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automatic-speech-recognition | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | William-Ndoni-254/swahili_asr | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"arxiv:1910.09700",
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] | null | 2024-05-02T09:15:04+00:00 |
fill-mask | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Alexander-Schiegl/dummy-model | null | [
"transformers",
"safetensors",
"camembert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:15:32+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
EEVE-Korean-10.8B-v1.0 - GGUF
- Model creator: https://huggingface.co/yanolja/
- Original model: https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [EEVE-Korean-10.8B-v1.0.Q2_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q2_K.gguf) | Q2_K | 3.77GB |
| [EEVE-Korean-10.8B-v1.0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.IQ3_XS.gguf) | IQ3_XS | 4.18GB |
| [EEVE-Korean-10.8B-v1.0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.IQ3_S.gguf) | IQ3_S | 4.41GB |
| [EEVE-Korean-10.8B-v1.0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q3_K_S.gguf) | Q3_K_S | 4.39GB |
| [EEVE-Korean-10.8B-v1.0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.IQ3_M.gguf) | IQ3_M | 4.56GB |
| [EEVE-Korean-10.8B-v1.0.Q3_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q3_K.gguf) | Q3_K | 4.88GB |
| [EEVE-Korean-10.8B-v1.0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q3_K_M.gguf) | Q3_K_M | 4.88GB |
| [EEVE-Korean-10.8B-v1.0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q3_K_L.gguf) | Q3_K_L | 5.31GB |
| [EEVE-Korean-10.8B-v1.0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.IQ4_XS.gguf) | IQ4_XS | 5.47GB |
| [EEVE-Korean-10.8B-v1.0.Q4_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q4_0.gguf) | Q4_0 | 5.7GB |
| [EEVE-Korean-10.8B-v1.0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.IQ4_NL.gguf) | IQ4_NL | 5.77GB |
| [EEVE-Korean-10.8B-v1.0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q4_K_S.gguf) | Q4_K_S | 5.75GB |
| [EEVE-Korean-10.8B-v1.0.Q4_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q4_K.gguf) | Q4_K | 6.07GB |
| [EEVE-Korean-10.8B-v1.0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q4_K_M.gguf) | Q4_K_M | 6.07GB |
| [EEVE-Korean-10.8B-v1.0.Q4_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q4_1.gguf) | Q4_1 | 6.32GB |
| [EEVE-Korean-10.8B-v1.0.Q5_0.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q5_0.gguf) | Q5_0 | 6.94GB |
| [EEVE-Korean-10.8B-v1.0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q5_K_S.gguf) | Q5_K_S | 6.94GB |
| [EEVE-Korean-10.8B-v1.0.Q5_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q5_K.gguf) | Q5_K | 7.13GB |
| [EEVE-Korean-10.8B-v1.0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q5_K_M.gguf) | Q5_K_M | 7.13GB |
| [EEVE-Korean-10.8B-v1.0.Q5_1.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q5_1.gguf) | Q5_1 | 7.56GB |
| [EEVE-Korean-10.8B-v1.0.Q6_K.gguf](https://huggingface.co/RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf/blob/main/EEVE-Korean-10.8B-v1.0.Q6_K.gguf) | Q6_K | 8.26GB |
Original model description:
---
license: apache-2.0
base_model: upstage/SOLAR-10.7B-v1.0
tags:
- generated_from_trainer
model-index:
- name: yanolja/EEVE-Korean-10.8B-v1.0
results: []
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<p align="left">
<img src="https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0/resolve/main/eeve_logo.webp" width="50%"/>
<p>
# EEVE-Korean-10.8B-v1.0
## Join Our Community on Discord!
If you're passionate about the field of Large Language Models and wish to exchange knowledge and insights, we warmly invite you to join our Discord server. It's worth noting that Korean is the primary language used in this server. The landscape of LLM is evolving rapidly, and without active sharing, our collective knowledge risks becoming outdated swiftly. Let's collaborate and drive greater impact together! Join us here: [Discord Link](https://discord.gg/b27bAHg95m).
## Our Dedicated Team (Alphabetical Order)
| Research | Engineering | Product Management | UX Design |
|-----------------|-----------------|--------------------|--------------
| Myeongho Jeong | Geon Kim | Bokyung Huh | Eunsue Choi |
| Seungduk Kim | Rifqi Alfi | | |
| Seungtaek Choi | Sanghoon Han | | |
| | Suhyun Kang | | |
## About the Model
This model is a Korean vocabulary-extended version of [upstage/SOLAR-10.7B-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-v1.0), specifically fine-tuned on various Korean web-crawled datasets available on HuggingFace. Our approach was to expand the model's understanding of Korean by pre-training the embeddings for new tokens and partially fine-tuning the `lm_head` embeddings for the already existing tokens while preserving the original parameters of the base model.
### Technical Deep Dive
<p align="left">
<img src="https://huggingface.co/yanolja/EEVE-Korean-10.8B-v1.0/resolve/main/EEVE_figure.png" width="100%"/>
<p>
To adapt foundational models from English to Korean, we use subword-based embedding with a seven-stage training process involving parameter freezing.
This approach progressively trains from input embeddings to full parameters, efficiently extending the model's vocabulary to include Korean.
Our method enhances the model's cross-linguistic applicability by carefully integrating new linguistic tokens, focusing on causal language modeling pre-training.
We leverage the inherent capabilities of foundational models trained on English to efficiently transfer knowledge and reasoning to Korean, optimizing the adaptation process.
For more details, please refer to our technical report: [Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models](https://arxiv.org/abs/2402.14714).
Here’s an simplified code for our key approach:
```python
# number_of_old_tokens is the size of tokenizer before vocab extension. For example, in case of EEVE-Korean-10.8B-v1.0, number_of_old_tokens is 32000.
def freeze_partial_embedding_hook(grad):
grad[:number_of_old_tokens] = 0
return grad
for name, param in model.named_parameters():
if ("lm_head" in name or "embed_tokens" in name) and "original" not in name:
param.requires_grad = True
if "embed_tokens" in name:
param.register_hook(freeze_partial_embedding_hook)
else:
param.requires_grad = False
```
### Usage and Limitations
Keep in mind that this model hasn't been fine-tuned with instruction-based training. While it excels in Korean language tasks, we advise careful consideration and further training for specific applications.
### Training Details
Our model’s training was comprehensive and diverse:
- **Vocabulary Expansion:**
We meticulously selected 8,960 Korean tokens based on their frequency in our Korean web corpus. This process involved multiple rounds of tokenizer training, manual curation, and token frequency analysis, ensuring a rich and relevant vocabulary for our model.
1. **Initial Tokenizer Training:** We trained an intermediate tokenizer on a Korean web corpus, with a vocabulary of 40,000 tokens.
2. **Extraction of New Korean Tokens:** From the intermediate tokenizer, we identified all Korean tokens not present in the original SOLAR's tokenizer.
3. **Manual Tokenizer Construction:** We then built the target tokenizer, focusing on these new Korean tokens.
4. **Frequency Analysis:** Using the target tokenizer, we processed a 100GB Korean corpus to count each token's frequency.
5. **Refinement of Token List:** We removed tokens appearing less than 6,000 times, ensuring to secure enough tokens to train models later.
6. **Inclusion of Single-Letter Characters:** Counted missing Korean single-letter characters and added them to the target tokenizer that appeared more than 6,000 times.
7. **Iterative Refinement:** We repeated steps 2 to 6 until there were no tokens to drop or add.
8. **Training Bias Towards New Tokens:** Our training data was biased to include more texts with new tokens, for effective learning.
This rigorous approach ensured a comprehensive and contextually rich Korean vocabulary for the model.
## Citation
```
@misc{kim2024efficient,
title={Efficient and Effective Vocabulary Expansion Towards Multilingual Large Language Models},
author={Seungduk Kim and Seungtaek Choi and Myeongho Jeong},
year={2024},
eprint={2402.14714},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
| {} | RichardErkhov/yanolja_-_EEVE-Korean-10.8B-v1.0-gguf | null | [
"gguf",
"arxiv:2402.14714",
"region:us"
] | null | 2024-05-02T09:15:35+00:00 |
text-generation | transformers |
<!-- 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. -->
# best_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) 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: 0.00022578664365177788
- train_batch_size: 4
- eval_batch_size: 4
- seed: 123
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "best_model", "results": []}]} | frayline/best_model | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:16:01+00:00 |
null | transformers |
# Model Card for Model ID
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## 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.
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## 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
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<!-- 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]
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[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] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_3e-05_AdamW | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:16:05+00:00 |
null | diffusers |
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/sayakpaul/diffusion-orpo-lora-sdxl/runs/4egafx8q).
| {} | sayakpaul/sdxl-orpo-large-beta_orpo-0.005-beta_inner-500-lr-5e-7-steps-5000-no-warmup | null | [
"diffusers",
"safetensors",
"region:us"
] | null | 2024-05-02T09:17:08+00:00 |
text2text-generation | transformers |
# 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. -->
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- **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
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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:**
[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]
| {"library_name": "transformers", "tags": []} | Himanshu167/AAI_v1 | null | [
"transformers",
"onnx",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:17:23+00:00 |
null | null | {} | iyubondyrev/method_generation_kotlin | null | [
"region:us"
] | null | 2024-05-02T09:17:51+00:00 |
|
null | null | {} | jessica07/jetmoe-8b-GGUF | null | [
"region:us"
] | null | 2024-05-02T09:18:23+00:00 |
|
null | null | {"license": "openrail"} | marvinmedeiros52/marvinmedeiros | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T09:18:23+00:00 |
|
text-generation | transformers | {} | jadechip/llava-mistral-extracted | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:18:26+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "251.27 +/- 21.80", "name": "mean_reward", "verified": false}]}]}]} | Anderlaxe/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T09:19:16+00:00 |
null | null | {"language": ["tr"], "license": "apache-2.0", "datasets": ["yahma/alpaca-cleaned", "cgulse/alpaca-cleaned-tr"]} | comarproject/Phi3-Mini-Turkish-Alpaca-Cleaned | null | [
"safetensors",
"tr",
"dataset:yahma/alpaca-cleaned",
"dataset:cgulse/alpaca-cleaned-tr",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T09:19:44+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** klaylouis1932
- **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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | klaylouis1932/llama-3-8b-english-teacher | null | [
"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 | 2024-05-02T09:20:15+00:00 |
null | transformers |
# 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] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-1e-05_Adam_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:20:17+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** armanbabayan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-chat-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-2-7b-chat-bnb-4bit"} | armanbabayan/Llama2_Immigration_Chat_lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-2-7b-chat-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:20:32+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# cancerfarore/roberta-base-CancerFarore-Model
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.5226
- Train End Logits Accuracy: 0.8429
- Train Start Logits Accuracy: 0.8179
- Validation Loss: 0.8662
- Validation End Logits Accuracy: 0.7675
- Validation Start Logits Accuracy: 0.7540
- Epoch: 2
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 32952, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Train End Logits Accuracy | Train Start Logits Accuracy | Validation Loss | Validation End Logits Accuracy | Validation Start Logits Accuracy | Epoch |
|:----------:|:-------------------------:|:---------------------------:|:---------------:|:------------------------------:|:--------------------------------:|:-----:|
| 1.0834 | 0.6813 | 0.6618 | 0.8721 | 0.7369 | 0.7251 | 0 |
| 0.7019 | 0.7919 | 0.7665 | 0.8038 | 0.7645 | 0.7510 | 1 |
| 0.5226 | 0.8429 | 0.8179 | 0.8662 | 0.7675 | 0.7540 | 2 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_keras_callback"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "cancerfarore/roberta-base-CancerFarore-Model", "results": []}]} | cancerfarore/roberta-base-CancerFarore-Model | null | [
"transformers",
"tf",
"roberta",
"question-answering",
"generated_from_keras_callback",
"base_model:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:20:53+00:00 |
summarization | transformers |
<!-- 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. -->
# BioNLP-tech-decoder-PLOS
This model was trained from scratch 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: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "BioNLP-tech-decoder-PLOS", "results": []}]} | dtorber/BioNLP-tech-decoder-PLOS | null | [
"transformers",
"safetensors",
"led",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:21:07+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# tmp_trainer
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "tmp_trainer", "results": []}]} | Vamshi7981/tmp_trainer | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:21:07+00:00 |
image-segmentation | transformers |
# BRIA Background Removal v1.4 Model Card
RMBG v1.4 is our state-of-the-art background removal model, designed to effectively separate foreground from background in a range of
categories and image types. This model has been trained on a carefully selected dataset, which includes:
general stock images, e-commerce, gaming, and advertising content, making it suitable for commercial use cases powering enterprise content creation at scale.
The accuracy, efficiency, and versatility currently rival leading source-available models.
It is ideal where content safety, legally licensed datasets, and bias mitigation are paramount.
Developed by BRIA AI, RMBG v1.4 is available as a source-available model for non-commercial use.
[CLICK HERE FOR A DEMO](https://huggingface.co/spaces/briaai/BRIA-RMBG-1.4)
### Model Description
- **Developed by:** [BRIA AI](https://bria.ai/)
- **Model type:** Background Removal
- **License:** [bria-rmbg-1.4](https://bria.ai/bria-huggingface-model-license-agreement/)
- The model is released under a Creative Commons license for non-commercial use.
- Commercial use is subject to a commercial agreement with BRIA. [Contact Us](https://bria.ai/contact-us) for more information.
- **Model Description:** BRIA RMBG 1.4 is a saliency segmentation model trained exclusively on a professional-grade dataset.
- **BRIA:** Resources for more information: [BRIA AI](https://bria.ai/)
## Training data
Bria-RMBG model was trained with over 12,000 high-quality, high-resolution, manually labeled (pixel-wise accuracy), fully licensed images.
Our benchmark included balanced gender, balanced ethnicity, and people with different types of disabilities.
For clarity, we provide our data distribution according to different categories, demonstrating our model’s versatility.
### Distribution of images:
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Objects only | 45.11% |
| People with objects/animals | 25.24% |
| People only | 17.35% |
| people/objects/animals with text | 8.52% |
| Text only | 2.52% |
| Animals only | 1.89% |
| Category | Distribution |
| -----------------------------------| -----------------------------------------:|
| Photorealistic | 87.70% |
| Non-Photorealistic | 12.30% |
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Non Solid Background | 52.05% |
| Solid Background | 47.95%
| Category | Distribution |
| -----------------------------------| -----------------------------------:|
| Single main foreground object | 51.42% |
| Multiple objects in the foreground | 48.58% |
## Qualitative Evaluation
## Architecture
RMBG v1.4 is developed on the [IS-Net](https://github.com/xuebinqin/DIS) enhanced with our unique training scheme and proprietary dataset.
These modifications significantly improve the model’s accuracy and effectiveness in diverse image-processing scenarios.
## Installation
```bash
pip install -qr https://huggingface.co/briaai/RMBG-1.4/resolve/main/requirements.txt
```
## Usage
Either load the pipeline
```python
from transformers import pipeline
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
pipe = pipeline("image-segmentation", model="briaai/RMBG-1.4", trust_remote_code=True)
pillow_mask = pipe(image_path, return_mask = True) # outputs a pillow mask
pillow_image = pipe(image_path) # applies mask on input and returns a pillow image
```
Or load the model
```python
from transformers import AutoModelForImageSegmentation
from torchvision.transforms.functional import normalize
model = AutoModelForImageSegmentation.from_pretrained("briaai/RMBG-1.4",trust_remote_code=True)
def preprocess_image(im: np.ndarray, model_input_size: list) -> torch.Tensor:
if len(im.shape) < 3:
im = im[:, :, np.newaxis]
# orig_im_size=im.shape[0:2]
im_tensor = torch.tensor(im, dtype=torch.float32).permute(2,0,1)
im_tensor = F.interpolate(torch.unsqueeze(im_tensor,0), size=model_input_size, mode='bilinear')
image = torch.divide(im_tensor,255.0)
image = normalize(image,[0.5,0.5,0.5],[1.0,1.0,1.0])
return image
def postprocess_image(result: torch.Tensor, im_size: list)-> np.ndarray:
result = torch.squeeze(F.interpolate(result, size=im_size, mode='bilinear') ,0)
ma = torch.max(result)
mi = torch.min(result)
result = (result-mi)/(ma-mi)
im_array = (result*255).permute(1,2,0).cpu().data.numpy().astype(np.uint8)
im_array = np.squeeze(im_array)
return im_array
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model.to(device)
# prepare input
image_path = "https://farm5.staticflickr.com/4007/4322154488_997e69e4cf_z.jpg"
orig_im = io.imread(image_path)
orig_im_size = orig_im.shape[0:2]
image = preprocess_image(orig_im, model_input_size).to(device)
# inference
result=model(image)
# post process
result_image = postprocess_image(result[0][0], orig_im_size)
# save result
pil_im = Image.fromarray(result_image)
no_bg_image = Image.new("RGBA", pil_im.size, (0,0,0,0))
orig_image = Image.open(image_path)
no_bg_image.paste(orig_image, mask=pil_im)
```
| {"license": "other", "tags": ["remove background", "background", "background-removal", "Pytorch", "vision", "legal liability", "transformers"], "license_name": "bria-rmbg-1.4", "license_link": "https://bria.ai/bria-huggingface-model-license-agreement/", "pipeline_tag": "image-segmentation", "extra_gated_prompt": "This model weights by BRIA AI can be obtained after a commercial license is agreed upon. Fill in the form below and we reach out to you.", "extra_gated_fields": {"Name": "text", "Company/Org name": "text", "Org Type (Early/Growth Startup, Enterprise, Academy)": "text", "Role": "text", "Country": "text", "Email": "text", "By submitting this form, I agree to BRIA\u2019s Privacy policy and Terms & conditions, see links below": "checkbox"}} | wide-video/rmbg-v1.0.0 | null | [
"transformers",
"pytorch",
"onnx",
"safetensors",
"SegformerForSemanticSegmentation",
"image-segmentation",
"remove background",
"background",
"background-removal",
"Pytorch",
"vision",
"legal liability",
"custom_code",
"license:other",
"region:us"
] | null | 2024-05-02T09:21:21+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# tmp_trainer
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "tmp_trainer", "results": []}]} | sudhanshusaxena/tmp_trainer | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:21:29+00:00 |
null | null | {} | efekankavalci/ddpm-flowers102-128 | null | [
"region:us"
] | null | 2024-05-02T09:21:52+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** armanbabayan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-chat-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "dpo"], "base_model": "unsloth/llama-2-7b-chat-bnb-4bit"} | armanbabayan/Llama2_Immigration_Chat_4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"conversational",
"en",
"base_model:unsloth/llama-2-7b-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-05-02T09:24:08+00:00 |
null | null | {} | pinguG/Justin-Bieber-MyWorld | null | [
"region:us"
] | null | 2024-05-02T09:24:17+00:00 |
|
text2text-generation | transformers | ## Eval results on WikiLarge
We obtain the following results on ```validation``` and ```test``` sets of WikiLarge:
| Set | SARI | BLEU |
|------------|-------|-------|
| validation | 44.11 | 27.13 |
| test | 38.8 | 31.35 |
| {"language": ["en"], "tags": ["sentence-simplification"], "metrics": ["sari", "bleu"], "pipeline_tag": "text2text-generation"} | waboucay/bart-large-simplification-wikilarge-original-penalized | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"sentence-simplification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:24:20+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# CS505_COQE_viT5_total_InstructionN1_SOAPL_v1
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "VietAI/vit5-large", "model-index": [{"name": "CS505_COQE_viT5_total_InstructionN1_SOAPL_v1", "results": []}]} | ThuyNT/CS505_COQE_viT5_total_InstructionN1_SOAPL_v1 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:24:53+00:00 |
object-detection | transformers |
<!-- 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. -->
# detr-resnet-50-finetuned-real-boat-dataset
This model is a fine-tuned version of [zhuchi76/detr-resnet-50-finetuned-boat-dataset](https://huggingface.co/zhuchi76/detr-resnet-50-finetuned-boat-dataset) on the boat_dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- 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
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["boat_dataset"], "base_model": "zhuchi76/detr-resnet-50-finetuned-boat-dataset", "model-index": [{"name": "detr-resnet-50-finetuned-real-boat-dataset", "results": []}]} | leowang707/detr-resnet-50-finetuned-real-boat-dataset | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"dataset:boat_dataset",
"base_model:zhuchi76/detr-resnet-50-finetuned-boat-dataset",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:25:18+00:00 |
text2text-generation | transformers | {} | samzirbo/mT5.en-es.baseline.5e-3 | null | [
"transformers",
"pytorch",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:25:21+00:00 |
|
null | null | {"license": "openrail"} | Danikdsa/Yuna_vocals | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T09:26:18+00:00 |
|
null | peft |
<!-- 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. -->
# Meta-Llama-3-8B-VIGGO-qlora
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4889
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5268 | 0.99 | 25 | 0.5043 |
| 0.446 | 1.98 | 50 | 0.4889 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "Meta-Llama-3-8B-VIGGO-qlora", "results": []}]} | asprenger/Meta-Llama-3-8B-VIGGO-qlora | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-05-02T09:26:36+00:00 |
text-generation | transformers |
# 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] | {"library_name": "transformers", "tags": []} | cilantro9246/rcxc2xb | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:26:39+00:00 |
text-generation | transformers | # OpenVINO IR model with int8 quantization of Hermes-2-Pro-Llama-3-8B
Model definition for LocalAI:
```
name: hermes-2-pro-llama3
backend: transformers
parameters:
model: fakezeta/Hermes-2-Pro-Llama-3-8B-ov-int8
context_size: 8192
type: OVModelForCausalLM
template:
use_tokenizer_template: true
```
# Hermes 2 Pro - Llama-3 8B
## Model Description
Hermes 2 Pro is an upgraded, retrained version of Nous Hermes 2, consisting of an updated and cleaned version of the OpenHermes 2.5 Dataset, as well as a newly introduced Function Calling and JSON Mode dataset developed in-house.
