |
PleIAs/French-TV-Headline-Classification | PleIAs | 2024-07-02T15:12:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T10:52:57Z | **French-TV-Headline-Classification** is an encoder model specialized for the classification of French news headlines.
The model incorporate two different sets of categories: thematics and non-thematics. Headlines fill multiple functions in the informational design of television and not all of them are straight about actual topics.
## Thematics categories
This covers all headlines about a classifiable event. The classification relies not only on the actual content but also on very distinctive manneer of phrasing the news, as the headlines are likely issues by different news service.
Typically crime news ("fait divers") uses a more daily/informal language than political news.
The available thematic categories are:
* Politique
* Relations internationales
* Santé
* Santé
* Sport
* Économie
* Éducation
* Culture
* Fait divers
* Débat
## Non-thematic categories
This includes headlines that are not directly describing an event but filling multiple meta-communication tasks helping to better contextualize the news broadcasts.
The available non-thematic categories are:
* Attribution (typically caption for the journalists that made a news reports)
* Clip (a musical clip with credits)
* Citation (a quote attributed to a specific person)
* Intervenant (the name/bio of a person speaking)
* Sondage (results of a poll)
* Méta (any kind of meta indication related to the broadcast)
* Inclassable (can't be classified under any of the previous categories)
## Examples
* *Dune 2 : Villeneuve dévoile les coulisses du film* => **Culture**
* *Nucléaire/espace : Poutine veut s'y implanter* => **Relations internationales**
* *Philippe Chalmin, économiste, spécialiste des marchés de matières premières* => **Intervenant**
* *Belloubet : "Faire de l'école un sanctuaire"* => **Citation**
|
nttaii/run_20240702175303 | nttaii | 2024-07-02T10:53:04Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:53:04Z | Invalid username or password. |
Akshay47/Llama-3-8B-Instruct_bvr_finetune_v2 | Akshay47 | 2024-07-02T10:53:16Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:53:16Z | Entry not found |
yemen2016/danskbert_1_NCST | yemen2016 | 2024-07-02T11:42:25Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:vesteinn/DanskBERT",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T10:53:50Z | ---
license: cc-by-4.0
base_model: vesteinn/DanskBERT
tags:
- generated_from_trainer
model-index:
- name: danskbert_1_NCST
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. -->
# danskbert_1_NCST
This model is a fine-tuned version of [vesteinn/DanskBERT](https://huggingface.co/vesteinn/DanskBERT) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0631
- F1-score: 0.5516
## Model description
More information needed
## Intended uses & 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6966 | 1.0 | 528 | 0.6922 | 0.4956 |
| 0.6865 | 2.0 | 1056 | 0.6859 | 0.5169 |
| 0.6391 | 3.0 | 1584 | 0.7170 | 0.5367 |
| 0.5605 | 4.0 | 2112 | 0.8024 | 0.5479 |
| 0.4597 | 5.0 | 2640 | 0.9936 | 0.5316 |
| 0.3856 | 6.0 | 3168 | 1.1091 | 0.5410 |
| 0.3049 | 7.0 | 3696 | 1.4508 | 0.5338 |
| 0.2469 | 8.0 | 4224 | 1.8612 | 0.5312 |
| 0.2278 | 9.0 | 4752 | 1.9508 | 0.5569 |
| 0.2115 | 10.0 | 5280 | 2.0631 | 0.5516 |
### Framework versions
- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
internlm/internlm-xcomposer2d5-7b | internlm | 2024-07-02T10:54:13Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-07-02T10:54:13Z | ---
license: apache-2.0
---
|
controngo/wav2vec2-large-xls-r-300m-de | controngo | 2024-07-02T13:09:33Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T10:54:21Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
qsdcfqsdfcxqfqs/Screening-Mammography-Performance-Improved-Workload-Decreased-With-AI-Assistance-gc-updated | qsdcfqsdfcxqfqs | 2024-07-02T10:54:47Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:54:47Z | Entry not found |
qsdcfqsdfcxqfqs/Alderman-pushes-for-earlier-curfew-at-Chicago-31st-Street-Beach-after-violent-month-f4-updated | qsdcfqsdfcxqfqs | 2024-07-02T10:54:58Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:54:58Z | Entry not found |
habulaj/145299121778 | habulaj | 2024-07-02T10:55:03Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T10:55:01Z | Entry not found |
whizzzzkid/whizzzzkid_409_2 | whizzzzkid | 2024-07-02T10:56:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T10:55:52Z | Entry not found |
Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs | Trelis | 2024-07-02T10:56:23Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:211",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T10:56:11Z | ---
base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:211
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How long is the initial period of extra time in the drop-off procedure?
sentences:
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- source_sentence: What happens to the touch count if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team's dead ball line and an attacking player drops the ball?
sentences:
- '##s as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately
makes contact with the ball in flight and the ball is retrieved by an attacking
player, without touching the ground, play continues and the next touch is zero
( 0 ) touch. 12. 3 if a player from the defending team deliberately makes contact
with the ball in flight, propelling it forward and an attacking player, in an
attempt to regain possession, drops the ball, the attacking team retains possession
and the fit playing rules - 5th edition 10 copyright © touch football australia
2020 touch count restarts as zero ( 0 ) touch. 12. 4 if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team ’ s dead ball line and an attacking player, in an attempt to
regain possession drops the ball, a change of possession occurs. 12. 5 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball goes to ground, a change of possession occurs. 12. 6 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball is retrieved by an attacking player, play and the touch count continues.'
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- ( 4 ) defending players are in an onside position or unless directed to so by
the referee. where the number of players on the field from the defending team
falls below four ( 4 ), all players must be in an onside position for a tap to
be taken unless directed to do so by the referee. ruling = the player will be
directed to return to the mark and to take the tap again. 7. 7 the tap to commence
or recommence play must be performed without delay. ruling = a penalty to the
non - offending team at the centre of the halfway line. 8 match duration 8. 1
a match is 40 minutes in duration, consisting of two ( 2 ) x 20 minute halves
with a half time break. 8. 1. 1 there is no time off for injury during a match.
8. 2 local competition and tournament conditions may vary the duration of a match.
8. 3 when time expires, play is to continue until the next touch or dead ball
and end of play is signaled by the referee. 8. 3. 1 should a penalty be awarded
during this period, the penalty is to be taken. 8. 4 if a match is abandoned in
any circumstances other than those referred to in
- source_sentence: What happens if an attacking player fails to perform a rollball
on the mark?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get
touched while in possession of the ball. ruling = a change of possession occurs
at the point of the touch, or if in - goal the nearest point on the seven ( 7
) metre line. 10. 8 if a touch is made in the in - goal area before the ball is
grounded, the player in possession is to perform a rollball seven ( 7 ) metres
from the team ’ s attacking try line, provided it is not the sixth touch and the
player is not half. 10. 9 if a player in possession is touched while on or behind
their defending try line, the touch counts and once the referee sets the mark
seven ( 7 ) metres directly forward of the contact point from the defending team
’ s try line, a rollball is performed. 10. 10 if a player in possession intentionally
makes a touch on an offside defender who is making every effort to retire and
remain out of play, the touch counts. fit playing rules - 5th edition copyright
© touch football australia 2020 9 10. 11 if a touch is made on a player in possession
while the player is juggling the ball in an attempt to maintain control of it,
- source_sentence: What is the result of a match if it is abandoned in circumstances
other than those specified in clause 24.1.6?
sentences:
- 'touch football australia 2020 11 13. 5. 5 when possession changes after the half
is touched or when the half places the ball on or over the try line ; or 13. 5.
6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.
13. 6 a player is to perform a rollball seven ( 7 ) metres in - field under the
following circumstances : 13. 6. 1 when a change of possession takes place due
to a player in possession making contact with the sideline or any ground outside
the field of play, prior to a touch being made ; or 13. 6. 2 when the ball not
in possession of a player makes contact with the sideline or any ground outside
the field of play. 13. 7 a player may not perform a tap in replacement of a rollball.
ruling = the offending team must return to the mark and perform the rollball.
13. 8 an attacking player, other than the player performing the rollball, may
receive the ball at the rollball and shall do so without delay. that player is
referred to as the half. 13. 9 the half may control the ball with a foot prior
to picking up the ball. 13. 10 a player'
- a rollball. half time the break in play between the two halves of a match. imminent
about to occur, it is almost certain to occur. infringement the action of a player
contrary to the rules of the game. in - goal area the area in the field of play
bounded by the sidelines, the try lines and the dead ball lines. there are two
( 2 ), one ( 1 ) at each end of the field of play. see appendix 1. interchange
the act of an on - field player leaving the field of play to be replaced by an
off - field player entering the field of play. interchange area a marked rectangle
for each team on opposite sides of the field of play usually measuring 20 metres
long by no more than five ( 5 ) metres wide, extending ten ( 10 ) metres either
side of the halfway line and not less than one ( 1 ) metre from the sideline.
it is the area in which all off - field players must remain until an interchange
is initiated. see appendix 1. kick strike or propel forcibly with the foot, a
blow or forceful thrust with the foot to the ball. a tap to commence or recommence
play or a penalty tap is not defined as a kick. line markings
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- source_sentence: What is the ruling if an attacking player intentionally passes
the ball to a defending player seeking a rebound or restart of the touch count?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- . see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful
thrust with the foot to the ball. a tap to commence or recommence play or a penalty
tap is not defined as a kick. line markings markings of the field of play. see
appendix 1. link the player beside the wing player. mark ( for a tap ) the centre
of the halfway line for the commencement or recommencement of play, or the position
where a penalty tap is awarded as a result of an infringement. mark ( for a touch
) the position in the field of play where the player in possession was at the
time the touch was made. fit playing rules - 5th edition 2 copyright © touch football
australia 2020 middle the player inside the link player. nta national touch association
as defined in the fit constitution. obstruction a deliberate attempt by either
an attacking or defending player to gain an unfair advantage by interfering with
the opposition to prevent them from gaining a rightful advantage. offside ( attacker
) an attacking player in a position forward of the ball. offside ( defender )
a defending player in a position closer than seven ( 7 ) metres from the mark
of the rollball ; or ten ( 10 ) metres from the mark
- or passed and goes to ground during play, a change of possession results. ruling
= the mark for the change of possession is where the ball makes initial contact
with the ground. 9. 5 if the ball, while still under the control of the half,
contacts the ground in the in - goal area, possession is lost. ruling = play will
restart with a rollball at the nearest point on the seven ( 7 ) metre line. fit
playing rules - 5th edition 8 copyright © touch football australia 2020 9. 6 if
a player mishandles the ball and even if in an effort to gain control, the ball
is accidentally knocked forward into any other player, a change of possession
results. 10 the touch 10. 1 a touch may be made by either a defending player or
a player in possession. 10. 2 a defending player may not claim a touch if contact
has not been made. if a player claims a touch has been made, but the referee is
unsure the touch will count. ruling = a penalty to the attacking team at the point
of the infringement and the offending player sent to the sin bin. 10. 3 players
of both defending and attacking teams are to use the minimum force necessary to
make a touch. players must ensure that the
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision 2430568290bb832d22ad5064f44dd86cf0240142 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs")
# Run inference
sentences = [
'What is the ruling if an attacking player intentionally passes the ball to a defending player seeking a rebound or restart of the touch count?',
'ball goes to ground, a change of possession occurs. 12. 6 if a player from the defending team unintentionally makes contact with the ball in flight and the ball is retrieved by an attacking player, play and the touch count continues. 12. 7 a player from the attacking team cannot pass the ball into a defending player intentionally seeking a rebound or a restart of the touch count. ruling = a penalty to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking player is to position on the mark, face the opponent ’ s try line, make a genuine attempt to stand parallel to the sidelines, place the ball on the ground between the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or 13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or 13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending team at the point of the infringement. 13. 2 a player must perform the rollball on the mark. ruling = a penalty to the defending team at the point of the infringement. 13. 3 a player must not perform a voluntary rollball',
'. see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful thrust with the foot to the ball. a tap to commence or recommence play or a penalty tap is not defined as a kick. line markings markings of the field of play. see appendix 1. link the player beside the wing player. mark ( for a tap ) the centre of the halfway line for the commencement or recommencement of play, or the position where a penalty tap is awarded as a result of an infringement. mark ( for a touch ) the position in the field of play where the player in possession was at the time the touch was made. fit playing rules - 5th edition 2 copyright © touch football australia 2020 middle the player inside the link player. nta national touch association as defined in the fit constitution. obstruction a deliberate attempt by either an attacking or defending player to gain an unfair advantage by interfering with the opposition to prevent them from gaining a rightful advantage. offside ( attacker ) an attacking player in a position forward of the ball. offside ( defender ) a defending player in a position closer than seven ( 7 ) metres from the mark of the rollball ; or ten ( 10 ) metres from the mark',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
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### Out-of-Scope Use
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.3
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.3
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.1429 | 2 | 1.0897 | - |
| 0.2857 | 4 | 1.3506 | 1.1206 |
| 0.4286 | 6 | 1.1968 | - |
| 0.5714 | 8 | 1.4074 | 1.0205 |
| 0.7143 | 10 | 1.3476 | - |
| 0.8571 | 12 | 1.0062 | 1.0278 |
| 1.0 | 14 | 1.4792 | - |
| 1.1429 | 16 | 0.8863 | 1.1568 |
| 1.2857 | 18 | 0.5465 | - |
| 1.4286 | 20 | 0.5672 | 1.1830 |
| 1.5714 | 22 | 0.5482 | - |
| 1.7143 | 24 | 0.7633 | 1.1838 |
| 1.8571 | 26 | 0.5931 | - |
| 2.0 | 28 | 0.4969 | 1.1800 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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sauntmaxmobile/custom-icon-arieniconX-UI-big-model-graphic-design_v1.0 | sauntmaxmobile | 2024-07-02T10:59:04Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-02T10:57:55Z | ---
license: mit
---
|
igorktech/some-mt | igorktech | 2024-07-02T14:06:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-07-02T10:59:24Z | Entry not found |
Dollce/111 | Dollce | 2024-07-02T11:00:22Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:00:22Z | Entry not found |
oz1115/llama_pre_model | oz1115 | 2024-07-02T11:03:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T11:03:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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oz1115/llama_pre_tokenizer | oz1115 | 2024-07-02T11:03:46Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T11:03:44Z | ---
library_name: transformers
tags: []
---
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Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-1-epoch | Trelis | 2024-07-02T11:03:55Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:211",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T11:03:51Z | ---
base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:211
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How long is the initial period of extra time in the drop-off procedure?
sentences:
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- source_sentence: What happens to the touch count if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team's dead ball line and an attacking player drops the ball?
sentences:
- '##s as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately
makes contact with the ball in flight and the ball is retrieved by an attacking
player, without touching the ground, play continues and the next touch is zero
( 0 ) touch. 12. 3 if a player from the defending team deliberately makes contact
with the ball in flight, propelling it forward and an attacking player, in an
attempt to regain possession, drops the ball, the attacking team retains possession
and the fit playing rules - 5th edition 10 copyright © touch football australia
2020 touch count restarts as zero ( 0 ) touch. 12. 4 if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team ’ s dead ball line and an attacking player, in an attempt to
regain possession drops the ball, a change of possession occurs. 12. 5 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball goes to ground, a change of possession occurs. 12. 6 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball is retrieved by an attacking player, play and the touch count continues.'
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- ( 4 ) defending players are in an onside position or unless directed to so by
the referee. where the number of players on the field from the defending team
falls below four ( 4 ), all players must be in an onside position for a tap to
be taken unless directed to do so by the referee. ruling = the player will be
directed to return to the mark and to take the tap again. 7. 7 the tap to commence
or recommence play must be performed without delay. ruling = a penalty to the
non - offending team at the centre of the halfway line. 8 match duration 8. 1
a match is 40 minutes in duration, consisting of two ( 2 ) x 20 minute halves
with a half time break. 8. 1. 1 there is no time off for injury during a match.
8. 2 local competition and tournament conditions may vary the duration of a match.
8. 3 when time expires, play is to continue until the next touch or dead ball
and end of play is signaled by the referee. 8. 3. 1 should a penalty be awarded
during this period, the penalty is to be taken. 8. 4 if a match is abandoned in
any circumstances other than those referred to in
- source_sentence: What happens if an attacking player fails to perform a rollball
on the mark?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get
touched while in possession of the ball. ruling = a change of possession occurs
at the point of the touch, or if in - goal the nearest point on the seven ( 7
) metre line. 10. 8 if a touch is made in the in - goal area before the ball is
grounded, the player in possession is to perform a rollball seven ( 7 ) metres
from the team ’ s attacking try line, provided it is not the sixth touch and the
player is not half. 10. 9 if a player in possession is touched while on or behind
their defending try line, the touch counts and once the referee sets the mark
seven ( 7 ) metres directly forward of the contact point from the defending team
’ s try line, a rollball is performed. 10. 10 if a player in possession intentionally
makes a touch on an offside defender who is making every effort to retire and
remain out of play, the touch counts. fit playing rules - 5th edition copyright
© touch football australia 2020 9 10. 11 if a touch is made on a player in possession
while the player is juggling the ball in an attempt to maintain control of it,
- source_sentence: What is the result of a match if it is abandoned in circumstances
other than those specified in clause 24.1.6?
sentences:
- 'touch football australia 2020 11 13. 5. 5 when possession changes after the half
is touched or when the half places the ball on or over the try line ; or 13. 5.
6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.
13. 6 a player is to perform a rollball seven ( 7 ) metres in - field under the
following circumstances : 13. 6. 1 when a change of possession takes place due
to a player in possession making contact with the sideline or any ground outside
the field of play, prior to a touch being made ; or 13. 6. 2 when the ball not
in possession of a player makes contact with the sideline or any ground outside
the field of play. 13. 7 a player may not perform a tap in replacement of a rollball.
ruling = the offending team must return to the mark and perform the rollball.