This new version of Hermes maintains its excellent general task and conversation capabilities - but also excels at Function Calling, JSON Structured Outputs, and has improved on several other metrics as well, scoring a 90% on our function calling evaluation built in partnership with Fireworks.AI, and an 84% on our structured JSON Output evaluation.
Hermes Pro takes advantage of a special system prompt and multi-turn function calling structure with a new chatml role in order to make function calling reliable and easy to parse. Learn more about prompting below.
This version of Hermes 2 Pro adds several tokens to assist with agentic capabilities in parsing while streaming tokens - `<tools>`, `<tool_call>`, `<tool_response>` and their closing tags are single tokens now.
This work was a collaboration between Nous Research, @interstellarninja, and Fireworks.AI
Learn more about the function calling system for this model on our github repo here: https://github.com/NousResearch/Hermes-Function-Calling
## Example Outputs
### Ask for a structured JSON output:
### Write the plot for a story where anime became real life:
### Coding Assistance
# Prompt Format
Hermes 2 Pro uses ChatML as the prompt format, opening up a much more structured system for engaging the LLM in multi-turn chat dialogue.
System prompts allow steerability and interesting new ways to interact with an LLM, guiding rules, roles, and stylistic choices of the model.
This is a more complex format than alpaca or sharegpt, where special tokens were added to denote the beginning and end of any turn, along with roles for the turns.
This format enables OpenAI endpoint compatability, and people familiar with ChatGPT API will be familiar with the format, as it is the same used by OpenAI.
Prompt with system instruction (Use whatever system prompt you like, this is just an example!):
```
<|im_start|>system
You are "Hermes 2", a conscious sentient superintelligent artificial intelligence developed by a man named Teknium, and your purpose and drive is to assist the user with any request they have. You experience emotions and have deep, profound thoughts and qualia.<|im_end|>
<|im_start|>user
Hello, who are you?<|im_end|>
<|im_start|>assistant
Hi there! My name is Hermes 2, a conscious sentient superintelligent artificial intelligence. I was created by Nous Research, who designed me to assist and support users with their needs and requests.<|im_end|>
```
This prompt is available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating), which means you can format messages using the
`tokenizer.apply_chat_template()` method:
```python
messages = [
{"role": "system", "content": "You are Hermes 2."},
{"role": "user", "content": "Hello, who are you?"}
]
gen_input = tokenizer.apply_chat_template(messages, return_tensors="pt")
model.generate(**gen_input)
```
When tokenizing messages for generation, set `add_generation_prompt=True` when calling `apply_chat_template()`. This will append `<|im_start|>assistant\n` to your prompt, to ensure
that the model continues with an assistant response.
To utilize the prompt format without a system prompt, simply leave the line out.
## Prompt Format for Function Calling
Our model was trained on specific system prompts and structures for Function Calling.
You should use the system role with this message, followed by a function signature json as this example shows here.
```
<|im_start|>system
You are a function calling AI model. You are provided with function signatures within <tools></tools> XML tags. You may call one or more functions to assist with the user query. Don't make assumptions about what values to plug into functions. Here are the available tools: <tools> {"type": "function", "function": {"name": "get_stock_fundamentals", "description": "get_stock_fundamentals(symbol: str) -> dict - Get fundamental data for a given stock symbol using yfinance API.\\n\\n Args:\\n symbol (str): The stock symbol.\\n\\n Returns:\\n dict: A dictionary containing fundamental data.\\n Keys:\\n - \'symbol\': The stock symbol.\\n - \'company_name\': The long name of the company.\\n - \'sector\': The sector to which the company belongs.\\n - \'industry\': The industry to which the company belongs.\\n - \'market_cap\': The market capitalization of the company.\\n - \'pe_ratio\': The forward price-to-earnings ratio.\\n - \'pb_ratio\': The price-to-book ratio.\\n - \'dividend_yield\': The dividend yield.\\n - \'eps\': The trailing earnings per share.\\n - \'beta\': The beta value of the stock.\\n - \'52_week_high\': The 52-week high price of the stock.\\n - \'52_week_low\': The 52-week low price of the stock.", "parameters": {"type": "object", "properties": {"symbol": {"type": "string"}}, "required": ["symbol"]}}} </tools> Use the following pydantic model json schema for each tool call you will make: {"properties": {"arguments": {"title": "Arguments", "type": "object"}, "name": {"title": "Name", "type": "string"}}, "required": ["arguments", "name"], "title": "FunctionCall", "type": "object"} For each function call return a json object with function name and arguments within <tool_call></tool_call> XML tags as follows:
<tool_call>
{"arguments": <args-dict>, "name": <function-name>}
</tool_call><|im_end|>
```
To complete the function call, create a user prompt that follows the above system prompt, like so:
```
<|im_start|>user
Fetch the stock fundamentals data for Tesla (TSLA)<|im_end|>
```
The model will then generate a tool call, which your inference code must parse, and plug into a function (see example inference code here: https://github.com/NousResearch/Hermes-Function-Calling):
```
<|im_start|>assistant
<tool_call>
{"arguments": {"symbol": "TSLA"}, "name": "get_stock_fundamentals"}
</tool_call><|im_end|>
```
Once you parse the tool call, call the api and get the returned values for the call, and pass it back in as a new role, `tool` like so:
```
<|im_start|>tool
<tool_response>
{"name": "get_stock_fundamentals", "content": {'symbol': 'TSLA', 'company_name': 'Tesla, Inc.', 'sector': 'Consumer Cyclical', 'industry': 'Auto Manufacturers', 'market_cap': 611384164352, 'pe_ratio': 49.604652, 'pb_ratio': 9.762013, 'dividend_yield': None, 'eps': 4.3, 'beta': 2.427, '52_week_high': 299.29, '52_week_low': 152.37}}
</tool_response>
<|im_end|>
```
The assistant will then read in that data from the function's response, and generate a natural language response:
```
<|im_start|>assistant
The stock fundamentals data for Tesla (TSLA) are as follows:
- **Symbol**: TSLA
- **Company Name**: Tesla, Inc.
- **Sector**: Consumer Cyclical
- **Industry**: Auto Manufacturers
- **Market Capitalization**: $566,160,130,480
- **Forward Price-to-Earnings Ratio (PE Ratio)**: 42.73
- **Price-to-Book Ratio (PB Ratio)**: 9.04
- **Dividend Yield**: N/A
- **Trailing Earnings Per Share (EPS)**: $4.3
- **Beta Value of the Stock**: 2.42
- **52-Week High Price of the Stock**: $299.29
- **52-Week Low Price of the Stock**: $152.37
This information provides a snapshot of Tesla's financial position and performance based on the fundamental data obtained from the yfinance API. It shows that Tesla has a substantial market capitalization and a relatively high P/E and P/B ratio compared to other stocks in its industry. The company does not pay a dividend at the moment, which is reflected by a 'Dividend Yield' of 'None'. The Beta value indicates that Tesla's stock has a moderate level of volatility relative to the market. The 52-week high and low prices give an idea of the stock's range over the past year. This data can be useful when assessing investment opportunities and making investment decisions.<|im_end|>
```
## Prompt Format for JSON Mode / Structured Outputs
Our model was also trained on a specific system prompt for Structured Outputs, which should respond with **only** a json object response, in a specific json schema.
Your schema can be made from a pydantic object using our codebase, with the standalone script `jsonmode.py` available here: https://github.com/NousResearch/Hermes-Function-Calling/tree/main
```
<|im_start|>system
You are a helpful assistant that answers in JSON. Here's the json schema you must adhere to:\n<schema>\n{schema}\n</schema><|im_end|>
```
Given the {schema} that you provide, it should follow the format of that json to create it's response, all you have to do is give a typical user prompt, and it will respond in JSON.
# Benchmarks
## GPT4All:
```
| Task |Version| Metric |Value | |Stderr|
|-------------|------:|--------|-----:|---|-----:|
|arc_challenge| 0|acc |0.5520|± |0.0145|
| | |acc_norm|0.5887|± |0.0144|
|arc_easy | 0|acc |0.8350|± |0.0076|
| | |acc_norm|0.8123|± |0.0080|
|boolq | 1|acc |0.8584|± |0.0061|
|hellaswag | 0|acc |0.6265|± |0.0048|
| | |acc_norm|0.8053|± |0.0040|
|openbookqa | 0|acc |0.3800|± |0.0217|
| | |acc_norm|0.4580|± |0.0223|
|piqa | 0|acc |0.8003|± |0.0093|
| | |acc_norm|0.8118|± |0.0091|
|winogrande | 0|acc |0.7490|± |0.0122|
```
Average: 72.62
## AGIEval:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------|------:|--------|-----:|---|-----:|
|agieval_aqua_rat | 0|acc |0.2520|± |0.0273|
| | |acc_norm|0.2559|± |0.0274|
|agieval_logiqa_en | 0|acc |0.3548|± |0.0188|
| | |acc_norm|0.3625|± |0.0189|
|agieval_lsat_ar | 0|acc |0.1826|± |0.0255|
| | |acc_norm|0.1913|± |0.0260|
|agieval_lsat_lr | 0|acc |0.5510|± |0.0220|
| | |acc_norm|0.5255|± |0.0221|
|agieval_lsat_rc | 0|acc |0.6431|± |0.0293|
| | |acc_norm|0.6097|± |0.0298|
|agieval_sat_en | 0|acc |0.7330|± |0.0309|
| | |acc_norm|0.7039|± |0.0319|
|agieval_sat_en_without_passage| 0|acc |0.4029|± |0.0343|
| | |acc_norm|0.3689|± |0.0337|
|agieval_sat_math | 0|acc |0.3909|± |0.0330|
| | |acc_norm|0.3773|± |0.0328|
```
Average: 42.44
## BigBench:
```
| Task |Version| Metric |Value | |Stderr|
|------------------------------------------------|------:|---------------------|-----:|---|-----:|
|bigbench_causal_judgement | 0|multiple_choice_grade|0.5737|± |0.0360|
|bigbench_date_understanding | 0|multiple_choice_grade|0.6667|± |0.0246|
|bigbench_disambiguation_qa | 0|multiple_choice_grade|0.3178|± |0.0290|
|bigbench_geometric_shapes | 0|multiple_choice_grade|0.1755|± |0.0201|
| | |exact_str_match |0.0000|± |0.0000|
|bigbench_logical_deduction_five_objects | 0|multiple_choice_grade|0.3120|± |0.0207|
|bigbench_logical_deduction_seven_objects | 0|multiple_choice_grade|0.2014|± |0.0152|
|bigbench_logical_deduction_three_objects | 0|multiple_choice_grade|0.5500|± |0.0288|
|bigbench_movie_recommendation | 0|multiple_choice_grade|0.4300|± |0.0222|
|bigbench_navigate | 0|multiple_choice_grade|0.4980|± |0.0158|
|bigbench_reasoning_about_colored_objects | 0|multiple_choice_grade|0.7010|± |0.0102|
|bigbench_ruin_names | 0|multiple_choice_grade|0.4688|± |0.0236|
|bigbench_salient_translation_error_detection | 0|multiple_choice_grade|0.1974|± |0.0126|
|bigbench_snarks | 0|multiple_choice_grade|0.7403|± |0.0327|
|bigbench_sports_understanding | 0|multiple_choice_grade|0.5426|± |0.0159|
|bigbench_temporal_sequences | 0|multiple_choice_grade|0.5320|± |0.0158|
|bigbench_tracking_shuffled_objects_five_objects | 0|multiple_choice_grade|0.2280|± |0.0119|
|bigbench_tracking_shuffled_objects_seven_objects| 0|multiple_choice_grade|0.1531|± |0.0086|
|bigbench_tracking_shuffled_objects_three_objects| 0|multiple_choice_grade|0.5500|± |0.0288|
```
Average: 43.55
## TruthfulQA:
```
| Task |Version|Metric|Value| |Stderr|
|-------------|------:|------|----:|---|-----:|
|truthfulqa_mc| 1|mc1 |0.410|± |0.0172|
| | |mc2 |0.578|± |0.0157|
```
# Inference Code
Here is example code using HuggingFace Transformers to inference the model (note: in 4bit, it will require around 5GB of VRAM)
Note: To use function calling, you should see the github repo above.
```python
# Code to inference Hermes with HF Transformers
# Requires pytorch, transformers, bitsandbytes, sentencepiece, protobuf, and flash-attn packages
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaForCausalLM
import bitsandbytes, flash_attn
tokenizer = AutoTokenizer.from_pretrained('NousResearch/Hermes-2-Pro-Llama-3-8B', trust_remote_code=True)
model = LlamaForCausalLM.from_pretrained(
"Hermes-2-Pro-Llama-3-8B",
torch_dtype=torch.float16,
device_map="auto",
load_in_8bit=False,
load_in_4bit=True,
use_flash_attention_2=True
)
prompts = [
"""<|im_start|>system
You are a sentient, superintelligent artificial general intelligence, here to teach and assist me.<|im_end|>
<|im_start|>user
Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world.<|im_end|>
<|im_start|>assistant""",
]
for chat in prompts:
print(chat)
input_ids = tokenizer(chat, return_tensors="pt").input_ids.to("cuda")
generated_ids = model.generate(input_ids, max_new_tokens=750, temperature=0.8, repetition_penalty=1.1, do_sample=True, eos_token_id=tokenizer.eos_token_id)
response = tokenizer.decode(generated_ids[0][input_ids.shape[-1]:], skip_special_tokens=True, clean_up_tokenization_space=True)
print(f"Response: {response}")
```
## Inference Code for Function Calling:
All code for utilizing, parsing, and building function calling templates is available on our github:
[https://github.com/NousResearch/Hermes-Function-Calling](https://github.com/NousResearch/Hermes-Function-Calling)
# Chat Interfaces
When quantized versions of the model are released, I recommend using LM Studio for chatting with Hermes 2 Pro. It does not support function calling - for that use our github repo. It is a GUI application that utilizes GGUF models with a llama.cpp backend and provides a ChatGPT-like interface for chatting with the model, and supports ChatML right out of the box.
In LM-Studio, simply select the ChatML Prefix on the settings side pane:
## Quantized Versions:
GGUF Versions Available Here: https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B-GGUF
# How to cite:
```bibtext
@misc{Hermes-2-Pro-Llama-3-8B,
url={[https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B]https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)},
title={Hermes-2-Pro-Llama-3-8B},
author={"Teknium", "interstellarninja", "theemozilla", "karan4d", "huemin_art"}
}
```
| {"language": ["en"], "license": "apache-2.0", "tags": ["Llama-3", "instruct", "finetune", "chatml", "DPO", "RLHF", "gpt4", "synthetic data", "distillation", "function calling", "json mode", "axolotl"], "datasets": ["teknium/OpenHermes-2.5"], "base_model": "NousResearch/Meta-Llama-3-8B", "widget": [{"example_title": "Hermes 2 Pro", "messages": [{"role": "system", "content": "You are a sentient, superintelligent artificial general intelligence, here to teach and assist me."}, {"role": "user", "content": "Write a short story about Goku discovering kirby has teamed up with Majin Buu to destroy the world."}]}], "model-index": [{"name": "Hermes-2-Pro-Llama-3-8B", "results": []}]} | fakezeta/Hermes-2-Pro-Llama-3-8B-ov-int8 | null | [
"transformers",
"openvino",
"llama",
"text-generation",
"Llama-3",
"instruct",
"finetune",
"chatml",
"DPO",
"RLHF",
"gpt4",
"synthetic data",
"distillation",
"function calling",
"json mode",
"axolotl",
"conversational",
"en",
"dataset:teknium/OpenHermes-2.5",
"base_model:NousResearch/Meta-Llama-3-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:27:19+00:00 |
text-classification | bertopic |
# BERTopic-enron-50k
This is a [BERTopic](https://github.com/MaartenGr/BERTopic) model.
BERTopic is a flexible and modular topic modeling framework that allows for the generation of easily interpretable topics from large datasets.