13. 8 an attacking player, other than the player performing the rollball, may
receive the ball at the rollball and shall do so without delay. that player is
referred to as the half. 13. 9 the half may control the ball with a foot prior
to picking up the ball. 13. 10 a player'
- a rollball. half time the break in play between the two halves of a match. imminent
about to occur, it is almost certain to occur. infringement the action of a player
contrary to the rules of the game. in - goal area the area in the field of play
bounded by the sidelines, the try lines and the dead ball lines. there are two
( 2 ), one ( 1 ) at each end of the field of play. see appendix 1. interchange
the act of an on - field player leaving the field of play to be replaced by an
off - field player entering the field of play. interchange area a marked rectangle
for each team on opposite sides of the field of play usually measuring 20 metres
long by no more than five ( 5 ) metres wide, extending ten ( 10 ) metres either
side of the halfway line and not less than one ( 1 ) metre from the sideline.
it is the area in which all off - field players must remain until an interchange
is initiated. see appendix 1. kick strike or propel forcibly with the foot, a
blow or forceful thrust with the foot to the ball. a tap to commence or recommence
play or a penalty tap is not defined as a kick. line markings
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- source_sentence: What is the ruling if an attacking player intentionally passes
the ball to a defending player seeking a rebound or restart of the touch count?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- . see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful
thrust with the foot to the ball. a tap to commence or recommence play or a penalty
tap is not defined as a kick. line markings markings of the field of play. see
appendix 1. link the player beside the wing player. mark ( for a tap ) the centre
of the halfway line for the commencement or recommencement of play, or the position
where a penalty tap is awarded as a result of an infringement. mark ( for a touch
) the position in the field of play where the player in possession was at the
time the touch was made. fit playing rules - 5th edition 2 copyright © touch football
australia 2020 middle the player inside the link player. nta national touch association
as defined in the fit constitution. obstruction a deliberate attempt by either
an attacking or defending player to gain an unfair advantage by interfering with
the opposition to prevent them from gaining a rightful advantage. offside ( attacker
) an attacking player in a position forward of the ball. offside ( defender )
a defending player in a position closer than seven ( 7 ) metres from the mark
of the rollball ; or ten ( 10 ) metres from the mark
- or passed and goes to ground during play, a change of possession results. ruling
= the mark for the change of possession is where the ball makes initial contact
with the ground. 9. 5 if the ball, while still under the control of the half,
contacts the ground in the in - goal area, possession is lost. ruling = play will
restart with a rollball at the nearest point on the seven ( 7 ) metre line. fit
playing rules - 5th edition 8 copyright © touch football australia 2020 9. 6 if
a player mishandles the ball and even if in an effort to gain control, the ball
is accidentally knocked forward into any other player, a change of possession
results. 10 the touch 10. 1 a touch may be made by either a defending player or
a player in possession. 10. 2 a defending player may not claim a touch if contact
has not been made. if a player claims a touch has been made, but the referee is
unsure the touch will count. ruling = a penalty to the attacking team at the point
of the infringement and the offending player sent to the sin bin. 10. 3 players
of both defending and attacking teams are to use the minimum force necessary to
make a touch. players must ensure that the
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision 2430568290bb832d22ad5064f44dd86cf0240142 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-1-epoch")
# Run inference
sentences = [
'What is the ruling if an attacking player intentionally passes the ball to a defending player seeking a rebound or restart of the touch count?',
'ball goes to ground, a change of possession occurs. 12. 6 if a player from the defending team unintentionally makes contact with the ball in flight and the ball is retrieved by an attacking player, play and the touch count continues. 12. 7 a player from the attacking team cannot pass the ball into a defending player intentionally seeking a rebound or a restart of the touch count. ruling = a penalty to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking player is to position on the mark, face the opponent ’ s try line, make a genuine attempt to stand parallel to the sidelines, place the ball on the ground between the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or 13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or 13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending team at the point of the infringement. 13. 2 a player must perform the rollball on the mark. ruling = a penalty to the defending team at the point of the infringement. 13. 3 a player must not perform a voluntary rollball',
'. see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful thrust with the foot to the ball. a tap to commence or recommence play or a penalty tap is not defined as a kick. line markings markings of the field of play. see appendix 1. link the player beside the wing player. mark ( for a tap ) the centre of the halfway line for the commencement or recommencement of play, or the position where a penalty tap is awarded as a result of an infringement. mark ( for a touch ) the position in the field of play where the player in possession was at the time the touch was made. fit playing rules - 5th edition 2 copyright © touch football australia 2020 middle the player inside the link player. nta national touch association as defined in the fit constitution. obstruction a deliberate attempt by either an attacking or defending player to gain an unfair advantage by interfering with the opposition to prevent them from gaining a rightful advantage. offside ( attacker ) an attacking player in a position forward of the ball. offside ( defender ) a defending player in a position closer than seven ( 7 ) metres from the mark of the rollball ; or ten ( 10 ) metres from the mark',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
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### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `num_train_epochs`: 1
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.3
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.3
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.1429 | 2 | 1.0897 | 1.2121 |
| 0.2857 | 4 | 1.2773 | 1.0726 |
| 0.4286 | 6 | 1.1082 | 1.0932 |
| 0.5714 | 8 | 1.4353 | 1.0729 |
| 0.7143 | 10 | 1.4442 | 1.0426 |
| 0.8571 | 12 | 1.0593 | 1.0475 |
| 1.0 | 14 | 1.5769 | 1.0494 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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ayabdi/ah-rvc | ayabdi | 2024-07-02T11:04:15Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:04:06Z | Entry not found |
habulaj/3372531056 | habulaj | 2024-07-02T11:06:13Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:06:10Z | Entry not found |
gglabs/Gemma-kiosk-scenario-5-epoch | gglabs | 2024-07-02T11:09:47Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:gemmathon/gemma-2b-ko-dev-pbmt192",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T11:07:19Z | ---
base_model: gemmathon/gemma-2b-ko-dev-pbmt192
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
---
# Uploaded model
- **Developed by:** gglabs
- **License:** apache-2.0
- **Finetuned from model :** gemmathon/gemma-2b-ko-dev-pbmt192
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
chrischang80/code-search-net-tokenizer | chrischang80 | 2024-07-02T11:07:25Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T11:07:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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- **Developed by:** [More Information Needed]
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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.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
SantReeo/HempEssentialGummiesCanada | SantReeo | 2024-07-02T11:08:02Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:07:51Z | ➥✅ Official Website:Hemp Essential Gummies Canada
➥✅ Product Name: Hemp Essential Gummies Canada
➥✅ Rating: ★★★★☆ (4.5/5.0)
➥✅ Side Effects: No Major Side Effects
➥✅ Availability: In Stock #1 Product in the Canada
https://www.facebook.com/HempEssentialGummiesCA/
https://www.facebook.com/EssentialHempGummiesCanada/
https://www.facebook.com/HempEssentialGummiesCanada/
https://sites.google.com/view/hemp-essential-gummies-ca-cost/home
https://sites.google.com/view/try-hemp-essential-gummies-ca/home
https://hemp-essential-gummies-canada.company.site/
https://medium.com/@erikanayaw/hemp-essential-gummies-canada-fake-complaints-or-ingredients-really-work-87f80b3e3418
https://medium.com/@erikanayaw/hemp-essential-gummies-canada-new-report-does-it-work-what-they-wont-tell-you-before-buying-7cb004323ec6
https://hemp-essential-gummies-ca-cost.jimdosite.com/
https://hemp-essential-gummies-canada-work.jimdosite.com/
https://hempessentialgummiescacost.blogspot.com/2024/03/hemp-essential-gummies-canada.html
https://essential-hemp-gummies-canada.jimdosite.com/
https://hemp-essential-gummies-canada.webflow.io/ |
HARSHU550/Emotions | HARSHU550 | 2024-07-02T13:22:49Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"roberta",
"text-classification",
"distilroberta",
"sentiment",
"emotion",
"twitter",
"reddit",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T11:09:07Z | ---
language: "en"
tags:
- distilroberta
- sentiment
- emotion
- twitter
- reddit
widget:
- text: "Oh wow. I didn't know that."
- text: "This movie always makes me cry.."
- text: "Oh Happy Day"
---
# Emotion English DistilRoBERTa-base
# Description ℹ
With this model, you can classify emotions in English text data. The model was trained on 6 diverse datasets (see Appendix below) and predicts Ekman's 6 basic emotions, plus a neutral class:
1) anger 🤬
2) disgust 🤢
3) fear 😨
4) joy 😀
5) neutral 😐
6) sadness 😭
7) surprise 😲
The model is a fine-tuned checkpoint of [DistilRoBERTa-base](https://huggingface.co/distilroberta-base). For a 'non-distilled' emotion model, please refer to the model card of the [RoBERTa-large](https://huggingface.co/j-hartmann/emotion-english-roberta-large) version.
# Application 🚀
a) Run emotion model with 3 lines of code on single text example using Hugging Face's pipeline command on Google Colab:
[](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/simple_emotion_pipeline.ipynb)
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
classifier("I love this!")