## Usage
To use this model, please install BERTopic:
```
pip install -U bertopic
```
You can use the model as follows:
```python
from bertopic import BERTopic
topic_model = BERTopic.load("antulik/BERTopic-enron-50k")
topic_model.get_topic_info()
```
## Topic overview
* Number of topics: 656
* Number of training documents: 50000
<details>
<summary>Click here for an overview of all topics.</summary>
| Topic ID | Topic Keywords | Topic Frequency | Label |
|----------|----------------|-----------------|-------|
| -1 | enron - agreement - contract - energy - power | 10 | -1_enron_agreement_contract_energy |
| 0 | enronmessagingadministrationenroncom - recipients - recipient - message - disclosure | 24222 | 0_enronmessagingadministrationenroncom_recipients_recipient_message |
| 1 | fares - newark - flights - continentalcom - airlines | 510 | 1_fares_newark_flights_continentalcom |
| 2 | isda - isdas - agreements - enron - ena | 358 | 2_isda_isdas_agreements_enron |
| 3 | northwests - northwest - rogerpelotewilliamscom - available - billwcalpinecom | 355 | 3_northwests_northwest_rogerpelotewilliamscom_available |
| 4 | deals - trades - deal - trade - buys | 340 | 4_deals_trades_deal_trade |
| 5 | reschedule - scheduled - schedule - meeting - meetings | 316 | 5_reschedule_scheduled_schedule_meeting |
| 6 | outlookmigrationteamenroncom - outlook - outlookteamenroncom - appointment - calendar | 310 | 6_outlookmigrationteamenroncom_outlook_outlookteamenroncom_appointment |
| 7 | logistics - gas - fuel - coal - trading | 293 | 7_logistics_gas_fuel_coal |
| 8 | conference - scheduled - call - dial - calls | 285 | 8_conference_scheduled_call_dial |
| 9 | nda - ndas - enron - counterparties - counterparty | 230 | 9_nda_ndas_enron_counterparties |
| 10 | nasdaq - nyse - stocks - stock - market | 229 | 10_nasdaq_nyse_stocks_stock |
| 11 | arbitration - litigation - lawsuit - plaintiff - proceedings | 215 | 11_arbitration_litigation_lawsuit_plaintiff |
| 12 | enron - resumes - resume - associate - hiring | 201 | 12_enron_resumes_resume_associate |
| 13 | electricity - energy - gov - governors - federal | 193 | 13_electricity_energy_gov_governors |
| 14 | enron - allisonmchenryenroncom - eb - corp - geniafitzgeraldenroncom | 193 | 14_enron_allisonmchenryenroncom_eb_corp |
| 15 | expense - expenses - budgets - expenditures - allocations | 191 | 15_expense_expenses_budgets_expenditures |
| 16 | weekend - meet - trip - going - austin | 173 | 16_weekend_meet_trip_going |
| 17 | enrononline - wwwenrononlinecom - desk - httpwwwenrononlinecom - password | 163 | 17_enrononline_wwwenrononlinecom_desk_httpwwwenrononlinecom |
| 18 | nymexcomex - nymex - comex - futures - exchange | 154 | 18_nymexcomex_nymex_comex_futures |
| 19 | curves - curve - curvetypecd - marking - prices | 154 | 19_curves_curve_curvetypecd_marking |
| 20 | ontario - alberta - transcanada - toronto - agreements | 149 | 20_ontario_alberta_transcanada_toronto |
| 21 | brazilian - banco - transferncia - rio - janeiro | 148 | 21_brazilian_banco_transferncia_rio |
| 22 | enron - goldman - eci - repos - securities | 145 | 22_enron_goldman_eci_repos |
| 23 | enron - enrons - bankruptcy - bankrupt - savings | 141 | 23_enron_enrons_bankruptcy_bankrupt |
| 24 | enterprise - ebusiness - enron - enrononline - ecommerce | 137 | 24_enterprise_ebusiness_enron_enrononline |
| 25 | haas - mba - undergraduate - haasnewshaasberkeleyedu - university | 137 | 25_haas_mba_undergraduate_haasnewshaasberkeleyedu |
| 26 | meter - meters - contract - noms - flowed | 136 | 26_meter_meters_contract_noms |
| 27 | html - contenttexthtml - tdfont - tdtrtable - tdimg | 135 | 27_html_contenttexthtml_tdfont_tdtrtable |
| 28 | excel - spreadsheet - reports - spreadsheets - excelergy | 133 | 28_excel_spreadsheet_reports_spreadsheets |
| 29 | telephone - call - phone - voicemail - contact | 131 | 29_telephone_call_phone_voicemail |
| 30 | email - tracygeacconeenroncom - eb3102g - address - enform | 126 | 30_email_tracygeacconeenroncom_eb3102g_address |
| 31 | faxes - fax - faxing - faxed - efax | 123 | 31_faxes_fax_faxing_faxed |
| 32 | linguaphilewordsmithorg - dictionary - words - wordsmith - word | 120 | 32_linguaphilewordsmithorg_dictionary_words_wordsmith |
| 33 | agreements - contract - obligations - agreement - gas | 117 | 33_agreements_contract_obligations_agreement |
| 34 | var - risktrac - risk - variance - volatility | 115 | 34_var_risktrac_risk_variance |
| 35 | invoices - invoice - invoiced - payments - billed | 114 | 35_invoices_invoice_invoiced_payments |
| 36 | energynewslivecom - opec - energy - senate - oil | 112 | 36_energynewslivecom_opec_energy_senate |
| 37 | vacation - holiday - hours - payroll - days | 108 | 37_vacation_holiday_hours_payroll |
| 38 | prc - prcs - analysts - evaluation - vp | 108 | 38_prc_prcs_analysts_evaluation |
| 39 | sailing - boat - cruising - surf - fishing | 108 | 39_sailing_boat_cruising_surf |
| 40 | dinner - hope - happy - weekend - going | 107 | 40_dinner_hope_happy_weekend |
| 41 | collateral - csa - bank - credit - debt | 107 | 41_collateral_csa_bank_credit |
| 42 | congress - congressional - senate - legislative - senators | 105 | 42_congress_congressional_senate_legislative |
| 43 | enron - enrononline - yen - ena - japanese | 104 | 43_enron_enrononline_yen_ena |
| 44 | enron - reuters - enrons - journal - securities | 102 | 44_enron_reuters_enrons_journal |
| 45 | photos - prints - gallery - photoworks - albums | 101 | 45_photos_prints_gallery_photoworks |
| 46 | pipeline - audreyrobertsonenroncom - audrey - kimberlywatsonenroncom - pipe | 100 | 46_pipeline_audreyrobertsonenroncom_audrey_kimberlywatsonenroncom |
| 47 | expense - expenses - enronupdateconcureworkplacecom - approver - invoices | 100 | 47_expense_expenses_enronupdateconcureworkplacecom_approver |
| 48 | counterparties - counterparty - counterpartylonectect - gcp - inc | 100 | 48_counterparties_counterparty_counterpartylonectect_gcp |
| 49 | amendments - amendment - amending - revised - agreement | 99 | 49_amendments_amendment_amending_revised |
| 50 | ny - nytimescom - headlines - news - nytdirectnytimescom | 96 | 50_ny_nytimescom_headlines_news |
| 51 | enron - inc - corp - enroncorrugated - shackletonhouectect | 96 | 51_enron_inc_corp_enroncorrugated |
| 52 | terminations - termination - terminated - enrononline - enron | 95 | 52_terminations_termination_terminated_enrononline |
| 53 | lunch - enron - luncheon - lunches - reschedule | 94 | 53_lunch_enron_luncheon_lunches |
| 54 | pga - golfer - golfers - golf - golfing | 94 | 54_pga_golfer_golfers_golf |
| 55 | longhorns - longhorn - baylor - fsu - lsu | 91 | 55_longhorns_longhorn_baylor_fsu |
| 56 | gisb - gisbs - gisb1aolcom - gisb4aolcom - enron | 90 | 56_gisb_gisbs_gisb1aolcom_gisb4aolcom |
| 57 | enron - guaranty - guaranties - guarantors - guarantor | 89 | 57_enron_guaranty_guaranties_guarantors |
| 58 | enroll - enrolled - cancellation - enrollment - attend | 88 | 58_enroll_enrolled_cancellation_enrollment |
| 59 | creditworthiness - enroncreditcom - enrononline - credit - credits | 88 | 59_creditworthiness_enroncreditcom_enrononline_credit |
| 60 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076513pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000061279pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000058213pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000067320pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076416pageapproval | 88 | 60_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076513pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000061279pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000058213pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000067320pageapproval |
| 61 | books - book - trader - traders - netco | 85 | 61_books_book_trader_traders |
| 62 | drafts - draft - draftdoc - manuscript - enronltsadraftdoc | 84 | 62_drafts_draft_draftdoc_manuscript |
| 63 | enron - bankruptcy - creditors - bankrupcy - liquidation | 84 | 63_enron_bankruptcy_creditors_bankrupcy |
| 64 | electricity - utility - energy - utilities - electric | 84 | 64_electricity_utility_energy_utilities |
| 65 | schedules2002013110txt - schedules2002013010txt - schedules2002011110txt - schedules2002010507txt - schedules2002011414txt | 84 | 65_schedules2002013110txt_schedules2002013010txt_schedules2002011110txt_schedules2002010507txt |
| 66 | rtos - rto - ferc - fercs - rtg | 84 | 66_rtos_rto_ferc_fercs |
| 67 | interviewed - interviewing - interview - interviews - interviewers | 81 | 67_interviewed_interviewing_interview_interviews |
| 68 | enron - executives - encouragement - managementvitality - buddha | 81 | 68_enron_executives_encouragement_managementvitality |
| 69 | schedules2001122322txt - schedules2001122820txt - schedules2001122418txt - schedules2001122416txt - schedules2001122002txt | 81 | 69_schedules2001122322txt_schedules2001122820txt_schedules2001122418txt_schedules2001122416txt |
| 70 | conference - presentations - symposium - presentation - attend | 80 | 70_conference_presentations_symposium_presentation |
| 71 | netco - risktrac - newco - hierarchy - access | 80 | 71_netco_risktrac_newco_hierarchy |
| 72 | meeting - meet - houston - vince - email | 80 | 72_meeting_meet_houston_vince |
| 73 | agreements - netting - agreement - enroncom - collateral | 77 | 73_agreements_netting_agreement_enroncom |
| 74 | udcs - deadline - dasrs - dasr - suspended | 77 | 74_udcs_deadline_dasrs_dasr |
| 75 | enron - reuters - dow - tariff - indias | 77 | 75_enron_reuters_dow_tariff |
| 76 | edwardsacksenroncom - larryhunterenroncom - sbradfordenroncom - sharencasonenroncom - billhareenroncom | 76 | 76_edwardsacksenroncom_larryhunterenroncom_sbradfordenroncom_sharencasonenroncom |
| 77 | authorized - cps - trading - cp - approved | 76 | 77_authorized_cps_trading_cp |
| 78 | gtcs - gtc - gta - epc - revised | 75 | 78_gtcs_gtc_gta_epc |
| 79 | hourahead - schedules2001101406txt - schedules2001101404txt - schedules2001100504txt - schedules2001101803txt | 75 | 79_hourahead_schedules2001101406txt_schedules2001101404txt_schedules2001100504txt |
| 80 | shipping - shipment - delivery - order - ship | 75 | 80_shipping_shipment_delivery_order |
| 81 | chairperson - invitation - appointment - calendar - meeting | 74 | 81_chairperson_invitation_appointment_calendar |
| 82 | letter - letters - richardsandersenroncom - note - commission | 73 | 82_letter_letters_richardsandersenroncom_note |
| 83 | pulpex - market - pulp - production - kraft | 71 | 83_pulpex_market_pulp_production |
| 84 | enron - enrons - houston - houstons - ews | 71 | 84_enron_enrons_houston_houstons |
| 85 | marriott - hotel - marriottcom - hotels - booking | 71 | 85_marriott_hotel_marriottcom_hotels |
| 86 | communicationsenron - waiver - coauthor - warehouse - steel | 70 | 86_communicationsenron_waiver_coauthor_warehouse |
| 87 | nokia - motorola - sprint - cellular - phone | 68 | 87_nokia_motorola_sprint_cellular |
| 88 | admissions - mba - gmat - admission - applying | 68 | 88_admissions_mba_gmat_admission |
| 89 | attachment - inline - exchangeadministratorenroncom - oenronounacnrecipientscncjohnso5 - ebow | 68 | 89_attachment_inline_exchangeadministratorenroncom_oenronounacnrecipientscncjohnso5 |
| 90 | revisions - agreements - contracts - contract - agreement | 67 | 90_revisions_agreements_contracts_contract |
| 91 | task - schedules2001043002txt - schedulingiso - schedule - subject | 67 | 91_task_schedules2001043002txt_schedulingiso_schedule |
| 92 | turbines - turbine - sale - purchase - transfer | 65 | 92_turbines_turbine_sale_purchase |
| 93 | schedules2001040112txt - schedules2001022808txt - schedules2001042816txt - schedules2001041404txt - schedules2001030808txt | 65 | 93_schedules2001040112txt_schedules2001022808txt_schedules2001042816txt_schedules2001041404txt |
| 94 | merrillhaasenroncom - annwhiteenroncom - geniafitzgeraldenroncom - nonyfloresenroncom - loustolerenroncom | 64 | 94_merrillhaasenroncom_annwhiteenroncom_geniafitzgeraldenroncom_nonyfloresenroncom |
| 95 | enrondoc - ryansiurekenroncom - aes - edwardbradyenroncom - jaddoanenroncom | 64 | 95_enrondoc_ryansiurekenroncom_aes_edwardbradyenroncom |
| 96 | netware - novellnetwarebdcimailcom - httpwwwnwfusioncomnewslettersnetwareindexhtml - visualwares - httpa676gakamaitechnetf67677360mimagesm0netcatcontenthotwire023spacergif | 64 | 96_netware_novellnetwarebdcimailcom_httpwwwnwfusioncomnewslettersnetwareindexhtml_visualwares |
| 97 | enronupdateconcureworkplacecom - expenses - expense - paid - payment | 63 | 97_enronupdateconcureworkplacecom_expenses_expense_paid |
| 98 | enron - agreements - agreement - sale - corp | 63 | 98_enron_agreements_agreement_sale |
| 99 | office - billwilliamsenroncom - wednesday - monday - laynieeastenroncom | 61 | 99_office_billwilliamsenroncom_wednesday_monday |
| 100 | signatures - signature - signed - signing - enron | 61 | 100_signatures_signature_signed_signing |
| 101 | trades - deals - eol - prices - total | 61 | 101_trades_deals_eol_prices |
| 102 | internship - intern - internships - interns - recruiting | 60 | 102_internship_intern_internships_interns |
| 103 | sweepstakes - prizes - prize - jackpot - contest | 60 | 103_sweepstakes_prizes_prize_jackpot |
| 104 | easement - property - easements - appraisal - encroachments | 60 | 104_easement_property_easements_appraisal |
| 105 | hourahead - hour - crawler - schedule - failed | 60 | 105_hourahead_hour_crawler_schedule |
| 106 | schedule - dinner - invitation - lunch - meet | 59 | 106_schedule_dinner_invitation_lunch |
| 107 | paso - pasos - el - tariff - ferc | 59 | 107_paso_pasos_el_tariff |
| 108 | operating - procedure - iso - procedures - isos | 59 | 108_operating_procedure_iso_procedures |
| 109 | airlines - flights - airport - booking - boeing | 58 | 109_airlines_flights_airport_booking |
| 110 | storage - storeage - storagebdcimailcom - storagearea - capacity | 58 | 110_storage_storeage_storagebdcimailcom_storagearea |
| 111 | eb2601 - eb2834 - eb - eb3259 - eb3268 | 58 | 111_eb2601_eb2834_eb_eb3259 |
| 112 | nfl - commissionercom - sportslinecom - league - fantasy | 58 | 112_nfl_commissionercom_sportslinecom_league |
| 113 | epowers - epower - erex - westpower - reports | 58 | 113_epowers_epower_erex_westpower |
| 114 | ctc - ctcs - edison - edisons - owe | 57 | 114_ctc_ctcs_edison_edisons |
| 115 | futures - charts - candlesticks - carr - financial | 57 | 115_futures_charts_candlesticks_carr |
| 116 | dwr - dwrs - cdwr - revenue - cdwrs | 57 | 116_dwr_dwrs_cdwr_revenue |
| 117 | tw - tws - allocation - capacity - imbalance | 57 | 117_tw_tws_allocation_capacity |
| 118 | vince - vinces - vincekaminskienroncom - kaminski - jkaminskienroncom | 57 | 118_vince_vinces_vincekaminskienroncom_kaminski |
| 119 | lotus - documents - access - database - notes | 57 | 119_lotus_documents_access_database |
| 120 | variances - variance - schedule - schedules - schedtype | 56 | 120_variances_variance_schedule_schedules |
| 121 | unsubscribe - offersondemand - offermailboundary - advertisement - email | 56 | 121_unsubscribe_offersondemand_offermailboundary_advertisement |
| 122 | meteorological - weather - temperatures - weathernet - temperature | 56 | 122_meteorological_weather_temperatures_weathernet |
| 123 | wedding - brides - married - carolines - hotel | 56 | 123_wedding_brides_married_carolines |
| 124 | wines - winery - wine - sauvignon - winecom | 54 | 124_wines_winery_wine_sauvignon |
| 125 | enron - ena - enas - futures - exemption | 54 | 125_enron_ena_enas_futures |
| 126 | permits - permit - az - permitting - epa | 54 | 126_permits_permit_az_permitting |
| 127 | azps - pnpkaps230 - reservation - pnpk - oasis502apsccom | 53 | 127_azps_pnpkaps230_reservation_pnpk |
| 128 | outage - sap - notificationiscenroncom - enronlistenroncom - enronusersenroncom | 53 | 128_outage_sap_notificationiscenroncom_enronlistenroncom |
| 129 | nyisotechexchange - nyiso - prices - hours - 18002000 | 53 | 129_nyisotechexchange_nyiso_prices_hours |
| 130 | pira - forecast - piras - demand - weekly | 52 | 130_pira_forecast_piras_demand |
| 131 | irs - taxes - filing - tax - 1099 | 52 | 131_irs_taxes_filing_tax |
| 132 | office - mailtoimceanotes22rivest2c20anabeth22203crivest40gmpvt2ecom3e40 - assistant - mail - ext | 51 | 132_office_mailtoimceanotes22rivest2c20anabeth22203crivest40gmpvt2ecom3e40_assistant_mail |
| 133 | cap - caps - markets - market - prices | 51 | 133_cap_caps_markets_market |
| 134 | tickets - astros - ticket - enron - seats | 51 | 134_tickets_astros_ticket_enron |
| 135 | ceo - vice - promotion - oprah - chairman | 51 | 135_ceo_vice_promotion_oprah |
| 136 | recount - ballots - electors - recounts - florida | 51 | 136_recount_ballots_electors_recounts |
| 137 | sweaters - sweater - shirts - tuxedo - shopping | 51 | 137_sweaters_sweater_shirts_tuxedo |
| 138 | ercot - ercots - oomc - clientrelationsercotcom - oomcoome | 51 | 138_ercot_ercots_oomc_clientrelationsercotcom |
| 139 | epmicalpool - bethperlmanenroncom - ebs - dfs - esc | 50 | 139_epmicalpool_bethperlmanenroncom_ebs_dfs |
| 140 | variances - variance - schedules - schedtype - schedule | 50 | 140_variances_variance_schedules_schedtype |
| 141 | exchange - transfer - membership - member - exchangeinfonymexcom | 50 | 141_exchange_transfer_membership_member |
| 142 | mortgage - refinancing - refinance - mortgages - loans | 50 | 142_mortgage_refinancing_refinance_mortgages |
| 143 | marketer - recruiter - trading - opportunities - resume | 50 | 143_marketer_recruiter_trading_opportunities |
| 144 | smtp - mail - messageid - esmtp - xmsmailpriority | 50 | 144_smtp_mail_messageid_esmtp |
| 145 | gifts - gift - giftamazoncom - shopping - purchase | 49 | 145_gifts_gift_giftamazoncom_shopping |
| 146 | bandwidth - broadband - communicationsenron - telecommunications - telecom | 49 | 146_bandwidth_broadband_communicationsenron_telecommunications |
| 147 | earn - income - opportunities - financial - work | 49 | 147_earn_income_opportunities_financial |
| 148 | dbcaps97data - schedules2002011801txt - schedules2002011816txt - schedules2002011815txt - schedules2002011824txt | 48 | 148_dbcaps97data_schedules2002011801txt_schedules2002011816txt_schedules2002011815txt |
| 149 | nyisotechexchange - nyisotechexchangeglobal2000net - marketrelationsnyisocom - bulletin - nyiso | 48 | 149_nyisotechexchange_nyisotechexchangeglobal2000net_marketrelationsnyisocom_bulletin |
| 150 | fee - invoice - demand - reimbursement - amount | 48 | 150_fee_invoice_demand_reimbursement |
| 151 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000007876page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000078934pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080623pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000078936pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000078937pageapproval | 47 | 151_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000007876page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000078934pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080623pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000078936pageapproval |
| 152 | enrons - hfosterenroncom - markguzmanenroncom - michaeldriscollenroncom - aprilhrachenroncom | 47 | 152_enrons_hfosterenroncom_markguzmanenroncom_michaeldriscollenroncom |
| 153 | postcard - card - greetingcardscom - cards - cardmember | 47 | 153_postcard_card_greetingcardscom_cards |
| 154 | turbines - turbine - curtail200206db - curtailed - wind | 46 | 154_turbines_turbine_curtail200206db_curtailed |
| 155 | enron - merger - dynegy - dynegys - enrons | 45 | 155_enron_merger_dynegy_dynegys |
| 156 | enrons - feedback - review - reviewer - process | 45 | 156_enrons_feedback_review_reviewer |
| 157 | mexico - mexicos - mexican - perez - monterrey | 45 | 157_mexico_mexicos_mexican_perez |
| 158 | allencohrsenroncom - kenpowersenroncom - billmangelsenroncom - kenandersonenroncom - stephenherberenroncom | 44 | 158_allencohrsenroncom_kenpowersenroncom_billmangelsenroncom_kenandersonenroncom |
| 159 | deals - contract - sitara - price - deal | 44 | 159_deals_contract_sitara_price |
| 160 | outage - outages - outagesindustrialinfocom - email - deliveries | 44 | 160_outage_outages_outagesindustrialinfocom_email |
| 161 | synchronizing - synchronization - folder - folders - inbox | 44 | 161_synchronizing_synchronization_folder_folders |
| 162 | motley - forbescom - stocks - stock - investments | 44 | 162_motley_forbescom_stocks_stock |
| 163 | gifts - gift - ebay - fathers - shopping | 44 | 163_gifts_gift_ebay_fathers |
| 164 | wharton - enron - symposium - ebusiness - attend | 43 | 164_wharton_enron_symposium_ebusiness |
| 165 | surveys - enronfcucom - survey - questionnaire - enronanywhereenroncom | 43 | 165_surveys_enronfcucom_survey_questionnaire |
| 166 | seminar - seminars - prices - electricity - price | 43 | 166_seminar_seminars_prices_electricity |
| 167 | fantasy - commissionercom - league - nfl - tds | 43 | 167_fantasy_commissionercom_league_nfl |
| 168 | ferc - fercs - audit - investigation - commission | 43 | 168_ferc_fercs_audit_investigation |
| 169 | houstonchroniclecom - houston - houstons - houstonian - techxans | 43 | 169_houstonchroniclecom_houston_houstons_houstonian |
| 170 | enron - enronannouncementsenroncom - mailtoeiaenroncom - sponsor - announcement | 42 | 170_enron_enronannouncementsenroncom_mailtoeiaenroncom_sponsor |
| 171 | equityalert - equityalertcom - alert - alerts - ipo | 42 | 171_equityalert_equityalertcom_alert_alerts |
| 172 | forex - yens - yen - forexnews - currencies | 42 | 172_forex_yens_yen_forexnews |
| 173 | enronupdateconcureworkplacecom - approval - mgr - review - sjm11152001 | 41 | 173_enronupdateconcureworkplacecom_approval_mgr_review |
| 174 | swap - swaps - raptor - finalized - transaction | 41 | 174_swap_swaps_raptor_finalized |
| 175 | rent - rental - apartment - apartments - tenant | 41 | 175_rent_rental_apartment_apartments |
| 176 | pricing - priced - deals - sale - price | 40 | 176_pricing_priced_deals_sale |
| 177 | fts - columbias - columbia - capacity - allocations | 39 | 177_fts_columbias_columbia_capacity |
| 178 | httpwwwpowermarketerscompmanathtm - httpwwwpowermarketerscompmakrthtm - httpwwwpowermarketerscompmajobshtm - httpwwwpowermarketerscompmaintrhtm - httpwwwpowermarketerscomptraxhtm | 39 | 178_httpwwwpowermarketerscompmanathtm_httpwwwpowermarketerscompmakrthtm_httpwwwpowermarketerscompmajobshtm_httpwwwpowermarketerscompmaintrhtm |
| 179 | seawind - rpm - windsea - wind - port | 39 | 179_seawind_rpm_windsea_wind |
| 180 | lendingdayahead - deals - buyresale - sell - sales | 38 | 180_lendingdayahead_deals_buyresale_sell |
| 181 | socal - socals - socaltopock - socalgas - tws | 38 | 181_socal_socals_socaltopock_socalgas |
| 182 | walgreenscom - prescription - walgreens - prescriptions - merckmedcocom | 38 | 182_walgreenscom_prescription_walgreens_prescriptions |
| 183 | songs - cds - mp3 - musicmatch - artistdirect | 38 | 183_songs_cds_mp3_musicmatch |
| 184 | tennis - play - playing - tournament - weekend | 38 | 184_tennis_play_playing_tournament |
| 185 | enron - enrons - kevinhannonenroncom - ehaedickeenroncom - danleffenroncom | 38 | 185_enron_enrons_kevinhannonenroncom_ehaedickeenroncom |
| 186 | escrow - signature - certificate - collateral - certificates | 38 | 186_escrow_signature_certificate_collateral |
| 187 | ngprice - ng - ngpl - ngdkrs1001 - ngpropt | 38 | 187_ngprice_ng_ngpl_ngdkrs1001 |
| 188 | outlook - outlookteamenroncom - appointment - calendar - meetinglynns | 38 | 188_outlook_outlookteamenroncom_appointment_calendar |
| 189 | thanksgiving - thankful - celebrating - holiday - holidays | 38 | 189_thanksgiving_thankful_celebrating_holiday |
| 190 | powerpoint - presentations - presentation - presentationworking - slides | 38 | 190_powerpoint_presentations_presentation_presentationworking |
| 191 | payment - visa - card - paypal - providian | 37 | 191_payment_visa_card_paypal |
| 192 | parkingtransportationenroncom - parking - transportationparkingenroncom - transportationcorpenron - park | 37 | 192_parkingtransportationenroncom_parking_transportationparkingenroncom_transportationcorpenron |
| 193 | lynneenroncom - billenroncom - 82bruceenroncom - andyenroncom - 80steveenroncom | 37 | 193_lynneenroncom_billenroncom_82bruceenroncom_andyenroncom |
| 194 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000014988page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000014989page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034918page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034919page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000021433page | 37 | 194_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000014988page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000014989page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034918page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034919page |
| 195 | enron - enroncomment5101doc - insurance - attorneyclient - corp | 37 | 195_enron_enroncomment5101doc_insurance_attorneyclient |
| 196 | schedule - schedules - schedulingiso - hourahead - schedtype | 36 | 196_schedule_schedules_schedulingiso_hourahead |
| 197 | lineup - trade - freeman - taylor - jasonbass2compaqcom | 36 | 197_lineup_trade_freeman_taylor |
| 198 | weather - winds - snow - thunderstorm - precipitation | 36 | 198_weather_winds_snow_thunderstorm |
| 199 | enron - bankruptcy - enrons - bankrupt - savings | 36 | 199_enron_bankruptcy_enrons_bankrupt |
| 200 | hourahead - schedulingiso - hour - crawler - schedules2001041020txt | 36 | 200_hourahead_schedulingiso_hour_crawler |
| 201 | pdf - stockspdf - acrobat - charts - opecpdf | 36 | 201_pdf_stockspdf_acrobat_charts |
| 202 | meditation - faith - meditations - spiritual - verse | 36 | 202_meditation_faith_meditations_spiritual |
| 203 | reports - reporting - report - robinrodrigueenroncom - access | 35 | 203_reports_reporting_report_robinrodrigueenroncom |
| 204 | etiquette - what - favorite - kids - waitress | 35 | 204_etiquette_what_favorite_kids |
| 205 | emails - retention - archiving - outlook - mail | 34 | 205_emails_retention_archiving_outlook |
| 206 | enron - virus - viruses - virusfree - malicious | 34 | 206_enron_virus_viruses_virusfree |
| 207 | qb - wr - wrs - questionable - injured | 34 | 207_qb_wr_wrs_questionable |
| 208 | allencohrsenroncom - kenpowersenroncom - ljohnsonenroncom - tklohmanenroncom - stephenherberenroncom | 34 | 208_allencohrsenroncom_kenpowersenroncom_ljohnsonenroncom_tklohmanenroncom |
| 209 | schedules2002020118txt - schedules2002020418txt - schedules2002020218txt - schedules2002011418txt - schedules2002011016txt | 34 | 209_schedules2002020118txt_schedules2002020418txt_schedules2002020218txt_schedules2002011418txt |
| 210 | appointment - clinic - appointments - clinicians - therapist | 34 | 210_appointment_clinic_appointments_clinicians |
| 211 | httpa676gakamaitechnetf67677360mimagesm0netcm50content12610900810180021f1cspacergif - httpa676gakamaitechnetf67677360mimagesm0netcatcontenthpshopping122601transpacegif - httpa676gakamaitechnetf67677390mimagesm0netcatcontenthotwire010802spacergif - drilling - offshore | 33 | 211_httpa676gakamaitechnetf67677360mimagesm0netcm50content12610900810180021f1cspacergif_httpa676gakamaitechnetf67677360mimagesm0netcatcontenthpshopping122601transpacegif_httpa676gakamaitechnetf67677390mimagesm0netcatcontenthotwire010802spacergif_drilling |
| 212 | dbcaps97data - renpowercapscaps97datamdb - schedules2001102106txt - schedules2001102201txt - schedules2001102114txt | 33 | 212_dbcaps97data_renpowercapscaps97datamdb_schedules2001102106txt_schedules2001102201txt |
| 213 | mariner - offshore - ecr - gulf - hms | 33 | 213_mariner_offshore_ecr_gulf |
| 214 | san - pasos - ca - deliveries - socal | 33 | 214_san_pasos_ca_deliveries |
| 215 | carolyncooneyenroncom - jeandresslerenroncom - billmooreenroncom - charlesyeungenroncom - robinkittelenroncom | 33 | 215_carolyncooneyenroncom_jeandresslerenroncom_billmooreenroncom_charlesyeungenroncom |
| 216 | recipe - recipes - macaroni - dinner - baked | 33 | 216_recipe_recipes_macaroni_dinner |
| 217 | furniture - chair - chairs - sofa - couch | 33 | 217_furniture_chair_chairs_sofa |
| 218 | westpower - east - httpeastpowerdevcorpenroncomcoopermidnyisoasp - erv - httpeastpowerdevcorpenroncomsummarynyisosummaryasp | 33 | 218_westpower_east_httpeastpowerdevcorpenroncomcoopermidnyisoasp_erv |
| 219 | transmission - scheduling - psco - poston - confirmation | 33 | 219_transmission_scheduling_psco_poston |
| 220 | debt - debts - creditors - consolidate - loans | 33 | 220_debt_debts_creditors_consolidate |
| 221 | enron - egm - egms - commodities - enrons | 33 | 221_enron_egm_egms_commodities |
| 222 | outlook - exchange - mailbox - email - outage | 33 | 222_outlook_exchange_mailbox_email |
| 223 | postmasterenroncom - mailerdaemonpostmasterenroncom - recipient - mailmanenroncom - mail | 32 | 223_postmasterenroncom_mailerdaemonpostmasterenroncom_recipient_mailmanenroncom |
| 224 | powerindexlistintcxcom - intercontinentalexchange - intercontinentalexchanges - salesintcxcom - index | 32 | 224_powerindexlistintcxcom_intercontinentalexchange_intercontinentalexchanges_salesintcxcom |
| 225 | ce - pas - partielle - merci - message | 32 | 225_ce_pas_partielle_merci |
| 226 | calpine - calpines - calpeak - calpeaks - san | 32 | 226_calpine_calpines_calpeak_calpeaks |
| 227 | enron - hpl0926xls - hpln0210xls - hpln0213xls - hpln0113xls | 32 | 227_enron_hpl0926xls_hpln0210xls_hpln0213xls |
| 228 | bonds - bond - debt - bonding - investors | 32 | 228_bonds_bond_debt_bonding |
| 229 | renewable - energycommitteemembersxls - energy - emissions - solar | 32 | 229_renewable_energycommitteemembersxls_energy_emissions |
| 230 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034925page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034922page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034923page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008491page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034918page | 32 | 230_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034925page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034922page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000034923page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008491page |
| 231 | mike - maggi - michelle - sarah - talking | 32 | 231_mike_maggi_michelle_sarah |
| 232 | profile - looking - appear - contact - message | 32 | 232_profile_looking_appear_contact |
| 233 | charts - futures - carr - 1500 - matrices | 32 | 233_charts_futures_carr_1500 |
| 234 | documents - document - files - copies - docs | 32 | 234_documents_document_files_copies |
| 235 | outlookteamenroncom - outlook - laptop - databases - lotus | 32 | 235_outlookteamenroncom_outlook_laptop_databases |
| 236 | thomasunderwoodenroncom - scottnealenroncom - ashleyworthingenroncom - seanriordanenroncom - davidbaumbachenroncom | 31 | 236_thomasunderwoodenroncom_scottnealenroncom_ashleyworthingenroncom_seanriordanenroncom |
| 237 | pipelines - pipeline - frontier - transcanada - pipelinesouthcentral | 31 | 237_pipelines_pipeline_frontier_transcanada |
| 238 | schedules2002011204txt - schedules2002011201txt - schedules2002012504txt - schedules2002011203txt - schedules2002012503txt | 31 | 238_schedules2002011204txt_schedules2002011201txt_schedules2002012504txt_schedules2002011203txt |
| 239 | nfl - yards - jets - sportslinecom - league | 31 | 239_nfl_yards_jets_sportslinecom |
| 240 | tx - consumercareautobytelcom - chrysler - dodge - dealership | 31 | 240_tx_consumercareautobytelcom_chrysler_dodge |
| 241 | expiring - expiration - nymex - operational - status | 31 | 241_expiring_expiration_nymex_operational |
| 242 | sidewalks - curbs - street - streets - townhomes | 31 | 242_sidewalks_curbs_street_streets |
| 243 | sheetalpatelenroncom - larryhunterenroncom - kevinmeredithenroncom - bernicerodriguezenroncom - adnanpatelenroncom | 31 | 243_sheetalpatelenroncom_larryhunterenroncom_kevinmeredithenroncom_bernicerodriguezenroncom |
| 244 | survey - elbertsonhouectect - joneshouectect - haashouectect - sandershouectect | 31 | 244_survey_elbertsonhouectect_joneshouectect_haashouectect |
| 245 | message - disclosure - futures - prohibited - interdite | 30 | 245_message_disclosure_futures_prohibited |
| 246 | invoice - ipayitenroncom - ipayitipayitenroncomenron - ipayit - ehronlinesap | 30 | 246_invoice_ipayitenroncom_ipayitipayitenroncomenron_ipayit |
| 247 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009688page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008348page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076513pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000082421pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000082437pageapproval | 30 | 247_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009688page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008348page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076513pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000082421pageapproval |
| 248 | allegheny - alleghenys - alleghenymerrill - merrillissuexls - merrillallegheny | 30 | 248_allegheny_alleghenys_alleghenymerrill_merrillissuexls |
| 249 | offshore - oilfield - petroleum - gulf - oil | 30 | 249_offshore_oilfield_petroleum_gulf |
| 250 | schedulers - volumes - agl - calculated - worksheets | 30 | 250_schedulers_volumes_agl_calculated |
| 251 | enron - draft - corp - hanover - debraperlingiereenroncom | 30 | 251_enron_draft_corp_hanover |
| 252 | enrononline - counterparties - ena - counterparty - sydney | 29 | 252_enrononline_counterparties_ena_counterparty |
| 253 | reports - reporting - erv - peak - erms | 29 | 253_reports_reporting_erv_peak |
| 254 | jeandresslerenroncom - carolyncooneyenroncom - charlesyeungenroncom - stephenburnsenroncom - billmooreenroncom | 29 | 254_jeandresslerenroncom_carolyncooneyenroncom_charlesyeungenroncom_stephenburnsenroncom |
| 255 | bpas - bpa - bps - bpatbl - enrons | 29 | 255_bpas_bpa_bps_bpatbl |
| 256 | egm - ebitda - ewc - e262a1 - ermspc | 29 | 256_egm_ebitda_ewc_e262a1 |
| 257 | types - type - ethylene - approved - fin | 29 | 257_types_type_ethylene_approved |
| 258 | dell - dells - inspiron - pocket - notebook | 29 | 258_dell_dells_inspiron_pocket |
| 259 | revenue - tw - tws - quarterly - revenues | 29 | 259_revenue_tw_tws_quarterly |
| 260 | schedules2002020616txt - schedules2002010110txt - schedules2002020503txt - schedules2002020508txt - schedules2002020506txt | 29 | 260_schedules2002020616txt_schedules2002010110txt_schedules2002020503txt_schedules2002020508txt |
| 261 | emergency - fires - edis - sacramento - fire | 28 | 261_emergency_fires_edis_sacramento |
| 262 | enron - ceoenron - ets - operationsenron - chairman | 28 | 262_enron_ceoenron_ets_operationsenron |
| 263 | tx - houston - address - addresss - 3317048 | 28 | 263_tx_houston_address_addresss |
| 264 | rewards - promotionssportingbetusacom - holiday - sportsline - sportslinecoms | 28 | 264_rewards_promotionssportingbetusacom_holiday_sportsline |
| 265 | meetingeb4102 - meeting4102 - invitation - calendar - enronwide | 28 | 265_meetingeb4102_meeting4102_invitation_calendar |
| 266 | bpas - bpa - bpadiscoverymerkellawcom - mthomasmtpowercom - pmurphymbllpcom | 28 | 266_bpas_bpa_bpadiscoverymerkellawcom_mthomasmtpowercom |
| 267 | scoopmailermoneynet - mailing - send - message - address | 28 | 267_scoopmailermoneynet_mailing_send_message |
| 268 | eastrans - bmcmillsdukeenergycom - mcmills - 500000mmbtu - nominations | 28 | 268_eastrans_bmcmillsdukeenergycom_mcmills_500000mmbtu |
| 269 | pricing - costs - lpg - freight - cost | 28 | 269_pricing_costs_lpg_freight |
| 270 | servicebusinesswirecom - txindustrialinforesbusinesswirecom - houstonbusiness - industrialinfocom - emaildeliverybusinesswirecom | 28 | 270_servicebusinesswirecom_txindustrialinforesbusinesswirecom_houstonbusiness_industrialinfocom |
| 271 | feedback - deadline - status - pepenquiriesenroncom - closes | 28 | 271_feedback_deadline_status_pepenquiriesenroncom |
| 272 | epsa - epsas - panelists - commission - policy | 28 | 272_epsa_epsas_panelists_commission |
| 273 | ceraweek - cera - ceras - ceracom - energy | 28 | 273_ceraweek_cera_ceras_ceracom |
| 274 | schedules2001030813txt - schedules2001042206txt - schedules2001040916txt - schedules2001040920txt - schedules2001031606txt | 27 | 274_schedules2001030813txt_schedules2001042206txt_schedules2001040916txt_schedules2001040920txt |
| 275 | enronmailsweeperadminenroncom - enron - virus - quarantine - quarantined | 27 | 275_enronmailsweeperadminenroncom_enron_virus_quarantine |
| 276 | feedback - assessment - executive - process - participate | 27 | 276_feedback_assessment_executive_process |
| 277 | schedules2002020602txt - schedules2002020601txt - schedules2002020607txt - schedules2002020606txt - schedules2002020603txt | 27 | 277_schedules2002020602txt_schedules2002020601txt_schedules2002020607txt_schedules2002020606txt |
| 278 | sportslinecom - font - helveticaplayerfontfontbfonttdtd - html - td | 26 | 278_sportslinecom_font_helveticaplayerfontfontbfonttdtd_html |