```
```python
Output:
[[{'label': 'anger', 'score': 0.004419783595949411},
{'label': 'disgust', 'score': 0.0016119900392368436},
{'label': 'fear', 'score': 0.0004138521908316761},
{'label': 'joy', 'score': 0.9771687984466553},
{'label': 'neutral', 'score': 0.005764586851000786},
{'label': 'sadness', 'score': 0.002092392183840275},
{'label': 'surprise', 'score': 0.008528684265911579}]]
```
b) Run emotion model on multiple examples and full datasets (e.g., .csv files) on Google Colab:
[](https://colab.research.google.com/github/j-hartmann/emotion-english-distilroberta-base/blob/main/emotion_prediction_example.ipynb)
# Contact 💻
Please reach out to [[email protected]](mailto:[email protected]) if you have any questions or feedback.
Thanks to Samuel Domdey and [chrsiebert](https://huggingface.co/siebert) for their support in making this model available.
# Reference ✅
For attribution, please cite the following reference if you use this model. A working paper will be available soon.
```
Jochen Hartmann, "Emotion English DistilRoBERTa-base". https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/, 2022.
```
BibTex citation:
```
@misc{hartmann2022emotionenglish,
author={Hartmann, Jochen},
title={Emotion English DistilRoBERTa-base},
year={2022},
howpublished = {\url{https://huggingface.co/j-hartmann/emotion-english-distilroberta-base/}},
}
```
# Appendix 📚
Please find an overview of the datasets used for training below. All datasets contain English text. The table summarizes which emotions are available in each of the datasets. The datasets represent a diverse collection of text types. Specifically, they contain emotion labels for texts from Twitter, Reddit, student self-reports, and utterances from TV dialogues. As MELD (Multimodal EmotionLines Dataset) extends the popular EmotionLines dataset, EmotionLines itself is not included here.
|Name|anger|disgust|fear|joy|neutral|sadness|surprise|
|---|---|---|---|---|---|---|---|
|Crowdflower (2016)|Yes|-|-|Yes|Yes|Yes|Yes|
|Emotion Dataset, Elvis et al. (2018)|Yes|-|Yes|Yes|-|Yes|Yes|
|GoEmotions, Demszky et al. (2020)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|ISEAR, Vikash (2018)|Yes|Yes|Yes|Yes|-|Yes|-|
|MELD, Poria et al. (2019)|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|SemEval-2018, EI-reg, Mohammad et al. (2018) |Yes|-|Yes|Yes|-|Yes|-|
The model is trained on a balanced subset from the datasets listed above (2,811 observations per emotion, i.e., nearly 20k observations in total). 80% of this balanced subset is used for training and 20% for evaluation. The evaluation accuracy is 66% (vs. the random-chance baseline of 1/7 = 14%).
# Scientific Applications 📖
Below you can find a list of papers using "Emotion English DistilRoBERTa-base". If you would like your paper to be added to the list, please send me an email.
- Butt, S., Sharma, S., Sharma, R., Sidorov, G., & Gelbukh, A. (2022). What goes on inside rumour and non-rumour tweets and their reactions: A Psycholinguistic Analyses. Computers in Human Behavior, 107345.
- Kuang, Z., Zong, S., Zhang, J., Chen, J., & Liu, H. (2022). Music-to-Text Synaesthesia: Generating Descriptive Text from Music Recordings. arXiv preprint arXiv:2210.00434.
- Rozado, D., Hughes, R., & Halberstadt, J. (2022). Longitudinal analysis of sentiment and emotion in news media headlines using automated labelling with Transformer language models. Plos one, 17(10), e0276367. |
Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-1-epoch-scale-20 | Trelis | 2024-07-02T11:10:02Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:211",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T11:09:57Z | ---
base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:211
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How long is the initial period of extra time in the drop-off procedure?
sentences:
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- source_sentence: What happens to the touch count if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team's dead ball line and an attacking player drops the ball?
sentences:
- '##s as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately
makes contact with the ball in flight and the ball is retrieved by an attacking
player, without touching the ground, play continues and the next touch is zero
( 0 ) touch. 12. 3 if a player from the defending team deliberately makes contact
with the ball in flight, propelling it forward and an attacking player, in an
attempt to regain possession, drops the ball, the attacking team retains possession
and the fit playing rules - 5th edition 10 copyright © touch football australia
2020 touch count restarts as zero ( 0 ) touch. 12. 4 if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team ’ s dead ball line and an attacking player, in an attempt to
regain possession drops the ball, a change of possession occurs. 12. 5 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball goes to ground, a change of possession occurs. 12. 6 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball is retrieved by an attacking player, play and the touch count continues.'
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- ( 4 ) defending players are in an onside position or unless directed to so by
the referee. where the number of players on the field from the defending team
falls below four ( 4 ), all players must be in an onside position for a tap to
be taken unless directed to do so by the referee. ruling = the player will be
directed to return to the mark and to take the tap again. 7. 7 the tap to commence
or recommence play must be performed without delay. ruling = a penalty to the
non - offending team at the centre of the halfway line. 8 match duration 8. 1
a match is 40 minutes in duration, consisting of two ( 2 ) x 20 minute halves
with a half time break. 8. 1. 1 there is no time off for injury during a match.
8. 2 local competition and tournament conditions may vary the duration of a match.
8. 3 when time expires, play is to continue until the next touch or dead ball
and end of play is signaled by the referee. 8. 3. 1 should a penalty be awarded
during this period, the penalty is to be taken. 8. 4 if a match is abandoned in
any circumstances other than those referred to in
- source_sentence: What happens if an attacking player fails to perform a rollball
on the mark?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get
touched while in possession of the ball. ruling = a change of possession occurs
at the point of the touch, or if in - goal the nearest point on the seven ( 7
) metre line. 10. 8 if a touch is made in the in - goal area before the ball is
grounded, the player in possession is to perform a rollball seven ( 7 ) metres
from the team ’ s attacking try line, provided it is not the sixth touch and the
player is not half. 10. 9 if a player in possession is touched while on or behind
their defending try line, the touch counts and once the referee sets the mark
seven ( 7 ) metres directly forward of the contact point from the defending team
’ s try line, a rollball is performed. 10. 10 if a player in possession intentionally
makes a touch on an offside defender who is making every effort to retire and
remain out of play, the touch counts. fit playing rules - 5th edition copyright
© touch football australia 2020 9 10. 11 if a touch is made on a player in possession
while the player is juggling the ball in an attempt to maintain control of it,
- source_sentence: What is the result of a match if it is abandoned in circumstances
other than those specified in clause 24.1.6?
sentences:
- 'touch football australia 2020 11 13. 5. 5 when possession changes after the half
is touched or when the half places the ball on or over the try line ; or 13. 5.
6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.
13. 6 a player is to perform a rollball seven ( 7 ) metres in - field under the
following circumstances : 13. 6. 1 when a change of possession takes place due
to a player in possession making contact with the sideline or any ground outside
the field of play, prior to a touch being made ; or 13. 6. 2 when the ball not
in possession of a player makes contact with the sideline or any ground outside
the field of play. 13. 7 a player may not perform a tap in replacement of a rollball.
ruling = the offending team must return to the mark and perform the rollball.
13. 8 an attacking player, other than the player performing the rollball, may
receive the ball at the rollball and shall do so without delay. that player is
referred to as the half. 13. 9 the half may control the ball with a foot prior
to picking up the ball. 13. 10 a player'
- a rollball. half time the break in play between the two halves of a match. imminent
about to occur, it is almost certain to occur. infringement the action of a player
contrary to the rules of the game. in - goal area the area in the field of play
bounded by the sidelines, the try lines and the dead ball lines. there are two
( 2 ), one ( 1 ) at each end of the field of play. see appendix 1. interchange
the act of an on - field player leaving the field of play to be replaced by an
off - field player entering the field of play. interchange area a marked rectangle
for each team on opposite sides of the field of play usually measuring 20 metres
long by no more than five ( 5 ) metres wide, extending ten ( 10 ) metres either
side of the halfway line and not less than one ( 1 ) metre from the sideline.
it is the area in which all off - field players must remain until an interchange
is initiated. see appendix 1. kick strike or propel forcibly with the foot, a
blow or forceful thrust with the foot to the ball. a tap to commence or recommence
play or a penalty tap is not defined as a kick. line markings
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- source_sentence: What is the ruling if an attacking player intentionally passes
the ball to a defending player seeking a rebound or restart of the touch count?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- . see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful
thrust with the foot to the ball. a tap to commence or recommence play or a penalty
tap is not defined as a kick. line markings markings of the field of play. see
appendix 1. link the player beside the wing player. mark ( for a tap ) the centre
of the halfway line for the commencement or recommencement of play, or the position
where a penalty tap is awarded as a result of an infringement. mark ( for a touch
) the position in the field of play where the player in possession was at the
time the touch was made. fit playing rules - 5th edition 2 copyright © touch football
australia 2020 middle the player inside the link player. nta national touch association
as defined in the fit constitution. obstruction a deliberate attempt by either
an attacking or defending player to gain an unfair advantage by interfering with
the opposition to prevent them from gaining a rightful advantage. offside ( attacker
) an attacking player in a position forward of the ball. offside ( defender )
a defending player in a position closer than seven ( 7 ) metres from the mark
of the rollball ; or ten ( 10 ) metres from the mark
- or passed and goes to ground during play, a change of possession results. ruling
= the mark for the change of possession is where the ball makes initial contact
with the ground. 9. 5 if the ball, while still under the control of the half,
contacts the ground in the in - goal area, possession is lost. ruling = play will
restart with a rollball at the nearest point on the seven ( 7 ) metre line. fit
playing rules - 5th edition 8 copyright © touch football australia 2020 9. 6 if
a player mishandles the ball and even if in an effort to gain control, the ball
is accidentally knocked forward into any other player, a change of possession
results. 10 the touch 10. 1 a touch may be made by either a defending player or
a player in possession. 10. 2 a defending player may not claim a touch if contact
has not been made. if a player claims a touch has been made, but the referee is
unsure the touch will count. ruling = a penalty to the attacking team at the point
of the infringement and the offending player sent to the sin bin. 10. 3 players
of both defending and attacking teams are to use the minimum force necessary to
make a touch. players must ensure that the
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision 2430568290bb832d22ad5064f44dd86cf0240142 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-1-epoch-scale-20")
# Run inference
sentences = [
'What is the ruling if an attacking player intentionally passes the ball to a defending player seeking a rebound or restart of the touch count?',
'ball goes to ground, a change of possession occurs. 12. 6 if a player from the defending team unintentionally makes contact with the ball in flight and the ball is retrieved by an attacking player, play and the touch count continues. 12. 7 a player from the attacking team cannot pass the ball into a defending player intentionally seeking a rebound or a restart of the touch count. ruling = a penalty to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking player is to position on the mark, face the opponent ’ s try line, make a genuine attempt to stand parallel to the sidelines, place the ball on the ground between the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or 13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or 13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending team at the point of the infringement. 13. 2 a player must perform the rollball on the mark. ruling = a penalty to the defending team at the point of the infringement. 13. 3 a player must not perform a voluntary rollball',
'. see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful thrust with the foot to the ball. a tap to commence or recommence play or a penalty tap is not defined as a kick. line markings markings of the field of play. see appendix 1. link the player beside the wing player. mark ( for a tap ) the centre of the halfway line for the commencement or recommencement of play, or the position where a penalty tap is awarded as a result of an infringement. mark ( for a touch ) the position in the field of play where the player in possession was at the time the touch was made. fit playing rules - 5th edition 2 copyright © touch football australia 2020 middle the player inside the link player. nta national touch association as defined in the fit constitution. obstruction a deliberate attempt by either an attacking or defending player to gain an unfair advantage by interfering with the opposition to prevent them from gaining a rightful advantage. offside ( attacker ) an attacking player in a position forward of the ball. offside ( defender ) a defending player in a position closer than seven ( 7 ) metres from the mark of the rollball ; or ten ( 10 ) metres from the mark',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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</details>
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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### Out-of-Scope Use
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## Bias, Risks and Limitations
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `learning_rate`: 0.0001
- `num_train_epochs`: 2
- `lr_scheduler_type`: constant
- `warmup_ratio`: 0.3
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 0.0001
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: constant
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.3
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.1429 | 2 | 1.0352 | - |
| 0.2857 | 4 | 0.983 | 1.0554 |
| 0.4286 | 6 | 1.0573 | - |
| 0.5714 | 8 | 1.5167 | 1.1372 |
| 0.7143 | 10 | 1.4807 | - |
| 0.8571 | 12 | 1.0505 | 1.1214 |
| 1.0 | 14 | 1.1143 | - |
| 1.1429 | 16 | 0.7394 | 1.1747 |
| 1.2857 | 18 | 0.4551 | - |
| 1.4286 | 20 | 0.6974 | 1.2331 |
| 1.5714 | 22 | 0.5394 | - |
| 1.7143 | 24 | 0.7328 | 1.3571 |
| 1.8571 | 26 | 0.5923 | - |
| 2.0 | 28 | 0.6218 | 1.2917 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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bezzam/diffusercam-mirflickr-unet4M-unrolled-admm10-unet4M | bezzam | 2024-07-02T11:10:43Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-07-02T11:10:12Z | ---
license: mit
---
|
ShapeKapseln33/Biovancia55 | ShapeKapseln33 | 2024-07-02T11:11:48Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:10:36Z | Biovancia France Avis Neo-Collagen associe du collagène breveté Verisol® et de la vitamine C pour la beauté de la peau, des cheveux et des ongles.
**[Cliquez ici pour acheter maintenant sur le site officiel Biovancia](https://adtocart.xyz/biovancia-fr)**
"Le produit que j'ai acheté Nutra Mag B6 , est un produit de tres grande qualité de part son dosage. Le prix est tres raisonnable. Je vous le recommande" Philippe D. à Le Havre (72)
"Ça fait 2 semaines que je prends Nutramag B6 et je ressens déjà les effets positifs c'est super bon produit" Daniel B. à Herimenil (54)
"En 48 heures j’ai ressenti une amélioration les douleurs dans mes mâchoires ont presque disparue" Patrick N. à Roquefort les Pins (06)
"Je suis en cours d experimentation, je me sens plus tonique" Annie L. à Toulouse (31)
"[...] j ai commande nutramag B6 je suis admirablement surprit de l'efficacité du produit qui m'apporte sur ma santé . pour avoir plus impact sur ma santé je le combiné avec d'autre produit de la même gamme c'est pour sa que je prendre un abonnement annuelle je vous recommande vivement se produit c'es le top" Nicolas C. à Saint Germain Lambron (63)
"Grâce à votre magnésium, la qualité de mon sommeil s'est améliorée et je n'ai plus de crampes. Tout cela sans problème intestinal. Merci à vous" Joelle R.
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**[Cliquez ici pour acheter maintenant sur le site officiel Biovancia](https://adtocart.xyz/biovancia-fr)**
## Vos avis sur CurQ10
« Bonjour , je suis nouvelle cliente de votre laboratoire et pour le moment je suis satisfaite du produit que je viens de commander . J'ai 85 ans et je souffre énormément du dos suite à une chute il y a 4 ans où je me suis fracturée une vertèbre […] je souffre d'une tendinopathie du moyen fessier gauche , très difficile à faire disparaitre. Je ne me soigne qu'avec des produits naturels , de plus j'ai une hernie hiatale qui me provoque des remontés acides . Votre Curcuma diminue considérablement mes douleurs et j'ai oublié Dafalgan et Ibuprofène . je fais confiance à votre laboratoire . à bientôt » - Nicole B. à Ajaccio (20)
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"Depuis que je l'utilise, j'ai remarqué une diminution des douleurs dans le corps et surtout de l'énergie qui revient. C'est très récent et j'espère que ce mieux va se poursuivre." Josée L., à Sarry (51)
"Très bien bon niveau énergétique" Marie-Laure D. à Versailles (78)
"Bien, je suis content" Edward N. à St Priest Taurion
"Très bien. Bon produit." Michel R. à Sète (34)
"Agréablement surprise de la gestion très professionnelle de l'achat à la livraison à la qualité des produits ..." Nora R. à Drancy (93)
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« voilà 5 jours que je le prends, pas de douleurs articulaires. » Bernard S. à Rouvres les vignes (10)
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**[Cliquez ici pour acheter maintenant sur le site officiel Biovancia](https://adtocart.xyz/biovancia-fr)**
|
vinglivskt/fine_tuned_model_gpt_2 | vinglivskt | 2024-07-02T11:48:11Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-07-02T11:11:36Z | Entry not found |
kheopss/kheops_fr_en_epoch1_2bits_GPTQ | kheopss | 2024-07-02T11:14:01Z | 0 | 0 | transformers | [
"transformers",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"2-bit",
"gptq",
"region:us"
] | text-generation | 2024-07-02T11:12:46Z | Entry not found |
diggum/rwcopy1epoch | diggum | 2024-07-02T11:20:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-07-02T11:13:09Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** diggum
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stojchet/1b1c95a3d2cf3aea9520af0a64ac2b76 | stojchet | 2024-07-02T11:18:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:deepseek-ai/deepseek-coder-1.3b-base",
"license:other",
"region:us"
] | null | 2024-07-02T11:13:16Z | ---
base_model: deepseek-ai/deepseek-coder-1.3b-base
datasets:
- generator
library_name: peft
license: other
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: 1b1c95a3d2cf3aea9520af0a64ac2b76
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. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/stojchets/huggingface/runs/gf9799bw)
# 1b1c95a3d2cf3aea9520af0a64ac2b76
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2408