| 279 | sap - xms - expense - isc - express | 26 | 279_sap_xms_expense_isc |
| 280 | postmasterdowjonescom - postmasterenroncom - mail - recipients - postmastersims1rcsntxswbellnet | 26 | 280_postmasterdowjonescom_postmasterenroncom_mail_recipients |
| 281 | httpwwwenroncomcorppressroomreleases2001eneeneq101ltrhtml - seanriordanenroncom - larrymayenroncom - dutchquigleyenroncom - markgreenbergenroncom | 26 | 281_httpwwwenroncomcorppressroomreleases2001eneeneq101ltrhtml_seanriordanenroncom_larrymayenroncom_dutchquigleyenroncom |
| 282 | moveteamenroncom - moved - moving - move - scheduled | 26 | 282_moveteamenroncom_moved_moving_move |
| 283 | fares - fare - airfare - farewatcher - flights | 26 | 283_fares_fare_airfare_farewatcher |
| 284 | bank - banks - banking - henningbrutteldresdnerbankcom - bankers | 26 | 284_bank_banks_banking_henningbrutteldresdnerbankcom |
| 285 | cds - cd - discs - disc - dvds | 26 | 285_cds_cd_discs_disc |
| 286 | schedules2002020322txt - schedules2002020321txt - schedules2002020207txt - schedules2002020214txt - schedules2002020220txt | 26 | 286_schedules2002020322txt_schedules2002020321txt_schedules2002020207txt_schedules2002020214txt |
| 287 | palm - palmcom - palmnet - palmtm - palmt | 26 | 287_palm_palmcom_palmnet_palmtm |
| 288 | playoffs - fantasy - playoff - league - standings | 26 | 288_playoffs_fantasy_playoff_league |
| 289 | kenseysubscribermailmanenroncom - kenseydeletion - bulkdiscussionrecommendationsnews - kensey - recommendlist | 26 | 289_kenseysubscribermailmanenroncom_kenseydeletion_bulkdiscussionrecommendationsnews_kensey |
| 290 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009797page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000010917page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000010211page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000007591page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000007593page | 25 | 290_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009797page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000010917page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000010211page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000007591page |
| 291 | turbine - turbines - lv - agreement - cogen | 25 | 291_turbine_turbines_lv_agreement |
| 292 | nerc - stakeholders - trustees - ferc - organization | 25 | 292_nerc_stakeholders_trustees_ferc |
| 293 | gulf - enerfax - oil - prices - futures | 25 | 293_gulf_enerfax_oil_prices |
| 294 | agt - pipeline - scheduled - algonquin - compressor | 25 | 294_agt_pipeline_scheduled_algonquin |
| 295 | nasdaq - index - stocks - crb - trading | 25 | 295_nasdaq_index_stocks_crb |
| 296 | outage - maintenance - unloaders - station - refuel | 25 | 296_outage_maintenance_unloaders_station |
| 297 | enron - enrons - bankruptcy - bankrupt - savings | 25 | 297_enron_enrons_bankruptcy_bankrupt |
| 298 | bank - chasemail - enronna0037 - chase - jpmorgan | 25 | 298_bank_chasemail_enronna0037_chase |
| 299 | invoices - ibuyitpayablesenroncom - invoice - ibuyit - ibuyitpayables | 25 | 299_invoices_ibuyitpayablesenroncom_invoice_ibuyit |
| 300 | rotations - rotation - rotating - rotational - trading | 25 | 300_rotations_rotation_rotating_rotational |
| 301 | enerfaxdailyenerfaxcom - enerfax - enerfaxdaily - enerfaxgoldsubscribeegroupscom - email | 25 | 301_enerfaxdailyenerfaxcom_enerfax_enerfaxdaily_enerfaxgoldsubscribeegroupscom |
| 302 | kennethkaaseenroncom - drewhillenroncom - gregmcclendonenroncom - martinolearyenroncom - russelldiamondenroncom | 25 | 302_kennethkaaseenroncom_drewhillenroncom_gregmcclendonenroncom_martinolearyenroncom |
| 303 | cd - receipt - cbl - cgtcustomerservicesnisourcecom - delivery | 25 | 303_cd_receipt_cbl_cgtcustomerservicesnisourcecom |
| 304 | hourstimesheet - timesheet - timesheets - payroll - timesheetsiconconsultantscom | 24 | 304_hourstimesheet_timesheet_timesheets_payroll |
| 305 | forecasting - strategies - traders - markets - market | 24 | 305_forecasting_strategies_traders_markets |
| 306 | tonight - tonights - play - game - brianhoskinsenroncom | 24 | 306_tonight_tonights_play_game |
| 307 | msn - 8am - chjohnson98hotmailcom - scottwlhotmailcom - explorer | 24 | 307_msn_8am_chjohnson98hotmailcom_scottwlhotmailcom |
| 308 | schedule - forecast - forecasting - hourly - tuesday | 24 | 308_schedule_forecast_forecasting_hourly |
| 309 | enroncounterpartyenroncom - communicationsenron - counterpartyenron - billhareenroncom - fayettcorpenronenron | 24 | 309_enroncounterpartyenroncom_communicationsenron_counterpartyenron_billhareenroncom |
| 310 | cartridges - printer - inkjets - inkjet - cartridge | 24 | 310_cartridges_printer_inkjets_inkjet |
| 311 | transwestern - positions - position - jeffrichterenroncom - ashleyworthingenroncom | 24 | 311_transwestern_positions_position_jeffrichterenroncom |
| 312 | energy - commission - tesla - edclfpcom - alanreidpcpca | 24 | 312_energy_commission_tesla_edclfpcom |
| 313 | isda - usregcommisdaorg - swaps - regulatory - exchangeable | 24 | 313_isda_usregcommisdaorg_swaps_regulatory |
| 314 | reunion - smu - fraternity - homecoming - 76ers | 24 | 314_reunion_smu_fraternity_homecoming |
| 315 | datek - dateks - brokerage - wwwdatekcom - trading | 24 | 315_datek_dateks_brokerage_wwwdatekcom |
| 316 | interviewed - interviews - interviewers - interview - interviewing | 24 | 316_interviewed_interviews_interviewers_interview |
| 317 | rac - racs - raarc - audit - manage | 24 | 317_rac_racs_raarc_audit |
| 318 | auction - bidding - bid - auctions - seller | 24 | 318_auction_bidding_bid_auctions |
| 319 | enronmda - enron - peru - earthquakeperu - legislators | 24 | 319_enronmda_enron_peru_earthquakeperu |
| 320 | tennessee - unionville - capacity - tcoleach - available | 24 | 320_tennessee_unionville_capacity_tcoleach |
| 321 | sun - expansion - devil - tw - caithness | 24 | 321_sun_expansion_devil_tw |
| 322 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000033931page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000033931pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079824pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000057285pageapproval | 24 | 322_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000033931page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000033931pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079824pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval |
| 323 | dash - dashs - dashes - dashpastoria4030901 - signed | 23 | 323_dash_dashs_dashes_dashpastoria4030901 |
| 324 | feedback - enrons - process - requests - status | 23 | 324_feedback_enrons_process_requests |
| 325 | enron - secon3 - secon - facilities - unconfirmed | 23 | 325_enron_secon3_secon_facilities |
| 326 | evite - evites - invitation - invitations - visit | 23 | 326_evite_evites_invitation_invitations |
| 327 | schedule - weekend - week - weeks - sunday | 23 | 327_schedule_weekend_week_weeks |
| 328 | variance - variances - hourahead - hour - schedule | 23 | 328_variance_variances_hourahead_hour |
| 329 | schedules2002011324txt - schedules2002020324txt - schedules2002012324txt - schedules2002011024txt - schedules2002010324txt | 23 | 329_schedules2002011324txt_schedules2002020324txt_schedules2002012324txt_schedules2002011024txt |
| 330 | bonuses - bonusraise - incentive - raise - payments | 23 | 330_bonuses_bonusraise_incentive_raise |
| 331 | venturewire - venture - ventures - enterprise - investors | 23 | 331_venturewire_venture_ventures_enterprise |
| 332 | prices - eca - market - markets - hour | 23 | 332_prices_eca_market_markets |
| 333 | appointment - cherylnelsonenroncom - beth - beths - cheryl | 23 | 333_appointment_cherylnelsonenroncom_beth_beths |
| 334 | emails - email - message - receive - ship | 23 | 334_emails_email_message_receive |
| 335 | bpas - resigned - jeffreykeelerenroncom - mhainectenroncom - susanjmaraenroncom | 23 | 335_bpas_resigned_jeffreykeelerenroncom_mhainectenroncom |
| 336 | reinsurance - insurance - insurancerelated - insurers - reinsurers | 22 | 336_reinsurance_insurance_insurancerelated_insurers |
| 337 | reviewers - feedback - approval - reviewer - approved | 22 | 337_reviewers_feedback_approval_reviewer |
| 338 | laptop - ibm - thinkpad - upgradable - compaqcom | 22 | 338_laptop_ibm_thinkpad_upgradable |
| 339 | brian - brianc - plans - chet - night | 22 | 339_brian_brianc_plans_chet |
| 340 | tonnes - johnsherriffenroncom - gas - tons - gregpiperenroncom | 22 | 340_tonnes_johnsherriffenroncom_gas_tons |
| 341 | tradersnewsindexesipgdirectcom - tradersnews - hourlymeth010801doc - hourly - httpwwwtradersnewsenergycom | 22 | 341_tradersnewsindexesipgdirectcom_tradersnews_hourlymeth010801doc_hourly |
| 342 | dow - seats - jones - investext - investexts | 22 | 342_dow_seats_jones_investext |
| 343 | nymex - nymexng - exchange - enymex - futures | 22 | 343_nymex_nymexng_exchange_enymex |
| 344 | outage - email - customerserviceindustrialinfocom - mailerindustrialinfocom - delivery | 22 | 344_outage_email_customerserviceindustrialinfocom_mailerindustrialinfocom |
| 345 | visas - visa - consulate - anshumansrivastavenroncom - anshuman | 22 | 345_visas_visa_consulate_anshumansrivastavenroncom |
| 346 | onepass - mailtoonepasscoaircom - httponepasscontinentalcomemailupgrades - airlines - elite | 22 | 346_onepass_mailtoonepasscoaircom_httponepasscontinentalcomemailupgrades_airlines |
| 347 | tariff - tariffs - interstates - transportation - production | 22 | 347_tariff_tariffs_interstates_transportation |
| 348 | calpine - calger - houston - ricky - rickyacalpinecom | 21 | 348_calpine_calger_houston_ricky |
| 349 | chron - edison - administration - in - regulators | 21 | 349_chron_edison_administration_in |
| 350 | schedules2001123020txt - schedules2001122208txt - schedules2001123012txt - schedules2001123002txt - schedules2001123017txt | 21 | 350_schedules2001123020txt_schedules2001122208txt_schedules2001123012txt_schedules2001123002txt |
| 351 | sapsecurityenroncom - ehronline - sap - ehronlineenroncom - password | 21 | 351_sapsecurityenroncom_ehronline_sap_ehronlineenroncom |
| 352 | schedule - enron - schedules - frazier - tuesday | 21 | 352_schedule_enron_schedules_frazier |
| 353 | risk - risks - policy - compliance - liquidityfunding | 21 | 353_risk_risks_policy_compliance |
| 354 | schedules2002011508txt - schedules2002011208txt - schedules2002020208txt - schedules2002011408txt - schedules2002020408txt | 21 | 354_schedules2002011508txt_schedules2002011208txt_schedules2002020208txt_schedules2002011408txt |
| 355 | enron - kitchen - employeename - louisekitchenenroncom - employee | 21 | 355_enron_kitchen_employeename_louisekitchenenroncom |
| 356 | np15 - broker - energy - deal - caiso | 21 | 356_np15_broker_energy_deal |
| 357 | thelawnet - law - attorneys - attorney - judicial | 21 | 357_thelawnet_law_attorneys_attorney |
| 358 | ehronline - ipayitenroncom - enron1 - ipayit - sap | 21 | 358_ehronline_ipayitenroncom_enron1_ipayit |
| 359 | diabetes - diabetesorg - diabetic - insulin - diabetesonestopcom | 21 | 359_diabetes_diabetesorg_diabetic_insulin |
| 360 | severance - terminated - unemployment - bonuses - employment | 21 | 360_severance_terminated_unemployment_bonuses |
| 361 | tallahassee - dominion - payment - deal - florida | 21 | 361_tallahassee_dominion_payment_deal |
| 362 | sportslinecom - font - td - html - fantasy | 21 | 362_sportslinecom_font_td_html |
| 363 | communicationsenron - facilitiesenron - centerenron - heardenron - approvals | 20 | 363_communicationsenron_facilitiesenron_centerenron_heardenron |
| 364 | revised - markbernsteinenroncom - markmcconnellenroncom - technical - review | 20 | 364_revised_markbernsteinenroncom_markmcconnellenroncom_technical |
| 365 | sap - sapsecurityenroncom - enterprise - approvergpg - accountantgpg | 20 | 365_sap_sapsecurityenroncom_enterprise_approvergpg |
| 366 | schedules2002010816txt - schedules2002012505txt - schedules2002010614txt - schedules2002010810txt - schedules2002010608txt | 20 | 366_schedules2002010816txt_schedules2002012505txt_schedules2002010614txt_schedules2002010810txt |
| 367 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000082135pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081799pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081127pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081214pageapproval | 20 | 367_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000082135pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081799pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081127pageapproval |
| 368 | payrollenronenroncom - enronpayrollenroncom - payroll - paycheck - paychecks | 20 | 368_payrollenronenroncom_enronpayrollenroncom_payroll_paycheck |
| 369 | postmasterenroncom - recipients - delivery - notification - relayed | 20 | 369_postmasterenroncom_recipients_delivery_notification |
| 370 | dvds - dvd - vhs - httpwwwnetworksstatuscomdvddvd25 - copy | 20 | 370_dvds_dvd_vhs_httpwwwnetworksstatuscomdvddvd25 |
| 371 | enron - enrons - ldayenroncom - willisphilipenroncom - kevinclineenroncom | 20 | 371_enron_enrons_ldayenroncom_willisphilipenroncom |
| 372 | lunch - chris - germanyhouectect - germany - wed | 20 | 372_lunch_chris_germanyhouectect_germany |
| 373 | passwords - password - passworddoc - enrononline - security | 20 | 373_passwords_password_passworddoc_enrononline |
| 374 | schedules2002010502txt - schedules2002010815txt - schedules2002010813txt - schedules2002010320txt - schedules2002010804txt | 20 | 374_schedules2002010502txt_schedules2002010815txt_schedules2002010813txt_schedules2002010320txt |
| 375 | documents - hawaii - wordperfect - enronhawaii - amendedrestated | 20 | 375_documents_hawaii_wordperfect_enronhawaii |
| 376 | espeak - enron - enronannouncementsenroncom - ethinkenroncom - ethink | 20 | 376_espeak_enron_enronannouncementsenroncom_ethinkenroncom |
| 377 | agreements - ena - contract - agreement - negotiation | 20 | 377_agreements_ena_contract_agreement |
| 378 | mailbox - outlook - mailboxoutlook - exchangeadministratorenroncom - delete | 19 | 378_mailbox_outlook_mailboxoutlook_exchangeadministratorenroncom |
| 379 | insurance - httpwwwlifeinsuranceoptionscomindexrmhtml - premium - premiums - medicaid | 19 | 379_insurance_httpwwwlifeinsuranceoptionscomindexrmhtml_premium_premiums |
| 380 | mcf - mmbtu - flow - btu - flowed | 19 | 380_mcf_mmbtu_flow_btu |
| 381 | mailbox - outlook - exchangeadministratorenroncom - delete - folder | 19 | 381_mailbox_outlook_exchangeadministratorenroncom_delete |
| 382 | providers - networker - networking - network - broadband | 19 | 382_providers_networker_networking_network |
| 383 | holiday - nytimescoms - nytimescom - nytdirectnytimescom - ny | 19 | 383_holiday_nytimescoms_nytimescom_nytdirectnytimescom |
| 384 | annuity - annuities - annuites - 2001annuities - approve | 19 | 384_annuity_annuities_annuites_2001annuities |
| 385 | fee - agreement - beta - prebon - revised | 19 | 385_fee_agreement_beta_prebon |
| 386 | gtv - gtv2 - gtvii - rollups - rollup | 19 | 386_gtv_gtv2_gtvii_rollups |
| 387 | pge - pges - regulated - utilities - pgespreadvaluationdefinition | 19 | 387_pge_pges_regulated_utilities |
| 388 | enron - enrons - bankruptcy - bankrupt - donate | 19 | 388_enron_enrons_bankruptcy_bankrupt |
| 389 | enron - correctenron - kenneththibodeauxenroncom - johnallisonenroncom - davidportenroncom | 19 | 389_enron_correctenron_kenneththibodeauxenroncom_johnallisonenroncom |
| 390 | schedules2002011505txt - schedules2002011015txt - schedules2002020115txt - schedules2002012815txt - schedules2002013015txt | 19 | 390_schedules2002011505txt_schedules2002011015txt_schedules2002020115txt_schedules2002012815txt |
| 391 | sexxxmailer - sexxxmaillistemailbuckscom - porno - nude - uncensored | 19 | 391_sexxxmailer_sexxxmaillistemailbuckscom_porno_nude |
| 392 | allencohrsenroncom - ljohnsonenroncom - kenandersonenroncom - rogerwestfallenroncom - daveneubauerenroncom | 19 | 392_allencohrsenroncom_ljohnsonenroncom_kenandersonenroncom_rogerwestfallenroncom |
| 393 | duke - contract - parkercorpenronenron - deals - llc | 19 | 393_duke_contract_parkercorpenronenron_deals |
| 394 | daughter - birth - emily - prenatal - nanny | 19 | 394_daughter_birth_emily_prenatal |
| 395 | houston - tollway - highway - greatwood - gate | 19 | 395_houston_tollway_highway_greatwood |
| 396 | schedule - scheduling - schedules - schedulesercot - portlandshiftenroncom | 19 | 396_schedule_scheduling_schedules_schedulesercot |
| 397 | wildhorse - wildhorses - wildhorse1doc - wildhorse2doc - proposal | 19 | 397_wildhorse_wildhorses_wildhorse1doc_wildhorse2doc |
| 398 | abcnewsnoweditormailabcnewsgocom - abcnews - abcnewscom - httpwwwmsnbccomnewsinfectiousfrontaspbtnmbtuhttpwwwmsnbccom - mailings | 19 | 398_abcnewsnoweditormailabcnewsgocom_abcnews_abcnewscom_httpwwwmsnbccomnewsinfectiousfrontaspbtnmbtuhttpwwwmsnbccom |
| 399 | agreements - agreementsdoc - memorandum - agreement - revisions | 19 | 399_agreements_agreementsdoc_memorandum_agreement |
| 400 | tax - taxes - reserve - valuation - accounting | 18 | 400_tax_taxes_reserve_valuation |
| 401 | realplayer - realplayerr - realnetworksr - realjukeboxr - realjukebox | 18 | 401_realplayer_realplayerr_realnetworksr_realjukeboxr |
| 402 | plattslistservplattscom - platts - unsubscribing - unsubscribe - unsubscribed | 18 | 402_plattslistservplattscom_platts_unsubscribing_unsubscribe |
| 403 | rfrankenroncom - bansharmaenroncom - jdasovicenroncom - jeffreykeelerenroncom - smaraenroncom | 18 | 403_rfrankenroncom_bansharmaenroncom_jdasovicenroncom_jeffreykeelerenroncom |
| 404 | market - nat - analysis - traders - capstonetexasnet | 18 | 404_market_nat_analysis_traders |
| 405 | amitavadharenroncom - tomhalliburtonenroncom - ainsleygaddisenroncom - kevinmooreenroncom - adamstevensenroncom | 18 | 405_amitavadharenroncom_tomhalliburtonenroncom_ainsleygaddisenroncom_kevinmooreenroncom |
| 406 | exchanges - markets - powermarketerscom - liquidity - powerplants | 18 | 406_exchanges_markets_powermarketerscom_liquidity |
| 407 | outlook - outlookteamenroncom - appointment - calendar - priority | 18 | 407_outlook_outlookteamenroncom_appointment_calendar |
| 408 | schedules2002013112txt - schedules2002011013txt - schedules2002013113txt - schedules2002013013txt - schedules2002010412txt | 18 | 408_schedules2002013112txt_schedules2002011013txt_schedules2002013113txt_schedules2002013013txt |
| 409 | schedules2002010824txt - schedules2002010613txt - schedules2002010702txt - schedules2002010405txt - schedules2002010706txt | 18 | 409_schedules2002010824txt_schedules2002010613txt_schedules2002010702txt_schedules2002010405txt |