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.0354 | 1.0 | 1 | 1.2408 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.43.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.19.2
- Tokenizers 0.19.1 |
srinivasan-sridhar28/Tiny-Storyteller-GPT2 | srinivasan-sridhar28 | 2024-07-02T11:13:17Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:13:17Z | Entry not found |
warachat/music_generate_v1 | warachat | 2024-07-02T11:13:44Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:13:44Z | Entry not found |
blackhole33/whisper-medium-uz_V2 | blackhole33 | 2024-07-02T11:13:44Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:13:44Z | Entry not found |
Danikdsa/jongho | Danikdsa | 2024-07-02T11:46:03Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | 2024-07-02T11:14:09Z | ---
license: openrail
---
|
J4bb4wukis/exercise-9-relation-classifier | J4bb4wukis | 2024-07-02T11:15:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-07-02T11:14:29Z | ---
license: apache-2.0
---
|
RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf | RichardErkhov | 2024-07-02T11:18:50Z | 0 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-07-02T11:15:39Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
smol_llama-220M-open_instruct - GGUF
- Model creator: https://huggingface.co/BEE-spoke-data/
- Original model: https://huggingface.co/BEE-spoke-data/smol_llama-220M-open_instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [smol_llama-220M-open_instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q2_K.gguf) | Q2_K | 0.09GB |
| [smol_llama-220M-open_instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.IQ3_XS.gguf) | IQ3_XS | 0.1GB |
| [smol_llama-220M-open_instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.IQ3_S.gguf) | IQ3_S | 0.1GB |
| [smol_llama-220M-open_instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q3_K_S.gguf) | Q3_K_S | 0.1GB |
| [smol_llama-220M-open_instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.IQ3_M.gguf) | IQ3_M | 0.1GB |
| [smol_llama-220M-open_instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q3_K.gguf) | Q3_K | 0.11GB |
| [smol_llama-220M-open_instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q3_K_M.gguf) | Q3_K_M | 0.11GB |
| [smol_llama-220M-open_instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q3_K_L.gguf) | Q3_K_L | 0.11GB |
| [smol_llama-220M-open_instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.IQ4_XS.gguf) | IQ4_XS | 0.12GB |
| [smol_llama-220M-open_instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q4_0.gguf) | Q4_0 | 0.12GB |
| [smol_llama-220M-open_instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.IQ4_NL.gguf) | IQ4_NL | 0.12GB |
| [smol_llama-220M-open_instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q4_K_S.gguf) | Q4_K_S | 0.12GB |
| [smol_llama-220M-open_instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q4_K.gguf) | Q4_K | 0.13GB |
| [smol_llama-220M-open_instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q4_K_M.gguf) | Q4_K_M | 0.13GB |
| [smol_llama-220M-open_instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q4_1.gguf) | Q4_1 | 0.13GB |
| [smol_llama-220M-open_instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q5_0.gguf) | Q5_0 | 0.14GB |
| [smol_llama-220M-open_instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q5_K_S.gguf) | Q5_K_S | 0.14GB |
| [smol_llama-220M-open_instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q5_K.gguf) | Q5_K | 0.15GB |
| [smol_llama-220M-open_instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q5_K_M.gguf) | Q5_K_M | 0.15GB |
| [smol_llama-220M-open_instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q5_1.gguf) | Q5_1 | 0.16GB |
| [smol_llama-220M-open_instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q6_K.gguf) | Q6_K | 0.17GB |
| [smol_llama-220M-open_instruct.Q8_0.gguf](https://huggingface.co/RichardErkhov/BEE-spoke-data_-_smol_llama-220M-open_instruct-gguf/blob/main/smol_llama-220M-open_instruct.Q8_0.gguf) | Q8_0 | 0.22GB |
Original model description:
---
license: apache-2.0
datasets:
- VMware/open-instruct
base_model: BEE-spoke-data/smol_llama-220M-GQA
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.04
no_repeat_ngram_size: 6
epsilon_cutoff: 0.0006
widget:
- text: "Below is an instruction that describes a task, paired with an input that\
\ provides further context. Write a response that appropriately completes the\
\ request. \n \n### Instruction: \n \nWrite an ode to Chipotle burritos.\
\ \n \n### Response: \n"
example_title: burritos
model-index:
- name: smol_llama-220M-open_instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 25.0
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 29.71
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 26.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 44.06
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 50.28
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=BEE-spoke-data/smol_llama-220M-open_instruct
name: Open LLM Leaderboard
---
# BEE-spoke-data/smol_llama-220M-open_instruct
> Please note that this is an experiment, and the model has limitations because it is smol.
prompt format is alpaca.
```
Below is an instruction that describes a task, paired with an input that
provides further context. Write a response that appropriately completes
the request.
### Instruction:
How can I increase my meme production/output? Currently, I only create them in ancient babylonian which is time consuming.
### Response:
```
This was **not** trained using a separate 'inputs' field (as `VMware/open-instruct` doesn't use one).
## Example
Output on the text above ^. The inference API is set to sample with low temp so you should see (_at least slightly_) different generations each time.
Note that the inference API parameters used here are an initial educated guess, and may be updated over time:
```yml
inference:
parameters:
do_sample: true
renormalize_logits: true
temperature: 0.25
top_p: 0.95
top_k: 50
min_new_tokens: 2
max_new_tokens: 96
repetition_penalty: 1.04
no_repeat_ngram_size: 6
epsilon_cutoff: 0.0006
```
Feel free to experiment with the parameters using the model in Python and let us know if you have improved results with other params!
## Data
This was trained on `VMware/open-instruct` so do whatever you want, provided it falls under the base apache-2.0 license :)
---
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_BEE-spoke-data__smol_llama-220M-open_instruct)
| Metric |Value|
|---------------------------------|----:|
|Avg. |29.19|
|AI2 Reasoning Challenge (25-Shot)|25.00|
|HellaSwag (10-Shot) |29.71|
|MMLU (5-Shot) |26.11|
|TruthfulQA (0-shot) |44.06|
|Winogrande (5-shot) |50.28|
|GSM8k (5-shot) | 0.00|
|
Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-2-cos-epoch-s20 | Trelis | 2024-07-02T11:16:12Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:211",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-07-02T11:16:08Z | ---
base_model: sentence-transformers/multi-qa-MiniLM-L6-cos-v1
datasets: []
language: []
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:211
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: How long is the initial period of extra time in the drop-off procedure?
sentences:
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- required in drawn matches, the following drop - off procedure is used to determine
a winner. 24. 1. 1 each team will reduce their on - field team to four ( 4 ) players
and within 60 seconds take up a position to restart play from the halfway line,
defending the same end of the field as at the end of play. 24. 1. 2 the drop -
off commences with a tap from the centre of the halfway line by the team that
did not commence the match with possession. 24. 1. 3 the drop - off will commence
with a two ( 2 ) minute period of extra time. 24. 1. 4 should a team be leading
at the expiration of the two ( 2 ) minute period of extra time then that team
will be declared the winner and match complete. 24. 1. 5 should neither team be
leading at the expiration of two ( 2 ) minutes, a signal is given and the match
will pause at the next touch or dead ball. each team will then remove another
player from the field of play. 24. 1. 6 the match will recommence immediately
after the players have left the field at the same place where it paused ( i. e.
the team retains possession at the
- source_sentence: What happens to the touch count if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team's dead ball line and an attacking player drops the ball?
sentences:
- '##s as zero ( 0 ) touch. 12. 2 if a player from the defending team deliberately
makes contact with the ball in flight and the ball is retrieved by an attacking
player, without touching the ground, play continues and the next touch is zero
( 0 ) touch. 12. 3 if a player from the defending team deliberately makes contact
with the ball in flight, propelling it forward and an attacking player, in an
attempt to regain possession, drops the ball, the attacking team retains possession
and the fit playing rules - 5th edition 10 copyright © touch football australia
2020 touch count restarts as zero ( 0 ) touch. 12. 4 if a player from the defending
team deliberately makes contact with the ball in flight, propelling it towards
the defending team ’ s dead ball line and an attacking player, in an attempt to
regain possession drops the ball, a change of possession occurs. 12. 5 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball goes to ground, a change of possession occurs. 12. 6 if a player
from the defending team unintentionally makes contact with the ball in flight
and the ball is retrieved by an attacking player, play and the touch count continues.'
- made but must be included in relevant competition, event or tournament conditions
1. 2 line markings should be 4cm in width but must be no less than 2. 5cm. line
markings are to be laid out as shown in appendix 1 - the field of play. sidelines
extend seven ( 7 ) metres beyond the try lines to join the dead ball lines and
define the in - goal areas which measure fifty ( 50 ) metres wide by seven ( 7
) metres in length. 1. 3 the interchange areas are located no closer than one
( 1 ) metre from each sideline. 1. 4 suitably sized markers, cones or corner posts
of a distinguishing colour and made from safe and pliable material should be positioned
at the intersections of the sideline and halfway line and the sideline and the
try line. 1. 4. 1 markers, cones or corner posts placed on the junction of the
sideline and try line are deemed to be in the field of play. 1. 4. 2 all other
markers or cones are deemed to be out of the field of play. 1. 5 the standard
playing surface is grass. other surfaces including synthetic grass may be used
but shall be subject to nta approved standards. 1. 6 the field of play
- ( 4 ) defending players are in an onside position or unless directed to so by
the referee. where the number of players on the field from the defending team
falls below four ( 4 ), all players must be in an onside position for a tap to
be taken unless directed to do so by the referee. ruling = the player will be
directed to return to the mark and to take the tap again. 7. 7 the tap to commence
or recommence play must be performed without delay. ruling = a penalty to the
non - offending team at the centre of the halfway line. 8 match duration 8. 1
a match is 40 minutes in duration, consisting of two ( 2 ) x 20 minute halves
with a half time break. 8. 1. 1 there is no time off for injury during a match.
8. 2 local competition and tournament conditions may vary the duration of a match.
8. 3 when time expires, play is to continue until the next touch or dead ball
and end of play is signaled by the referee. 8. 3. 1 should a penalty be awarded
during this period, the penalty is to be taken. 8. 4 if a match is abandoned in
any circumstances other than those referred to in
- source_sentence: What happens if an attacking player fails to perform a rollball
on the mark?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- ( 7 ) metre line. 10. 7 the half may pass or run with the ball but cannot get
touched while in possession of the ball. ruling = a change of possession occurs
at the point of the touch, or if in - goal the nearest point on the seven ( 7
) metre line. 10. 8 if a touch is made in the in - goal area before the ball is
grounded, the player in possession is to perform a rollball seven ( 7 ) metres
from the team ’ s attacking try line, provided it is not the sixth touch and the
player is not half. 10. 9 if a player in possession is touched while on or behind
their defending try line, the touch counts and once the referee sets the mark
seven ( 7 ) metres directly forward of the contact point from the defending team
’ s try line, a rollball is performed. 10. 10 if a player in possession intentionally
makes a touch on an offside defender who is making every effort to retire and
remain out of play, the touch counts. fit playing rules - 5th edition copyright
© touch football australia 2020 9 10. 11 if a touch is made on a player in possession
while the player is juggling the ball in an attempt to maintain control of it,
- source_sentence: What is the result of a match if it is abandoned in circumstances
other than those specified in clause 24.1.6?
sentences:
- 'touch football australia 2020 11 13. 5. 5 when possession changes after the half
is touched or when the half places the ball on or over the try line ; or 13. 5.
6 in replacement of a penalty tap ; or 13. 5. 7 when so directed by the referee.
13. 6 a player is to perform a rollball seven ( 7 ) metres in - field under the
following circumstances : 13. 6. 1 when a change of possession takes place due
to a player in possession making contact with the sideline or any ground outside
the field of play, prior to a touch being made ; or 13. 6. 2 when the ball not
in possession of a player makes contact with the sideline or any ground outside
the field of play. 13. 7 a player may not perform a tap in replacement of a rollball.
ruling = the offending team must return to the mark and perform the rollball.
13. 8 an attacking player, other than the player performing the rollball, may
receive the ball at the rollball and shall do so without delay. that player is
referred to as the half. 13. 9 the half may control the ball with a foot prior
to picking up the ball. 13. 10 a player'
- a rollball. half time the break in play between the two halves of a match. imminent
about to occur, it is almost certain to occur. infringement the action of a player
contrary to the rules of the game. in - goal area the area in the field of play
bounded by the sidelines, the try lines and the dead ball lines. there are two
( 2 ), one ( 1 ) at each end of the field of play. see appendix 1. interchange
the act of an on - field player leaving the field of play to be replaced by an
off - field player entering the field of play. interchange area a marked rectangle
for each team on opposite sides of the field of play usually measuring 20 metres
long by no more than five ( 5 ) metres wide, extending ten ( 10 ) metres either
side of the halfway line and not less than one ( 1 ) metre from the sideline.
it is the area in which all off - field players must remain until an interchange
is initiated. see appendix 1. kick strike or propel forcibly with the foot, a
blow or forceful thrust with the foot to the ball. a tap to commence or recommence
play or a penalty tap is not defined as a kick. line markings
- dead ball and end of play is signaled by the referee. 8. 3. 1 should a penalty
be awarded during this period, the penalty is to be taken. 8. 4 if a match is
abandoned in any circumstances other than those referred to in clause 24. 1. 6
the nta or nta competition provider in its sole discretion shall determine the
result of the match. 9 possession 9. 1 the team with the ball is entitled to six
( 6 ) touches prior to a change of possession. 9. 2 on the change of possession
due to an intercept, the first touch will be zero ( 0 ) touch. 9. 3 following
the sixth touch or a loss of possession due to any other means, the ball must
be returned to the mark without delay. ruling = a deliberate delay in the changeover
procedure will result in a penalty awarded to the non - offending team ten ( 10
) metres forward of the mark for the change of possession. 9. 4 if the ball is
dropped or passed and goes to ground during play, a change of possession results.
ruling = the mark for the change of possession is where the ball makes initial
contact with the ground. 9. 5 if the ball, while still under the control of the
half
- source_sentence: What is the ruling if an attacking player intentionally passes
the ball to a defending player seeking a rebound or restart of the touch count?
sentences:
- 'ball goes to ground, a change of possession occurs. 12. 6 if a player from the
defending team unintentionally makes contact with the ball in flight and the ball
is retrieved by an attacking player, play and the touch count continues. 12. 7
a player from the attacking team cannot pass the ball into a defending player
intentionally seeking a rebound or a restart of the touch count. ruling = a penalty
to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking
player is to position on the mark, face the opponent ’ s try line, make a genuine
attempt to stand parallel to the sidelines, place the ball on the ground between
the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or
13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or
13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending
team at the point of the infringement. 13. 2 a player must perform the rollball
on the mark. ruling = a penalty to the defending team at the point of the infringement.
13. 3 a player must not perform a voluntary rollball'
- . see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful
thrust with the foot to the ball. a tap to commence or recommence play or a penalty
tap is not defined as a kick. line markings markings of the field of play. see
appendix 1. link the player beside the wing player. mark ( for a tap ) the centre
of the halfway line for the commencement or recommencement of play, or the position
where a penalty tap is awarded as a result of an infringement. mark ( for a touch
) the position in the field of play where the player in possession was at the
time the touch was made. fit playing rules - 5th edition 2 copyright © touch football
australia 2020 middle the player inside the link player. nta national touch association
as defined in the fit constitution. obstruction a deliberate attempt by either
an attacking or defending player to gain an unfair advantage by interfering with
the opposition to prevent them from gaining a rightful advantage. offside ( attacker
) an attacking player in a position forward of the ball. offside ( defender )
a defending player in a position closer than seven ( 7 ) metres from the mark
of the rollball ; or ten ( 10 ) metres from the mark
- or passed and goes to ground during play, a change of possession results. ruling
= the mark for the change of possession is where the ball makes initial contact
with the ground. 9. 5 if the ball, while still under the control of the half,
contacts the ground in the in - goal area, possession is lost. ruling = play will
restart with a rollball at the nearest point on the seven ( 7 ) metre line. fit
playing rules - 5th edition 8 copyright © touch football australia 2020 9. 6 if
a player mishandles the ball and even if in an effort to gain control, the ball
is accidentally knocked forward into any other player, a change of possession
results. 10 the touch 10. 1 a touch may be made by either a defending player or
a player in possession. 10. 2 a defending player may not claim a touch if contact
has not been made. if a player claims a touch has been made, but the referee is
unsure the touch will count. ruling = a penalty to the attacking team at the point
of the infringement and the offending player sent to the sin bin. 10. 3 players
of both defending and attacking teams are to use the minimum force necessary to
make a touch. players must ensure that the
---
# SentenceTransformer based on sentence-transformers/multi-qa-MiniLM-L6-cos-v1
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1) <!-- at revision 2430568290bb832d22ad5064f44dd86cf0240142 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, '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, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Trelis/multi-qa-MiniLM-L6-cos-v1-ft-pairs-2-cos-epoch-s20")
# Run inference
sentences = [
'What is the ruling if an attacking player intentionally passes the ball to a defending player seeking a rebound or restart of the touch count?',
'ball goes to ground, a change of possession occurs. 12. 6 if a player from the defending team unintentionally makes contact with the ball in flight and the ball is retrieved by an attacking player, play and the touch count continues. 12. 7 a player from the attacking team cannot pass the ball into a defending player intentionally seeking a rebound or a restart of the touch count. ruling = a penalty to the defending team at the point of the pass. 13 the rollball 13. 1 the attacking player is to position on the mark, face the opponent ’ s try line, make a genuine attempt to stand parallel to the sidelines, place the ball on the ground between the feet in a controlled manner and : 13. 1. 1 step forward over the ball ; or 13. 1. 2 roll the ball back between the feet no more than one ( 1 ) metre ; or 13. 1. 3 pass a foot over the ball. ruling = a change of possession to the defending team at the point of the infringement. 13. 2 a player must perform the rollball on the mark. ruling = a penalty to the defending team at the point of the infringement. 13. 3 a player must not perform a voluntary rollball',
'. see appendix 1. kick strike or propel forcibly with the foot, a blow or forceful thrust with the foot to the ball. a tap to commence or recommence play or a penalty tap is not defined as a kick. line markings markings of the field of play. see appendix 1. link the player beside the wing player. mark ( for a tap ) the centre of the halfway line for the commencement or recommencement of play, or the position where a penalty tap is awarded as a result of an infringement. mark ( for a touch ) the position in the field of play where the player in possession was at the time the touch was made. fit playing rules - 5th edition 2 copyright © touch football australia 2020 middle the player inside the link player. nta national touch association as defined in the fit constitution. obstruction a deliberate attempt by either an attacking or defending player to gain an unfair advantage by interfering with the opposition to prevent them from gaining a rightful advantage. offside ( attacker ) an attacking player in a position forward of the ball. offside ( defender ) a defending player in a position closer than seven ( 7 ) metres from the mark of the rollball ; or ten ( 10 ) metres from the mark',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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### Direct Usage (Transformers)
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</details>
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You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
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## Training Details
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `learning_rate`: 2e-05
- `num_train_epochs`: 2
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.3
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 32
- `per_device_eval_batch_size`: 32
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 2
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.3
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss |
|:------:|:----:|:-------------:|:------:|
| 0.2857 | 2 | 1.9902 | 1.6592 |
| 0.5714 | 4 | 2.0653 | 1.5674 |
| 0.8571 | 6 | 1.7624 | 1.4603 |
| 1.1429 | 8 | 1.9255 | 1.3945 |
| 1.4286 | 10 | 1.3696 | 1.3618 |
| 1.7143 | 12 | 1.5989 | 1.3467 |
| 2.0 | 14 | 1.6238 | 1.3442 |
### Framework Versions
- Python: 3.10.12
- Sentence Transformers: 3.0.1
- Transformers: 4.42.3
- PyTorch: 2.1.1+cu121
- Accelerate: 0.31.0
- Datasets: 2.17.1
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
maxseats/SungBeom-whisper-small-ko-set16 | maxseats | 2024-07-02T11:16:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"speech-recognition",
"ko",
"dataset:maxseats/aihub-464-preprocessed-680GB-set-16",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-07-02T11:16:16Z |
---
language: ko
tags:
- whisper
- speech-recognition
datasets:
- maxseats/aihub-464-preprocessed-680GB-set-16
metrics:
- cer
---
# Model Name : maxseats/SungBeom-whisper-small-ko-set15
# Description
- 파인튜닝 데이터셋 : maxseats/aihub-464-preprocessed-680GB-set-16
# 설명
- AI hub의 주요 영역별 회의 음성 데이터셋을 학습 중이에요.