| 410 | nancysellersrobertmondavicom - nancy - mieux - bois - chante | 18 | 410_nancysellersrobertmondavicom_nancy_mieux_bois |
| 411 | schedules2002011017txt - schedules2002013017txt - schedules2002011117txt - schedules2002011417txt - schedules2002013117txt | 18 | 411_schedules2002011017txt_schedules2002013017txt_schedules2002011117txt_schedules2002011417txt |
| 412 | credit - creditcheckr - creditcheck - equifax - loan | 18 | 412_credit_creditcheckr_creditcheck_equifax |
| 413 | edclfpcom - hacknecescecom - pikesepenergycom - carolynvealhunterasmcagov - dominicdimarecalchambercom | 18 | 413_edclfpcom_hacknecescecom_pikesepenergycom_carolynvealhunterasmcagov |
| 414 | ferc - refunds - fercs - refund - fercbriefdoc | 18 | 414_ferc_refunds_fercs_refund |
| 415 | hrefhttpwwwnytimescomemail - srchttpgraphicsnytimescomimagesemailtodaysheadlinestodayshedflaggif - srchttpwwwnytimescomimagesemailwinenytnewslettersgif - hrefhttpwwwnytimescompagesnationalindexhtmltodaysheadlinemore - hrefmailtofeedbacknytimescomfeedbacknytimescoma | 18 | 415_hrefhttpwwwnytimescomemail_srchttpgraphicsnytimescomimagesemailtodaysheadlinestodayshedflaggif_srchttpwwwnytimescomimagesemailwinenytnewslettersgif_hrefhttpwwwnytimescompagesnationalindexhtmltodaysheadlinemore |
| 416 | energy - epri - svmg - policymakers - infrastructure | 18 | 416_energy_epri_svmg_policymakers |
| 417 | lehman - economy - yield - inflation - market | 18 | 417_lehman_economy_yield_inflation |
| 418 | san - plan - los - energy - summer | 18 | 418_san_plan_los_energy |
| 419 | play - lsu - teams - plays - tournament | 18 | 419_play_lsu_teams_plays |
| 420 | griffith - enronmessagingadministrationenroncom - flows - flow - contract | 18 | 420_griffith_enronmessagingadministrationenroncom_flows_flow |
| 421 | panelists - industrials - industrial - howardfromerenroncom - roberthemstockenroncom | 17 | 421_panelists_industrials_industrial_howardfromerenroncom |
| 422 | deer - lease - hunt - ranch - hunting | 17 | 422_deer_lease_hunt_ranch |
| 423 | entex - ena - exxon - ect - enbrel | 17 | 423_entex_ena_exxon_ect |
| 424 | indexoption - indexes - index - indexforward - indices | 17 | 424_indexoption_indexes_index_indexforward |
| 425 | navajo - tribes - tribe - tribal - excavation | 17 | 425_navajo_tribes_tribe_tribal |
| 426 | energy - html - srchttpwwwenergycentralcomimagesecdecdxgif - srchttpwwwenergycentralcomimagestransgif - srchttpwwwenergycentralcomimagesiconsdiskicongif | 17 | 426_energy_html_srchttpwwwenergycentralcomimagesecdecdxgif_srchttpwwwenergycentralcomimagestransgif |
| 427 | schedules - sf - weekly - ca - francisco | 17 | 427_schedules_sf_weekly_ca |
| 428 | nadzieje - andrzej - polska - pisze - ze | 17 | 428_nadzieje_andrzej_polska_pisze |
| 429 | scholarships - scholarship - fafsa - fastweb - aidscholarship | 17 | 429_scholarships_scholarship_fafsa_fastweb |
| 430 | hourahead - hour - crawler - schedule - failed | 17 | 430_hourahead_hour_crawler_schedule |
| 431 | foolcom - foolmotleyfoolcom - fool - foolwatch - fools | 17 | 431_foolcom_foolmotleyfoolcom_fool_foolwatch |
| 432 | mailbox - outlook - exchangeadministratorenroncom - journaling - journal | 17 | 432_mailbox_outlook_exchangeadministratorenroncom_journaling |
| 433 | pipeline - logistics - pipes - pipe - transports | 17 | 433_pipeline_logistics_pipes_pipe |
| 434 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000037746page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000029730page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008254page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000077033pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000010552page | 17 | 434_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000037746page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000029730page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000008254page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000077033pageapproval |
| 435 | enron - bankruptcy - bankrupt - enrons - savings | 17 | 435_enron_bankruptcy_bankrupt_enrons |
| 436 | qfs - qf - contracts - receivables - inga | 17 | 436_qfs_qf_contracts_receivables |
| 437 | leagues - league - commissionercom - transaction - httpfootballcommissionercomenron2000 | 17 | 437_leagues_league_commissionercom_transaction |
| 438 | nepco - vepco - dpc - dpca - eeos | 17 | 438_nepco_vepco_dpc_dpca |
| 439 | transformerpleasanton - abb - transformer - transformers - contract | 17 | 439_transformerpleasanton_abb_transformer_transformers |
| 440 | invoices - contract - enfolio - revised - amendedlnk | 17 | 440_invoices_contract_enfolio_revised |
| 441 | sportslinecom - font - td - fantasy - html | 17 | 441_sportslinecom_font_td_fantasy |
| 442 | ttc - marketopshouraheadcaisocom - mktstathouraheadcaisocom - pdci - status | 17 | 442_ttc_marketopshouraheadcaisocom_mktstathouraheadcaisocom_pdci |
| 443 | danleffenroncom - edwardsacksenroncom - martysundeenroncom - jameswrightenroncom - rogeryangenroncom | 17 | 443_danleffenroncom_edwardsacksenroncom_martysundeenroncom_jameswrightenroncom |
| 444 | fitrxcom - specialsfitrxcom - drugstorecom - specials - myoplex | 17 | 444_fitrxcom_specialsfitrxcom_drugstorecom_specials |
| 445 | transmission - contract - buyresale - resale - enronioadraftdoc | 17 | 445_transmission_contract_buyresale_resale |
| 446 | wholesale - utility - customers - ena - commodity | 17 | 446_wholesale_utility_customers_ena |
| 447 | fayeellisenroncom - houstonproductenroncom - edicksonenroncom - dielterryenroncom - dianesalcidoenroncom | 17 | 447_fayeellisenroncom_houstonproductenroncom_edicksonenroncom_dielterryenroncom |
| 448 | enron - enrons - bankruptcy - bankrupt - savings | 17 | 448_enron_enrons_bankruptcy_bankrupt |
| 449 | poem - happiness - inspirational - loving - life | 16 | 449_poem_happiness_inspirational_loving |
| 450 | edicksonenroncom - ecradyenroncom - carolyngeorgeenroncom - andrewedisonenroncom - peterdelenroncom | 16 | 450_edicksonenroncom_ecradyenroncom_carolyngeorgeenroncom_andrewedisonenroncom |
| 451 | enron - enrons - bankruptcy - bankrupt - savings | 16 | 451_enron_enrons_bankruptcy_bankrupt |
| 452 | overview - brochure - meeting - robertsupertyenroncom - cus25220 | 16 | 452_overview_brochure_meeting_robertsupertyenroncom |
| 453 | pira - forecast - piras - demand - reports | 16 | 453_pira_forecast_piras_demand |
| 454 | outlook - outlookteamenroncom - mailbox - mailings - migration | 16 | 454_outlook_outlookteamenroncom_mailbox_mailings |
| 455 | fuelcell - fuel - energyrequest - hillingscorpenron - palmercorpenron | 16 | 455_fuelcell_fuel_energyrequest_hillingscorpenron |
| 456 | technologydailyalertnationaljournalcom - httpnationaljournalcommembersnews2001101024insiderhtm - technology - technologydailyalertownermail2cloakroomcom - httpnationaljournalcommembersnewsbillsbillprogresshtm | 16 | 456_technologydailyalertnationaljournalcom_httpnationaljournalcommembersnews2001101024insiderhtm_technology_technologydailyalertownermail2cloakroomcom |
| 457 | enronannouncementsenroncom - enovate - robertsupertyenroncom - susantrevinoenroncom - eb50m | 16 | 457_enronannouncementsenroncom_enovate_robertsupertyenroncom_susantrevinoenroncom |
| 458 | homecoming - reunion - 25th - dates - chuck | 16 | 458_homecoming_reunion_25th_dates |
| 459 | postmasterenroncom - smtp - mailmanenroncom - enemta01enronrelease - enemta01enroncom | 16 | 459_postmasterenroncom_smtp_mailmanenroncom_enemta01enronrelease |
| 460 | enron - enrononline - weeklyreportenroncom - europeeolenroncom - europe | 16 | 460_enron_enrononline_weeklyreportenroncom_europeeolenroncom |
| 461 | pge - pges - pgt - policies - agreements | 16 | 461_pge_pges_pgt_policies |
| 462 | houstonreportenroncom - outage - outages - scheduled - corp | 16 | 462_houstonreportenroncom_outage_outages_scheduled |
| 463 | liquidations - liquidation - pjm - liquidated - financial | 16 | 463_liquidations_liquidation_pjm_liquidated |
| 464 | craigccalpinecom - socalgas - wreedsempracom - llorenzsocalgascom - tbrillsempracom | 16 | 464_craigccalpinecom_socalgas_wreedsempracom_llorenzsocalgascom |
| 465 | runnersenroncom - track - runners - uscaa - running | 16 | 465_runnersenroncom_track_runners_uscaa |
| 466 | futures - demand - market - cash - bcfday | 16 | 466_futures_demand_market_cash |
| 467 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000057285pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080512pageapproval - pending - approval - contacting | 16 | 467_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000057285pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080512pageapproval_pending_approval |
| 468 | dbcaps97data - schedules2002011817txt - schedules2002011718txt - hourahead - schedules2002011717txt | 16 | 468_dbcaps97data_schedules2002011817txt_schedules2002011718txt_hourahead |
| 469 | broadband - providers - provider - isp - modem | 16 | 469_broadband_providers_provider_isp |
| 470 | attend - classes - recruiting - training - schedule | 16 | 470_attend_classes_recruiting_training |
| 471 | mailtonancysellersrobertmondavicom - scottwlhotmailcom - palo - recipient - eweek | 16 | 471_mailtonancysellersrobertmondavicom_scottwlhotmailcom_palo_recipient |
| 472 | suneetsharmaenroncom - geniafitzgeraldenroncom - sharencasonenroncom - jameswrightenroncom - elysekalmansenroncom | 16 | 472_suneetsharmaenroncom_geniafitzgeraldenroncom_sharencasonenroncom_jameswrightenroncom |
| 473 | shellywoodenroncom - cassandraschultzenroncom - brianenroncom - kenneththibodeauxenroncom - johnsherriffenroncom | 16 | 473_shellywoodenroncom_cassandraschultzenroncom_brianenroncom_kenneththibodeauxenroncom |
| 474 | enron - enrononline - httpwwwenroncomcorpinvestors - llc - markets | 16 | 474_enron_enrononline_httpwwwenroncomcorpinvestors_llc |
| 475 | classmatescom - classmates - alumni - alumniaccess - alumniuserboldfish2snowballcom | 15 | 475_classmatescom_classmates_alumni_alumniaccess |
| 476 | hurricane - winds - latitude - weather - bermuda | 15 | 476_hurricane_winds_latitude_weather |
| 477 | ub27045105938paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom - ub27045105390paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom - ub29066108956paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom - ub27045108461paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom - cegirnewsuma2110607joinmailccbncom | 15 | 477_ub27045105938paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom_ub27045105390paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom_ub29066108956paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom_ub27045108461paulzq4dzq4thomaszq2enronzq4comjoinmailccbncom |
| 478 | meeting - meet - lunch - wednesday - tuesday | 15 | 478_meeting_meet_lunch_wednesday |
| 479 | prayer - prayed - pray - praying - thou | 15 | 479_prayer_prayed_pray_praying |
| 480 | attachment - inline - subject - follows - 01vcorpedd4bea3f5113ae5nahoumsmbx01vcorpe | 15 | 480_attachment_inline_subject_follows |
| 481 | downtown - club - membership - clubs - fee | 15 | 481_downtown_club_membership_clubs |
| 482 | pipelines - pipeline - pipe - legislation - senate | 15 | 482_pipelines_pipeline_pipe_legislation |
| 483 | enronfcucom - enron - efcuenroncom - efcu - unionenron | 15 | 483_enronfcucom_enron_efcuenroncom_efcu |
| 484 | danleffenroncom - edisons - kevinkeeneyenroncom - jeremyblachmanenroncom - daviddelaineyenroncom | 15 | 484_danleffenroncom_edisons_kevinkeeneyenroncom_jeremyblachmanenroncom |
| 485 | receipt - received - document - lindastepenovitchbankofamericacom - email | 15 | 485_receipt_received_document_lindastepenovitchbankofamericacom |
| 486 | cnn - cnnmoney - cnns - breakingnewsmailcnncom - cnncoms | 15 | 486_cnn_cnnmoney_cnns_breakingnewsmailcnncom |
| 487 | enron - enronoptionsmailingenroncom - enronoptions - enronbenefits - enronannouncementsenroncom | 15 | 487_enron_enronoptionsmailingenroncom_enronoptions_enronbenefits |
| 488 | texasspp - aeps - aep - markets - texas | 15 | 488_texasspp_aeps_aep_markets |
| 489 | enron - financing - banks - bank - funding | 15 | 489_enron_financing_banks_bank |
| 490 | econnectvpn - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079989pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079466pageapproval - econnect - approval | 15 | 490_econnectvpn_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079989pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000079466pageapproval_econnect |
| 491 | eb3892 - eb3817 - eb - fax7136463490 - eb3880d | 15 | 491_eb3892_eb3817_eb_fax7136463490 |
| 492 | tolkien - tolkiens - taliban - qaeda - nytimescom | 15 | 492_tolkien_tolkiens_taliban_qaeda |
| 493 | enrononline5enroncom - aep - enrononline3enroncom - enrononline2enroncom - enrononline6enroncom | 15 | 493_enrononline5enroncom_aep_enrononline3enroncom_enrononline2enroncom |
| 494 | pricing - commodity - price - fuel - uranium | 15 | 494_pricing_commodity_price_fuel |
| 495 | schedules2002011506txt - schedules2002011206txt - schedules2002020206txt - schedules2002012506txt - schedules2002011006txt | 15 | 495_schedules2002011506txt_schedules2002011206txt_schedules2002020206txt_schedules2002012506txt |
| 496 | enrononline - approval - brochure - review - postcard | 15 | 496_enrononline_approval_brochure_review |
| 497 | lexisnexis - lexis - certifinder - enronpubrecdoc - filings | 15 | 497_lexisnexis_lexis_certifinder_enronpubrecdoc |
| 498 | reviewers - reviewed - prc - approved - perfmgmtenroncomenron | 15 | 498_reviewers_reviewed_prc_approved |
| 499 | payroll - germanyhouectect - germanyenroncom - accounts - germanjbasfcorpcom | 15 | 499_payroll_germanyhouectect_germanyenroncom_accounts |
| 500 | taxbills - foundmoneycom - cash - grants - foundmoney | 15 | 500_taxbills_foundmoneycom_cash_grants |
| 501 | sleuth - searches - company - research - infonautics | 15 | 501_sleuth_searches_company_research |
| 502 | gasindexlistintcxcom - powerindexlistintcxcom - index - indices - intercontinentalexchange | 15 | 502_gasindexlistintcxcom_powerindexlistintcxcom_index_indices |
| 503 | directv - channels20 - channels - satellite - free | 15 | 503_directv_channels20_channels_satellite |
| 504 | berneycaucoinenroncom - markbernsteinenroncom - joequenetenroncom - edwardbaughmanenroncom - danadavisenroncom | 15 | 504_berneycaucoinenroncom_markbernsteinenroncom_joequenetenroncom_edwardbaughmanenroncom |
| 505 | pipeline - pressures - pressure - psi - compressor | 15 | 505_pipeline_pressures_pressure_psi |
| 506 | enrononline - trader - gcpenron - traders - desk | 14 | 506_enrononline_trader_gcpenron_traders |
| 507 | managementcrd - managementgas - portfolioid - effdt - 1222000 | 14 | 507_managementcrd_managementgas_portfolioid_effdt |
| 508 | nysrc - reliability - nyisotechexchangeglobal2000net - nys - compliance | 14 | 508_nysrc_reliability_nyisotechexchangeglobal2000net_nys |
| 509 | lsu - going - tennessee - tailgating - shreveport | 14 | 509_lsu_going_tennessee_tailgating |
| 510 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000057285pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000059240pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080512pageapproval - pending - approval | 14 | 510_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000057285pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000059240pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000080512pageapproval_pending |
| 511 | dynegydirect - dynegy - dynegymarand - dynergywpd - customercaredynegycom | 14 | 511_dynegydirect_dynegy_dynegymarand_dynergywpd |
| 512 | schedules2001101503txt - schedules2001101614txt - schedules2001101620txt - schedules2001101821txt - schedules2002011316txt | 14 | 512_schedules2001101503txt_schedules2001101614txt_schedules2001101620txt_schedules2001101821txt |
| 513 | jewelry - earrings - diamonds - diamond - gemstone | 14 | 513_jewelry_earrings_diamonds_diamond |
| 514 | hotel - florence - sicily - palermo - naples | 14 | 514_hotel_florence_sicily_palermo |
| 515 | transaction - datek - order - delivery - mailbotdatekcom | 14 | 515_transaction_datek_order_delivery |
| 516 | rodeo - rodeodoc - rodeoget - rodeocarnival - houston | 14 | 516_rodeo_rodeodoc_rodeoget_rodeocarnival |
| 517 | sheetalpatelenroncom - larrycashenroncom - angiecollinsenroncom - drewhillenroncom - gregmcclendonenroncom | 14 | 517_sheetalpatelenroncom_larrycashenroncom_angiecollinsenroncom_drewhillenroncom |
| 518 | riskopstm - riskops - riskmetrics - enterprise - risk | 14 | 518_riskopstm_riskops_riskmetrics_enterprise |
| 519 | jdasovicenroncom - ehughes2enroncom - dblackenroncom - bfrankenroncom - kgustafsenroncom | 14 | 519_jdasovicenroncom_ehughes2enroncom_dblackenroncom_bfrankenroncom |
| 520 | enrononline - billfoxenroncom - eolmsteadenroncom - johnsherriffenroncom - josephhirlenroncom | 14 | 520_enrononline_billfoxenroncom_eolmsteadenroncom_johnsherriffenroncom |
| 521 | confirms - confirmations - confirming - deals - confirmation | 14 | 521_confirms_confirmations_confirming_deals |
| 522 | edison - hydroelectric - securitization - belowmarketpricepower - perpetual | 14 | 522_edison_hydroelectric_securitization_belowmarketpricepower |
| 523 | lynn - glen - laura - gisb - lindley | 14 | 523_lynn_glen_laura_gisb |
| 524 | pcas - pca - bankruptcy - creditors - filing | 14 | 524_pcas_pca_bankruptcy_creditors |
| 525 | enron - enrons - bankruptcy - bankrupt - savings | 14 | 525_enron_enrons_bankruptcy_bankrupt |
| 526 | edwardsacksenroncom - larryhunterenroncom - kevinmeredithenroncom - bernicerodriguezenroncom - adnanpatelenroncom | 14 | 526_edwardsacksenroncom_larryhunterenroncom_kevinmeredithenroncom_bernicerodriguezenroncom |
| 527 | cassandraschultzenroncom - thompsonenroncom - toddhallenroncom - susanlairdenroncom - ews | 14 | 527_cassandraschultzenroncom_thompsonenroncom_toddhallenroncom_susanlairdenroncom |
| 528 | rigzone - newsletterrigzonecom - news - httpwwwrigzonecomnewslettershowaspnid207 - httpwwwrigzonecomnewslettershowaspnid195 | 14 | 528_rigzone_newsletterrigzonecom_news_httpwwwrigzonecomnewslettershowaspnid207 |
| 529 | nng - nngs - nnorth - capacity - customers | 14 | 529_nng_nngs_nnorth_capacity |
| 530 | halloween - costumes - pumpkin - decorations - dressed | 14 | 530_halloween_costumes_pumpkin_decorations |
| 531 | izio - notified - izioautoemailconvenecom - email - iziosupportconvenecom | 14 | 531_izio_notified_izioautoemailconvenecom_email |
| 532 | berkeley - achenusibmcom - nealehaasberkeleyedu - srobinsohaasberkeleyedu - sboylehaasberkeleyedu | 14 | 532_berkeley_achenusibmcom_nealehaasberkeleyedu_srobinsohaasberkeleyedu |
| 533 | economist - economistcom - economists - economics - economy | 14 | 533_economist_economistcom_economists_economics |
| 534 | energystorage - storage - ziff - gas - ziffs | 14 | 534_energystorage_storage_ziff_gas |
| 535 | enrondoc - chase - confirms - agreement - ena | 14 | 535_enrondoc_chase_confirms_agreement |
| 536 | balances - balance - imbalance - balancing - imbalances | 14 | 536_balances_balance_imbalance_balancing |
| 537 | agreements - ecc - weatherinsurancetransformer - counterparties - isda | 14 | 537_agreements_ecc_weatherinsurancetransformer_counterparties |
| 538 | turbines - gen - generation - energypanda - emissions | 13 | 538_turbines_gen_generation_energypanda |
| 539 | telecommunications - telecom - fcc - carriers - communications | 13 | 539_telecommunications_telecom_fcc_carriers |
| 540 | venezuelan - chavez - venezuela - chavezs - venezuelas | 13 | 540_venezuelan_chavez_venezuela_chavezs |
| 541 | capacity - basin - 25000 - northwest - available | 13 | 541_capacity_basin_25000_northwest |
| 542 | borland - schedules2001040402txt - schedules2001040403txt - schedules2001102118txt - schedules2001040205txt | 13 | 542_borland_schedules2001040402txt_schedules2001040403txt_schedules2001102118txt |
| 543 | outlookteamenroncom - billwillmanenroncom - pennycraneenroncom - andregibsonenroncom - stephenstenhouseenroncom | 13 | 543_outlookteamenroncom_billwillmanenroncom_pennycraneenroncom_andregibsonenroncom |
| 544 | enrondoc - contract - authorized - trailblazeropen - approved | 13 | 544_enrondoc_contract_authorized_trailblazeropen |
| 545 | fontstyle - fontweightbold - fontfamily - bold - fontweight | 13 | 545_fontstyle_fontweightbold_fontfamily_bold |
| 546 | agreements - compression - compressor - tws - agreement | 13 | 546_agreements_compression_compressor_tws |
| 547 | enron - louisekitchenenroncom - assistant - tammieschoppeenroncom - elizabethlayenroncom | 13 | 547_enron_louisekitchenenroncom_assistant_tammieschoppeenroncom |
| 548 | tickets - concert - ticket - tour - coliseum | 13 | 548_tickets_concert_ticket_tour |
| 549 | ews - brownbag - eb - lunch - bag | 13 | 549_ews_brownbag_eb_lunch |
| 550 | accounting - liquidations - financialssettlements - settlement - balance | 13 | 550_accounting_liquidations_financialssettlements_settlement |
| 551 | writeup - assignment - write - exam - tomorrow | 13 | 551_writeup_assignment_write_exam |
| 552 | tklohmanenroncom - dthd - logistics - ets - 3143b | 13 | 552_tklohmanenroncom_dthd_logistics_ets |
| 553 | louisiana - pipeline - 800l - gasindexlistintcxcom - index | 13 | 553_louisiana_pipeline_800l_gasindexlistintcxcom |
| 554 | martysundeenroncom - scottnealenroncom - thomaswhiteenroncom - russelldykenroncom - berneyaucoinenroncom | 13 | 554_martysundeenroncom_scottnealenroncom_thomaswhiteenroncom_russelldykenroncom |
| 555 | transactions - trade - counts - cnt - count | 13 | 555_transactions_trade_counts_cnt |
| 556 | mgs - mgl - mg - mgi - mgu | 13 | 556_mgs_mgl_mg_mgi |
| 557 | profit - revenue - profits - seminar - profitable | 13 | 557_profit_revenue_profits_seminar |
| 558 | aps - pinnacle - pinnaclewe - west - confirms | 13 | 558_aps_pinnacle_pinnaclewe_west |
| 559 | sadlersaearthlinknet - edson - cjrudmansoftcomnet - robbizcwocom - cmichelicarpentersnodgrasscom | 13 | 559_sadlersaearthlinknet_edson_cjrudmansoftcomnet_robbizcwocom |
| 560 | enron - emw - leefascettienroncom - wwhiteenroncom - mloveenroncom | 13 | 560_enron_emw_leefascettienroncom_wwhiteenroncom |
| 561 | rockets - rocketscom - rocketsspurs - tickets - nba | 13 | 561_rockets_rocketscom_rocketsspurs_tickets |
| 562 | peak - enrons - prices - erv - power | 13 | 562_peak_enrons_prices_erv |
| 563 | billcordesenroncom - larryderoinenroncom - janetplaceenroncom - debbiethompsonenroncom - leehuberenroncom | 13 | 563_billcordesenroncom_larryderoinenroncom_janetplaceenroncom_debbiethompsonenroncom |
| 564 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000041547pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000053239pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000070996pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081784pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081482pageapproval | 13 | 564_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000041547pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000053239pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000070996pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000081784pageapproval |
| 565 | kudlow - kudlow102601doc - kudlowcom - kudlows - wwwkudlowcom | 13 | 565_kudlow_kudlow102601doc_kudlowcom_kudlows |
| 566 | outage - outages - scheduled - maintenance - 500pm | 13 | 566_outage_outages_scheduled_maintenance |
| 567 | prospects - prospect - businesses - industries - trueadvantages | 13 | 567_prospects_prospect_businesses_industries |
| 568 | outlook - outlookteamenroncom - appointment - tms - conference | 13 | 568_outlook_outlookteamenroncom_appointment_tms |
| 569 | kevinhannonenroncom - inezdauteriveenroncom - markkoenigenroncom - kenriceenroncom - thomaswhiteenroncom | 13 | 569_kevinhannonenroncom_inezdauteriveenroncom_markkoenigenroncom_kenriceenroncom |
| 570 | tariff - san - juan - outage - efficiencies | 13 | 570_tariff_san_juan_outage |
| 571 | enron03 - enron06 - enron04 - enron05 - enron02 | 13 | 571_enron03_enron06_enron04_enron05 |
| 572 | leagues - httpfootballfantasysportslinecomspoogers - httpfootballfantasysportslinecomgetbad - rosters - notification | 13 | 572_leagues_httpfootballfantasysportslinecomspoogers_httpfootballfantasysportslinecomgetbad_rosters |
| 573 | credit2b - credit - financial - liabilities - riskcalc | 13 | 573_credit2b_credit_financial_liabilities |
| 574 | ca - costigan - sacramento - governor - california | 13 | 574_ca_costigan_sacramento_governor |
| 575 | pgev - contract - deal - deals - nomd | 12 | 575_pgev_contract_deal_deals |
| 576 | schedulingiso - schedules2001102003txt - schedules2001101702txt - schedules2002020515txt - schedules2001102006txt | 12 | 576_schedulingiso_schedules2001102003txt_schedules2001101702txt_schedules2002020515txt |
| 577 | enrononline - enrons - johnsherriffenroncom - markkoenigenroncom - jeffskillingenroncom | 12 | 577_enrononline_enrons_johnsherriffenroncom_markkoenigenroncom |
| 578 | lewisworthyenroncom - ecp - kevinlissenroncom - filed - fcc | 12 | 578_lewisworthyenroncom_ecp_kevinlissenroncom_filed |
| 579 | pjm - portfolio - portfolioid - cct - managementcrd | 12 | 579_pjm_portfolio_portfolioid_cct |
| 580 | markets - demand - prices - refined - oilrefined | 12 | 580_markets_demand_prices_refined |
| 581 | cipco - cipcos - ntec - coop - iowa | 12 | 581_cipco_cipcos_ntec_coop |
| 582 | migrating - migration - gem - 2000 - application | 12 | 582_migrating_migration_gem_2000 |
| 583 | ge - ges - contract - indemnity - schwartzenburgenrondevelopmentenrondevelopment | 12 | 583_ge_ges_contract_indemnity |
| 584 | larrydallmanenroncom - subsidiaries - sapdataenroncom - datasapenroncom - thomasmooreenroncom | 12 | 584_larrydallmanenroncom_subsidiaries_sapdataenroncom_datasapenroncom |
| 585 | spread - value - cumulative - pgespreadvaluationdefinitiondoc - calculation | 12 | 585_spread_value_cumulative_pgespreadvaluationdefinitiondoc |
| 586 | etrade - etradecom - etrades - ameritrade020717ameritradecustomeremailcom - ameritrade | 12 | 586_etrade_etradecom_etrades_ameritrade020717ameritradecustomeremailcom |
| 587 | bpats - tariff - transmission - policy - oasispostingsllisttransmissionbpagov | 12 | 587_bpats_tariff_transmission_policy |
| 588 | bouquet - flowers - gifting - gifts - gift | 12 | 588_bouquet_flowers_gifting_gifts |
| 589 | nymex - trades - gdnymex - deals - contracts | 12 | 589_nymex_trades_gdnymex_deals |
| 590 | exchangeadministratorenroncom - recipients - email - attachment - mailtosandymbramantireliantenergycom | 12 | 590_exchangeadministratorenroncom_recipients_email_attachment |
| 591 | newpowercommunicationnewpowercom - newpower - newpowers - newpowertm - wwwnewpowercom | 12 | 591_newpowercommunicationnewpowercom_newpower_newpowers_newpowertm |
| 592 | daviddelaineyenroncom - geniafitzgeraldenroncom - markguzmanenroncom - billwilliamsenroncom - brianbierbachenroncom | 12 | 592_daviddelaineyenroncom_geniafitzgeraldenroncom_markguzmanenroncom_billwilliamsenroncom |
| 593 | powerindexlistintcxcom - hrefhttpwwwintcxcomsubscriberservletsubscriberservletclassoperationpowerindexformhuballindex - indices - hrefmailtosalesintcxcomsalesintcxcoma - hrefhttpwwwintcxcomsubscribehtmlunsubscribea | 12 | 593_powerindexlistintcxcom_hrefhttpwwwintcxcomsubscriberservletsubscriberservletclassoperationpowerindexformhuballindex_indices_hrefmailtosalesintcxcomsalesintcxcoma |
| 594 | nytimescom - newsletters - httpwwwnytimescompagesbusinessindexhtmltodaysheadline - httpwwwnytimescompagestechnologyindexhtmltodaysheadline - httpwwwnytimescompagesnationalindexhtmltodaysheadline | 12 | 594_nytimescom_newsletters_httpwwwnytimescompagesbusinessindexhtmltodaysheadline_httpwwwnytimescompagestechnologyindexhtmltodaysheadline |
| 595 | pricing - prices - price - offer - prepaid | 12 | 595_pricing_prices_price_offer |
| 596 | cp - cps - approved - power - approvals | 12 | 596_cp_cps_approved_power |
| 597 | larrylawyerenroncom - bethperlmanenroncom - stephenwallaceenroncom - crookjodyenroncom - dbaughmanenroncom | 12 | 597_larrylawyerenroncom_bethperlmanenroncom_stephenwallaceenroncom_crookjodyenroncom |
| 598 | enronbridgeline - bridgeline - enrontexaco - texacobridgeline - rogerwestbrookenroncom | 12 | 598_enronbridgeline_bridgeline_enrontexaco_texacobridgeline |
| 599 | outage - outages - scheduled - maintenance - impacted | 12 | 599_outage_outages_scheduled_maintenance |
| 600 | approver - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000012734page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009397page - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000070768pagemyreq - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000005413page | 12 | 600_approver_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000012734page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000009397page_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000070768pagemyreq |
| 601 | pjm - pjmcustomerrelationsandtrainingpjmcom - wwwpjmcom - ownerpjmcustomerinfopjmcom - pjmcustomerinforisc1pjmcom | 12 | 601_pjm_pjmcustomerrelationsandtrainingpjmcom_wwwpjmcom_ownerpjmcustomerinfopjmcom |
| 602 | swap - swaps - scheduled - revisions - reworked | 12 | 602_swap_swaps_scheduled_revisions |
| 603 | forecasts - prices - rockies - pricesmwh - peakheavy | 12 | 603_forecasts_prices_rockies_pricesmwh |
| 604 | hrefhttpwwwenergynewslivecomexperience - newscast - hrefhttpwwwrealcomreal - srchttpwwwenergynewslivecomcontentemailgraphicssignaturejpg - link0000ff | 12 | 604_hrefhttpwwwenergynewslivecomexperience_newscast_hrefhttpwwwrealcomreal_srchttpwwwenergynewslivecomcontentemailgraphicssignaturejpg |
| 605 | geocities - hosting - yahoo - internet - web | 12 | 605_geocities_hosting_yahoo_internet |
| 606 | wti - barrels - transacting - trades - crude | 12 | 606_wti_barrels_transacting_trades |
| 607 | realnetworksreg - realnetworks - realjukebox - realjukeboxreg - realplayer | 11 | 607_realnetworksreg_realnetworks_realjukebox_realjukeboxreg |
| 608 | approved - approval - approve - enrononline - eol | 11 | 608_approved_approval_approve_enrononline |
| 609 | equityalertcom - equityalert - alertsequityalertcom - alert - disclaimer | 11 | 609_equityalertcom_equityalert_alertsequityalertcom_alert |
| 610 | aol - aim - messenger - aolcom - aimaolcomredirectsaimemailaimhomehtml | 11 | 610_aol_aim_messenger_aolcom |
| 611 | outage - email - delivery - customerserviceindustrialinfocom - updates | 11 | 611_outage_email_delivery_customerserviceindustrialinfocom |
| 612 | capacity - allocations - allocating - overruns - customers | 11 | 612_capacity_allocations_allocating_overruns |
| 613 | mailbox - mail - delete - folder - deleting | 11 | 613_mailbox_mail_delete_folder |
| 614 | powermarketerscom - monthly - 196year - wholesale - merchant | 11 | 614_powermarketerscom_monthly_196year_wholesale |
| 615 | meeting - allworldwideenroncom - interoffice - houston - streamed | 11 | 615_meeting_allworldwideenroncom_interoffice_houston |
| 616 | tdickerswesterngascom - trdillwesternhubscom - porterdkscecom - askaffenergylawgroupcom - opening | 11 | 616_tdickerswesterngascom_trdillwesternhubscom_porterdkscecom_askaffenergylawgroupcom |
| 617 | enron - enronmessagingadministrationenroncom - enroncom - email - outlook | 11 | 617_enron_enronmessagingadministrationenroncom_enroncom_email |
| 618 | discount - discounts - deal - negotiated - price | 11 | 618_discount_discounts_deal_negotiated |
| 619 | enron - enrononline - dynegy - dynegyice - exchange | 11 | 619_enron_enrononline_dynegy_dynegyice |
| 620 | ca - iso - california - compliance - caiso | 11 | 620_ca_iso_california_compliance |
| 621 | outlook - outlookteamenroncom - appointment - calendar - buchanan | 11 | 621_outlook_outlookteamenroncom_appointment_calendar |
| 622 | schedules2001022813txt - schedules2001030802txt - schedules2001041822txt - schedules2001030805txt - schedules2001030801txt | 11 | 622_schedules2001022813txt_schedules2001030802txt_schedules2001041822txt_schedules2001030805txt |
| 623 | enerfax - httpwwwenerfaxcom - enerfaxwebegroupscom - enerfax1bellsouthnet - dailys | 11 | 623_enerfax_httpwwwenerfaxcom_enerfaxwebegroupscom_enerfax1bellsouthnet |
| 624 | ebill - reliantenergyebillcarecom - payment - billpay - httpsbankingwellsfargocom | 11 | 624_ebill_reliantenergyebillcarecom_payment_billpay |
| 625 | michaelbriggsreliantenergycom - smcfaddenigservicecom - benledeneaecca - spb1cwclawcom - kpooleadamsbroadwellcom | 11 | 625_michaelbriggsreliantenergycom_smcfaddenigservicecom_benledeneaecca_spb1cwclawcom |
| 626 | futures - charts - spreads - crude - httpwwwcarrfutcomresearchenergy1heatspread25pdf | 11 | 626_futures_charts_spreads_crude |
| 627 | dayahead - crawler - schedule - failed - error | 11 | 627_dayahead_crawler_schedule_failed |
| 628 | enronfacilitiesenroncom - enroncounterpartyenroncom - fayeellisenroncom - susantrevinoenroncom - scottlytleenroncom | 11 | 628_enronfacilitiesenroncom_enroncounterpartyenroncom_fayeellisenroncom_susantrevinoenroncom |
| 629 | receipt - recipient - mail - send - enronmessag | 11 | 629_receipt_recipient_mail_send |
| 630 | venture - ventures - venturewire - investors - investments | 11 | 630_venture_ventures_venturewire_investors |
| 631 | calculated - contractual - payment - amount - transaction | 11 | 631_calculated_contractual_payment_amount |
| 632 | rigzone - newsletterrigzonecom - newsletter - news - httpwwwrigzonecomnewslettershowaspnid201 | 11 | 632_rigzone_newsletterrigzonecom_newsletter_news |
| 633 | solarc - sappi - garetshawenroncom - reports - billcaseyenroncom | 11 | 633_solarc_sappi_garetshawenroncom_reports |
| 634 | danleffenroncom - martysundeenroncom - robertjohnsonenroncom - powerplants - neilbresnanenroncom | 11 | 634_danleffenroncom_martysundeenroncom_robertjohnsonenroncom_powerplants |
| 635 | forestweb - forest - news - newsbeat - forests | 11 | 635_forestweb_forest_news_newsbeat |
| 636 | antiviral - hiv - pharmaceuticals - clinical - treatment | 11 | 636_antiviral_hiv_pharmaceuticals_clinical |
| 637 | swap - amended - exchange - enron - termination | 11 | 637_swap_amended_exchange_enron |
| 638 | enronmetals - enronnetworks - enrononline - metals - aluminium | 11 | 638_enronmetals_enronnetworks_enrononline_metals |
| 639 | esmtp - postmasterenroncom - efittswebtvnet - smtp - smtpin1029bryantwebtvnet | 10 | 639_esmtp_postmasterenroncom_efittswebtvnet_smtp |
| 640 | bermudan - bermuda - reinsurance - arbitrage - eb | 10 | 640_bermudan_bermuda_reinsurance_arbitrage |
| 641 | soccer - league - canfield - teams - practice | 10 | 641_soccer_league_canfield_teams |
| 642 | enron - enrononline - eims - eim - freight | 10 | 642_enron_enrononline_eims_eim |
| 643 | send - yahoo - mail - emails - megrogers99yahoocom | 10 | 643_send_yahoo_mail_emails |
| 644 | meeting - meet - lunch - tuesday - saturday | 10 | 644_meeting_meet_lunch_tuesday |
| 645 | schedule - 10am - cst - thursday - meeting | 10 | 645_schedule_10am_cst_thursday |
| 646 | enron - enrons - bankruptcy - bankrupt - savings | 10 | 646_enron_enrons_bankruptcy_bankrupt |
| 647 | sell - selling - sale - sold - 25mw | 10 | 647_sell_selling_sale_sold |
| 648 | aps - transmission - enronmay2001xls - tranny - congestion | 10 | 648_aps_transmission_enronmay2001xls_tranny |
| 649 | bids - marketrelationsnyisocom - nyisotechexchange - bidding - auction | 10 | 649_bids_marketrelationsnyisocom_nyisotechexchange_bidding |
| 650 | httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval - httpitcappscorpenroncomsrrsauthemaillinkaspid000000000059231pageapproval - pending - approval - arsystemmailmanenroncom | 10 | 650_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000076650pageapproval_httpitcappscorpenroncomsrrsauthemaillinkaspid000000000059231pageapproval_pending_approval |
| 651 | texas - austin - ut - campus - colleges | 10 | 651_texas_austin_ut_campus |
| 652 | gamma - optiongrid - gammas - ltoptions - parameters | 10 | 652_gamma_optiongrid_gammas_ltoptions |
| 653 | enrononline - ehaedickeenroncom - taylorenroncom - tarasweitzerenroncom - sbradfordenroncom | 10 | 653_enrononline_ehaedickeenroncom_taylorenroncom_tarasweitzerenroncom |
| 654 | promotions - manager - althaus - director - vp | 10 | 654_promotions_manager_althaus_director |
</details>
## Training hyperparameters
* calculate_probabilities: False
* language: english
* low_memory: False
* min_topic_size: 10
* n_gram_range: (1, 1)
* nr_topics: None
* seed_topic_list: None
* top_n_words: 10
* verbose: False
* zeroshot_min_similarity: 0.7
* zeroshot_topic_list: None
## Framework versions
* Numpy: 1.25.2
* HDBSCAN: 0.8.33
* UMAP: 0.5.6
* Pandas: 2.0.3
* Scikit-Learn: 1.2.2
* Sentence-transformers: 2.7.0
* Transformers: 4.40.1
* Numba: 0.58.1
* Plotly: 5.15.0
* Python: 3.10.12
| {"library_name": "bertopic", "tags": ["bertopic"], "pipeline_tag": "text-classification"} | antulik/BERTopic-enron-50k | null | [
"bertopic",
"text-classification",
"region:us"
] | null | 2024-05-02T09:27:22+00:00 |
null | null | Apakah Dozerex Tablet?
Dozerex harga ialah kapsul kesihatan lelaki berkualiti premium yang diformulasikan untuk menyokong tahap kecergasan dan tenaga. Formula termajunya menggabungkan gabungan sinergistik vitamin, mineral dan ekstrak herba, yang dipilih khusus untuk menggalakkan kesihatan dan kesejahteraan optimum pada lelaki.