- 680GB 중 set_0~15 데이터(160GB)까지 파인튜닝한 모델을 불러와서, set_16 데이터(10GB)를 학습한 모델입니다.
- 링크 : https://huggingface.co/datasets/maxseats/aihub-464-preprocessed-680GB-set-16
|
meghnareddy90/batch-6-5001-6000 | meghnareddy90 | 2024-07-02T11:17:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-07-02T11:17:06Z | ---
base_model: microsoft/phi-2
library_name: peft
license: mit
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: batch-6-5001-6000
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. -->
# batch-6-5001-6000
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) 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: 0.0002
- train_batch_size: 2
- 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
### Framework versions
- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
bunnycore/RoleMind-Llama-3-8B | bunnycore | 2024-07-02T11:24:26Z | 0 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-07-02T11:19:38Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
---
# RoleMind-Llama-3-8B
RoleMind-Llama-3-8B is a merge of the following models using [mergekit](https://github.com/cg123/mergekit):
## 🧩 Configuration
```yaml
models:
- model: Replete-AI/Replete-Coder-Llama3-8B
- model: bunnycore/Intellplay-Llama-3-8B
- model: NousResearch/Hermes-2-Theta-Llama-3-8B
merge_method: model_stock
base_model: bunnycore/Intellplay-Llama-3-8B
dtype: bfloat16
``` |
Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF | Nabokov | 2024-07-02T11:20:15Z | 0 | 1 | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | text-generation | 2024-07-02T11:19:53Z | ---
base_model: microsoft/Phi-3-mini-4k-instruct
language:
- en
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- code
- llama-cpp
- gguf-my-repo
inference:
parameters:
temperature: 0.0
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
# Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF --hf-file phi-3-mini-4k-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF --hf-file phi-3-mini-4k-instruct-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF --hf-file phi-3-mini-4k-instruct-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Nabokov/Phi-3-mini-4k-instruct-Q8_0-GGUF --hf-file phi-3-mini-4k-instruct-q8_0.gguf -c 2048
```
|
socianai/socian-bn | socianai | 2024-07-02T13:28:55Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T11:20:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF | bartowski | 2024-07-02T18:05:24Z | 0 | 0 | null | [
"gguf",
"text-generation",
"license:gemma",
"region:us"
] | text-generation | 2024-07-02T11:20:40Z | ---
license: gemma
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Tess-v2.5-Gemma-2-27B-alpha
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3278">b3278</a> for quantization.
Original model: https://huggingface.co/migtissera/Tess-v2.5-Gemma-2-27B-alpha
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Experimental quants are made with `--output-tensor-type f16 --token-embedding-type f16` per [ZeroWw](https://huggingface.co/ZeroWw)'s suggestion, please provide any feedback on quality differences you spot.
## Prompt format
```
<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>model
```
Note that this model does not support a System prompt.
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Tess-v2.5-Gemma-2-27B-alpha-Q8_0_L.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q8_1.gguf) | Q8_0_L | 30.04GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Extremely high quality, generally unneeded but max available quant. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q8_0.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q8_0.gguf) | Q8_0 | 28.93GB | Extremely high quality, generally unneeded but max available quant. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q6_K_L.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q6_K_L.gguf) | Q6_K_L | 23.73GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Very high quality, near perfect, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q6_K.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q6_K.gguf) | Q6_K | 22.34GB | Very high quality, near perfect, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q5_K_L.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q5_K_L.gguf) | Q5_K_L | 20.79GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. High quality, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q5_K_M.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q5_K_M.gguf) | Q5_K_M | 19.40GB | High quality, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q5_K_S.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q5_K_S.gguf) | Q5_K_S | 18.88GB | High quality, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q4_K_L.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q4_K_L.gguf) | Q4_K_L | 18.03GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q4_K_M.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q4_K_M.gguf) | Q4_K_M | 16.64GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q4_K_S.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q4_K_S.gguf) | Q4_K_S | 15.73GB | Slightly lower quality with more space savings, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ4_XS.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ4_XS.gguf) | IQ4_XS | 14.81GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q3_K_XL.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q3_K_XL.gguf) | Q3_K_XL | 15.91GB | *Experimental*, uses f16 for embed and output weights. Please provide any feedback of differences. Lower quality but usable, good for low RAM availability. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q3_K_L.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q3_K_L.gguf) | Q3_K_L | 14.51GB | Lower quality but usable, good for low RAM availability. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q3_K_M.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q3_K_M.gguf) | Q3_K_M | 13.42GB | Even lower quality. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ3_M.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ3_M.gguf) | IQ3_M | 12.45GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q3_K_S.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q3_K_S.gguf) | Q3_K_S | 12.16GB | Low quality, not recommended. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ3_XS.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ3_XS.gguf) | IQ3_XS | 11.55GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ3_XXS.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ3_XXS.gguf) | IQ3_XXS | 10.75GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Tess-v2.5-Gemma-2-27B-alpha-Q2_K.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-Q2_K.gguf) | Q2_K | 10.44GB | Very low quality but surprisingly usable. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ2_M.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ2_M.gguf) | IQ2_M | 9.39GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ2_S.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ2_S.gguf) | IQ2_S | 8.65GB | Very low quality, uses SOTA techniques to be usable. |
| [Tess-v2.5-Gemma-2-27B-alpha-IQ2_XS.gguf](https://huggingface.co/bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF/blob/main/Tess-v2.5-Gemma-2-27B-alpha-IQ2_XS.gguf) | IQ2_XS | 8.39GB | Very low quality, uses SOTA techniques to be usable. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF --include "Tess-v2.5-Gemma-2-27B-alpha-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Tess-v2.5-Gemma-2-27B-alpha-GGUF --include "Tess-v2.5-Gemma-2-27B-alpha-Q8_0.gguf/*" --local-dir Tess-v2.5-Gemma-2-27B-alpha-Q8_0
```
You can either specify a new local-dir (Tess-v2.5-Gemma-2-27B-alpha-Q8_0) or download them all in place (./)
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
adamfendri/distilBertFull | adamfendri | 2024-07-02T11:21:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-07-02T11:20:56Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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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
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
abdfajar707/llama3_8B_lora_model_rkp_pn2025_v3 | abdfajar707 | 2024-07-02T11:25:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-07-02T11:25:24Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** abdfajar707
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
KAISHER/ALI | KAISHER | 2024-07-02T11:25:33Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:25:33Z | Entry not found |
sgonzalezsilot/whisper-tiny-es-Nemo_unique_2024-07-02_11-26-12 | sgonzalezsilot | 2024-07-02T11:26:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:26:12Z | Entry not found |
quydau/bert-finetuned-squad | quydau | 2024-07-02T11:26:18Z | 0 | 0 | null | [
"region:us"
] | null | 2024-07-02T11:26:18Z | Entry not found |
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