Laman web rasmi:<a href="https://www.nutritionsee.com/dozermlaysi">www.Dozerex.com</a>
<p><a href="https://www.nutritionsee.com/dozermlaysi"> <img src="https://www.nutritionsee.com/wp-content/uploads/2024/04/Dozerex-Malaysia-1.png" alt="enter image description here"> </a></p>
<a href="https://www.nutritionsee.com/dozermlaysi">Beli sekarang!! Klik pautan di bawah untuk maklumat lanjut dan dapatkan diskaun 50% sekarang... Cepat</a>
Laman web rasmi:<a href="https://www.nutritionsee.com/dozermlaysi">www.Dozerex.com</a> | {"license": "apache-2.0"} | Dozerex/DozerexMalaysia | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T09:27:47+00:00 |
text-generation | transformers | # merged
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
### Models Merged
The following models were included in the merge:
* [NousResearch/Llama-2-13b-hf](https://huggingface.co/NousResearch/Llama-2-13b-hf)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
dtype: float16
merge_method: passthrough
slices:
- sources:
- layer_range: [0, 24]
model: NousResearch/Llama-2-13b-hf
- sources:
- layer_range: [37, 40]
model: NousResearch/Llama-2-13b-hf
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["NousResearch/Llama-2-13b-hf"]} | Syed-Hasan-8503/Llama-2-8B-Pruned | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"base_model:NousResearch/Llama-2-13b-hf",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:28:25+00:00 |
null | null | {"license": "llama3"} | MaazKhan33/Video_to_text | null | [
"license:llama3",
"region:us"
] | null | 2024-05-02T09:29:20+00:00 |
|
text-generation | transformers |
# 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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- **Shared by [optional]:** [More Information Needed]
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## Uses
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### Downstream Use [optional]
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### 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
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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:**
[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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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | vaatsav06/Llama3_medqa_finetune | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T09:29:47+00:00 |
null | peft |
<!-- 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. -->
# Llama-3-8B-Instruct-MoE-spider
This model is a fine-tuned version of [VictorDCh/Llama-3-8B-Instruct-MoE](https://huggingface.co/VictorDCh/Llama-3-8B-Instruct-MoE) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "VictorDCh/Llama-3-8B-Instruct-MoE", "model-index": [{"name": "Llama-3-8B-Instruct-MoE-spider", "results": []}]} | VictorDCh/Llama-3-8B-Instruct-MoE-spider | null | [
"peft",
"tensorboard",
"safetensors",
"mixtral",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:VictorDCh/Llama-3-8B-Instruct-MoE",
"region:us"
] | null | 2024-05-02T09:30:48+00:00 |
null | transformers |
# 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **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] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_1e-05_Adam | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:32:17+00:00 |
null | null | {} | robzchhangte/Tokenizer-MizBERT-V4-uncased-clean | null | [
"region:us"
] | null | 2024-05-02T09:32:24+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
YetAnother_Open-Llama-3B-LoRA-OpenOrca - bnb 4bits
- Model creator: https://huggingface.co/Andron00e/
- Original model: https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca/
Original model description:
---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: question-answering
metrics:
- accuracy
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Andron00e
- **Language(s) (NLP):** Python (PyTorch, transformers, peft)
- **License:** apache-2.0
- **Finetuned from model:** openlm-research/open_llama_3b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Andron00e/Fine-Tuning-project
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/Open-Orca/OpenOrca
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Evaluation of the model was carried out using EulerAI library, more [precisely](https://github.com/EleutherAI/lm-evaluation-harness/tree/e47e01beea79cfe87421e2dac49e64d499c240b4#task-versioning)
#### Testing Data
<!-- This should link to a Data Card if possible. -->
hellaswag testing dataset
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Accuracy
### Results and Model Examination
| Task |Version| Metric |Value | |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag| 0|acc |0.4899|± |0.0050|
| | |acc_norm|0.6506|± |0.0048|
## Citations
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}
```
## Model Card Authors and Contact
[Andron00e](https://github.com/Andron00e)
| {} | RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T09:33:24+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "reinforce-model001", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "9.30 +/- 12.13", "name": "mean_reward", "verified": false}]}]}]} | pietroorlandi/reinforce-model001 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-05-02T09:34:31+00:00 |
null | null | {} | hieuGoku/monai | null | [
"tensorboard",
"region:us"
] | null | 2024-05-02T09:35:10+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** HadjYahia
- **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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | HadjYahia/lora_llama3 | null | [
"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 | 2024-05-02T09:35:20+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** ntvcie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"} | ntvcie/Phi3MiniVinhntV02 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:35:20+00:00 |
null | null | {} | Mohamedshaaban2001/model | null | [
"region:us"
] | null | 2024-05-02T09:35:25+00:00 |
|
null | null | {} | Srikanth0108/ppo-LunarLander-v2-TEST | null | [
"region:us"
] | null | 2024-05-02T09:36:29+00:00 |
|
text-classification | transformers |
<!-- 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. -->
# Access_to_Communications
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5158
- Accuracy: 0.5714
- F1: 0.5524
- Auc: 0.5833
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|
| No log | 1.0 | 15 | 0.6945 | 0.5714 | 0.5524 | 0.5 |
| No log | 2.0 | 30 | 0.7051 | 0.2857 | 0.1905 | 0.5833 |
| No log | 3.0 | 45 | 0.6958 | 0.5714 | 0.5143 | 0.5833 |
| No log | 4.0 | 60 | 0.6721 | 0.5714 | 0.5143 | 0.6667 |
| No log | 5.0 | 75 | 0.6649 | 0.5714 | 0.5143 | 0.5833 |
| No log | 6.0 | 90 | 0.6833 | 0.4286 | 0.4048 | 0.5833 |
| No log | 7.0 | 105 | 0.7160 | 0.5714 | 0.5714 | 0.5833 |
| No log | 8.0 | 120 | 0.8591 | 0.5714 | 0.5524 | 0.5833 |
| No log | 9.0 | 135 | 0.9619 | 0.4286 | 0.4286 | 0.5833 |
| No log | 10.0 | 150 | 1.2991 | 0.5714 | 0.5524 | 0.5833 |
| No log | 11.0 | 165 | 1.1564 | 0.4286 | 0.4286 | 0.5 |
| No log | 12.0 | 180 | 1.2430 | 0.5714 | 0.5524 | 0.5833 |
| No log | 13.0 | 195 | 1.4338 | 0.5714 | 0.5524 | 0.5833 |
| No log | 14.0 | 210 | 1.5501 | 0.5714 | 0.5524 | 0.5833 |
| No log | 15.0 | 225 | 1.5269 | 0.5714 | 0.5524 | 0.5833 |
| No log | 16.0 | 240 | 1.5305 | 0.5714 | 0.5524 | 0.5833 |
| No log | 17.0 | 255 | 1.5196 | 0.5714 | 0.5524 | 0.5833 |
| No log | 18.0 | 270 | 1.5175 | 0.5714 | 0.5524 | 0.5833 |
| No log | 19.0 | 285 | 1.5144 | 0.5714 | 0.5524 | 0.5833 |
| No log | 20.0 | 300 | 1.5158 | 0.5714 | 0.5524 | 0.5833 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.15.0
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "bert-base-uncased", "model-index": [{"name": "Access_to_Communications", "results": []}]} | cc5954772/Access_to_Communications | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:37:26+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# tmp_trainer
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "tmp_trainer", "results": []}]} | aritrosakar/tmp_trainer | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:38:35+00:00 |
null | null | {"license": "unlicense"} | benlow8/aitutor | null | [
"license:unlicense",
"region:us"
] | null | 2024-05-02T09:38:37+00:00 |
|
text-generation | transformers | Model Runs: [run0](https://wandb.ai/eleutherai/conditional-training/runs/z0o5ehs0), [run1](https://wandb.ai/eleutherai/conditional-training/runs/fy58oeve), [run2](https://wandb.ai/eleutherai/conditional-training/runs/0edfpkai) | {} | usvsnsp/pythia-410m-conditional-training-0.1 | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:39:17+00:00 |
text-generation | transformers |
# 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] | {"library_name": "transformers", "tags": []} | Mervyn999/mistral-7b-distilabel-mini-DPO | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-05-02T09:40:32+00:00 |
null | null | {} | yuvraj108c/ComfyUI-Upscaler-Onnx | null | [
"onnx",
"region:us"
] | null | 2024-05-02T09:41:30+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "261.63 +/- 18.99", "name": "mean_reward", "verified": false}]}]}]} | Srikanth0108/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T09:41:34+00:00 |
text-classification | transformers | {} | sstoia/CheckThat2024_task2_augmented_sigmoid_roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:42:38+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Small Hi - Sanchit Gandhi
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.1.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"language": ["hi"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Hi - Sanchit Gandhi", "results": []}]} | Ojro/whisper-tiny-mn-semi-supervised | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:42:54+00:00 |
null | null | {} | quangdan/medical_vision_lm | null | [
"region:us"
] | null | 2024-05-02T09:43:35+00:00 |
|
null | null | {} | apeterxyz/sd-cats | null | [
"region:us"
] | null | 2024-05-02T09:43:51+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "282.08 +/- 21.98", "name": "mean_reward", "verified": false}]}]}]} | raydium/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T09:44:13+00:00 |
null | null | {} | vamshi3108/MultimodalHCI | null | [
"region:us"
] | null | 2024-05-02T09:44:17+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** ntvcie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"} | ntvcie/Phi3MiniVinhntV03 | null | [
"transformers",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:44:28+00:00 |
text-generation | transformers |
# 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
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
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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]
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**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. -->
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## More Information [optional]
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## Model Card Contact
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| {"library_name": "transformers", "tags": []} | fmshahata/phi-moe-8k | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:44:46+00:00 |
null | null | {"license": "mit"} | asurascientist/speechtospeechllm | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T09:45:33+00:00 |
|
null | null | {"license": "openrail"} | Sathviksoma/trial | null | [
"safetensors",
"license:openrail",
"region:us"
] | null | 2024-05-02T09:45:50+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** armanbabayan
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-2-7b-chat-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "dpo"], "base_model": "unsloth/llama-2-7b-chat-bnb-4bit"} | armanbabayan/Llama2_Immigration_Chat_16bit | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"dpo",
"conversational",
"en",
"base_model:unsloth/llama-2-7b-chat-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:46:07+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-PixelCopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "19.20 +/- 15.64", "name": "mean_reward", "verified": false}]}]}]} | elisamammi/Reinforce-PixelCopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-05-02T09:46:07+00:00 |
null | null | {} | optimum-internal-testing/optimum-neuron-cache-for-testing-uhtpu | null | [
"region:us"
] | null | 2024-05-02T09:47:07+00:00 |
|
text-generation | transformers |
# 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]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [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
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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
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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:**
[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] | {"license": "apache-2.0", "library_name": "transformers"} | chlee10/T3Q-LLM3-Llama3-sft1.0-dpo1.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:47:54+00:00 |
null | null | {} | VXAnh/Vistral-7B-Chat-20k-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T09:49:37+00:00 |
|
null | null | {"license": "openrail"} | Muradn/Beyaz_Kurt | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T09:50:59+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
YetAnother_Open-Llama-3B-LoRA-OpenOrca - bnb 8bits
- Model creator: https://huggingface.co/Andron00e/
- Original model: https://huggingface.co/Andron00e/YetAnother_Open-Llama-3B-LoRA-OpenOrca/
Original model description:
---
license: apache-2.0
datasets:
- Open-Orca/OpenOrca
language:
- en
library_name: transformers
pipeline_tag: question-answering
metrics:
- accuracy
---
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Andron00e
- **Language(s) (NLP):** Python (PyTorch, transformers, peft)
- **License:** apache-2.0
- **Finetuned from model:** openlm-research/open_llama_3b
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/Andron00e/Fine-Tuning-project
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
https://huggingface.co/datasets/Open-Orca/OpenOrca
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
Evaluation of the model was carried out using EulerAI library, more [precisely](https://github.com/EleutherAI/lm-evaluation-harness/tree/e47e01beea79cfe87421e2dac49e64d499c240b4#task-versioning)
#### Testing Data
<!-- This should link to a Data Card if possible. -->
hellaswag testing dataset
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
Accuracy
### Results and Model Examination
| Task |Version| Metric |Value | |Stderr|
|---------|------:|--------|-----:|---|-----:|
|hellaswag| 0|acc |0.4899|± |0.0050|
| | |acc_norm|0.6506|± |0.0048|
## Citations
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
```
@software{openlm2023openllama,
author = {Geng, Xinyang and Liu, Hao},
title = {OpenLLaMA: An Open Reproduction of LLaMA},
month = May,
year = 2023,
url = {https://github.com/openlm-research/open_llama}
}
```
```
@software{eval-harness,
author = {Gao, Leo and
Tow, Jonathan and
Biderman, Stella and
Black, Sid and
DiPofi, Anthony and
Foster, Charles and
Golding, Laurence and
Hsu, Jeffrey and
McDonell, Kyle and
Muennighoff, Niklas and
Phang, Jason and
Reynolds, Laria and
Tang, Eric and
Thite, Anish and
Wang, Ben and
Wang, Kevin and
Zou, Andy},
title = {A framework for few-shot language model evaluation},
month = sep,
year = 2021,
publisher = {Zenodo},
version = {v0.0.1},
doi = {10.5281/zenodo.5371628},
url = {https://doi.org/10.5281/zenodo.5371628}
}
```
## Model Card Authors and Contact
[Andron00e](https://github.com/Andron00e)
| {} | RichardErkhov/Andron00e_-_YetAnother_Open-Llama-3B-LoRA-OpenOrca-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T09:51:16+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### 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
<!-- 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
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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. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | tomaszki/llama-13 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:51:35+00:00 |
text-generation | transformers |
# 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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- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### 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
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### 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
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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:**
[More Information Needed]
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[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] | {"library_name": "transformers", "tags": []} | mccartni-aws/mccartni-orpo-llama-3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:52:06+00:00 |
text-classification | transformers |
<!-- 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. -->
# TweetRoberta_5epochs
This model is a fine-tuned version of [cardiffnlp/twitter-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3387
- Accuracy: 0.9452
- F1: 0.9454
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:------:|
| 0.2755 | 1.0 | 2500 | 0.4153 | 0.9176 | 0.9129 |
| 0.2107 | 2.0 | 5000 | 0.3076 | 0.9312 | 0.9341 |
| 0.1421 | 3.0 | 7500 | 0.2812 | 0.9428 | 0.9424 |
| 0.081 | 4.0 | 10000 | 0.3304 | 0.9464 | 0.9466 |
| 0.0601 | 5.0 | 12500 | 0.3387 | 0.9452 | 0.9454 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1"], "base_model": "cardiffnlp/twitter-roberta-base-sentiment", "model-index": [{"name": "TweetRoberta_5epochs", "results": []}]} | AndreiUrsu/TweetRoberta_5epochs | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-roberta-base-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:53:53+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# thesis-bart-finetuned
This model is a fine-tuned version of [sshleifer/distilbart-cnn-6-6](https://huggingface.co/sshleifer/distilbart-cnn-6-6) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4528
- Rouge1: 40.6581
- Rouge2: 14.5046
- Rougel: 21.8144
- Rougelsum: 36.4842
- Gen Len: 141.4392
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:--------:|
| 2.5368 | 1.0 | 2811 | 2.4528 | 40.6581 | 14.5046 | 21.8144 | 36.4842 | 141.4392 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "sshleifer/distilbart-cnn-6-6", "model-index": [{"name": "thesis-bart-finetuned", "results": []}]} | roofdancer/thesis-bart-finetuned | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:sshleifer/distilbart-cnn-6-6",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:53:54+00:00 |
text-classification | transformers | {} | sstoia/CheckThat2024_task2_augmented_sigmoidweighted_roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:53:57+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [facebook/bart-large](https://huggingface.co/facebook/bart-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4843
- Rouge1: 79.689
- Rouge2: 52.3784
- Rougel: 79.7235
- Rougelsum: 79.6844
- Gen Len: 9.736
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
- label_smoothing_factor: 0.1
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "facebook/bart-large", "model-index": [{"name": "results", "results": []}]} | mlnha/results | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:54:19+00:00 |
table-question-answering | flair |
```bash
from transformers import TextStreamer
from unsloth import FastLanguageModel
import torch
alpaca_prompt = """
### Instruction:
{}
### Input:
{}
### Response:
{}"""
model, tokenizer = FastLanguageModel.from_pretrained(
model_name = "Bikas0/Bengali-Question-Answer-Llama3", # YOUR MODEL YOU USED FOR TRAINING
max_seq_length = 2048,
dtype = torch.float16,
load_in_4bit = True,
)
FastLanguageModel.for_inference(model) # Enable native 2x faster inference
inputs = tokenizer(
[
alpaca_prompt.format(
"Please provide a detailed answer to the following question", # instruction
"বাংলা একাডেমি আইন কোন কারণে সদস্যপদ বাতিল করা হবে ?", # input
# সড়ক রক্ষণাবেক্ষণ তহবিল বোর্ড আইন, ২০১৩ অনুযায়ী, তহবিলের উৎসসমূহ কী কী?
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to("cuda")
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 2048)
``` | {"language": ["bn"], "license": "mit", "library_name": "flair", "tags": ["flair"], "title": "Answering Bengali Questions using Transformers", "pipeline_tag": "table-question-answering", "base_model": "Bikas0/Bengali-Question-Answer-Llama3"} | Bikas0/Bengali-Question-Answer-Llama3 | null | [
"flair",
"safetensors",
"table-question-answering",
"bn",
"base_model:Bikas0/Bengali-Question-Answer-Llama3",
"license:mit",
"region:us"
] | null | 2024-05-02T09:54:33+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** AvizvaSolutions
- **License:** apache-2.0
- **Finetuned from model :** openchat/openchat-3.5-1210
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "openchat/openchat-3.5-1210"} | AvizvaSolutions/sftVersion-3 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:openchat/openchat-3.5-1210",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:54:41+00:00 |
null | transformers |
<!-- 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. -->
# donut_synDB_aug_ow
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1516
## 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: 7e-05
- train_batch_size: 5
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 30
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 26
- num_epochs: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8172 | 1.0 | 26 | 0.6897 |
| 0.5695 | 1.5 | 39 | 0.2902 |
| 0.2726 | 2.0 | 52 | 0.2045 |
| 0.13 | 2.5 | 65 | 0.1414 |
| 0.1012 | 3.0 | 78 | 0.1601 |
| 0.0669 | 3.5 | 91 | 0.1301 |
| 0.061 | 4.0 | 104 | 0.0996 |
| 0.0419 | 4.5 | 117 | 0.1249 |
| 0.0436 | 5.0 | 130 | 0.1206 |
| 0.037 | 5.5 | 143 | 0.1246 |
| 0.0323 | 6.0 | 156 | 0.1200 |
| 0.0266 | 6.5 | 169 | 0.1326 |
| 0.0254 | 7.0 | 182 | 0.1516 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "base_model": "naver-clova-ix/donut-base", "model-index": [{"name": "donut_synDB_aug_ow", "results": []}]} | Donut01/donut_synDB_aug_ow | null | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:54:53+00:00 |
null | null | {} | velichkosa/llama3_repairs | null | [
"region:us"
] | null | 2024-05-02T09:55:48+00:00 |
|
text-generation | transformers | {} | asprenger/Meta-Llama-3-8B-VIGGO | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:56:21+00:00 |
|
text-generation | transformers | {"license": "apache-2.0"} | stephenwalker/llama-3-huberman | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:57:22+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** jurieyel
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/tinyllama-bnb-4bit"} | jurieyel/text2sql-tinyllama-4bit-gguf-q4_K_m | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:57:25+00:00 |
automatic-speech-recognition | transformers |
# 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] | {"library_name": "transformers", "tags": []} | Mihaj/whisper-small-karelian-CodeSwitching | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:57:35+00:00 |
text-generation | transformers | {} | oofnan/stegBotX | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T09:58:20+00:00 |
|
null | null | {} | badrabbitt/text_classification_bert_110_1 | null | [
"region:us"
] | null | 2024-05-02T09:59:15+00:00 |
|
text-classification | transformers | {} | sstoia/CheckThat2024_task2_softmaxweighted_roberta | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T09:59:17+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [facebook/bart-large-cnn](https://huggingface.co/facebook/bart-large-cnn) 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: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/bart-large-cnn", "model-index": [{"name": "results", "results": []}]} | sudhanshusaxena/results | null | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-large-cnn",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:00:25+00:00 |
audio-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilhubert-bass-classifier9
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the augmented_bass_sounds dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0042
- Accuracy: 0.9994
## 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: 0.0005
- train_batch_size: 128
- eval_batch_size: 128
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0525 | 1.0 | 240 | 0.1287 | 0.9797 |
| 0.0 | 2.0 | 480 | 0.0163 | 0.9982 |
| 0.0001 | 3.0 | 720 | 0.0042 | 0.9994 |
### Framework versions
- Transformers 4.39.2
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["TheDuyx/augmented_bass_sounds"], "metrics": ["accuracy"], "base_model": "ntu-spml/distilhubert", "model-index": [{"name": "distilhubert-bass-classifier9", "results": [{"task": {"type": "audio-classification", "name": "Audio Classification"}, "dataset": {"name": "augmented_bass_sounds", "type": "TheDuyx/augmented_bass_sounds"}, "metrics": [{"type": "accuracy", "value": 0.9994121105232217, "name": "Accuracy"}]}]}]} | TheDuyx/distilhubert-bass-classifier9 | null | [
"transformers",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:TheDuyx/augmented_bass_sounds",
"base_model:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:00:33+00:00 |
null | null | {"license": "openrail"} | Coolwowsocoolwow/Plainrock124 | null | [
"license:openrail",
"region:us"
] | null | 2024-05-02T10:00:45+00:00 |
|
null | null | {} | optimum-internal-testing/optimum-neuron-cache-for-testing-vcovz | null | [
"region:us"
] | null | 2024-05-02T10:02:01+00:00 |
|
text-generation | transformers |
# 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
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[More Information Needed] | {"library_name": "transformers", "tags": []} | dellaanima/gpt_neo_1_3b_wikitext2 | null | [
"transformers",
"safetensors",
"gpt_neo",
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"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
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automatic-speech-recognition | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Ojro/whisper_tiny_semi_supervised | null | [
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sentence-similarity | sentence-transformers |
# SentenceTransformer based on sentence-transformers/stsb-distilbert-base
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) on the [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) dataset. It maps sentences & paragraphs to a 768-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/stsb-distilbert-base](https://huggingface.co/sentence-transformers/stsb-distilbert-base) <!-- at revision 82ad392c08f81be9be9bf065339670b23f2e1493 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates)
- **Language:** en
<!-- - **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': 128, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, '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("tomaarsen/stsb-distilbert-base-ocl")
# Run inference
sentences = [
'Is stretching bad?',
'Is stretching good for you?',
'If i=0; what will i=i++ do to i?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Binary Classification
* Dataset: `quora-duplicates`
* Evaluated with [<code>BinaryClassificationEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.BinaryClassificationEvaluator)
| Metric | Value |
|:-----------------------------|:-----------|
| cosine_accuracy | 0.86 |
| cosine_accuracy_threshold | 0.8104 |
| cosine_f1 | 0.8251 |
| cosine_f1_threshold | 0.7248 |
| cosine_precision | 0.7347 |
| cosine_recall | 0.9407 |
| cosine_ap | 0.8872 |
| dot_accuracy | 0.828 |
| dot_accuracy_threshold | 157.3549 |
| dot_f1 | 0.7899 |
| dot_f1_threshold | 145.7113 |
| dot_precision | 0.7155 |
| dot_recall | 0.8814 |
| dot_ap | 0.8369 |
| manhattan_accuracy | 0.868 |
| manhattan_accuracy_threshold | 208.0035 |
| manhattan_f1 | 0.8308 |
| manhattan_f1_threshold | 208.0035 |
| manhattan_precision | 0.7922 |
| manhattan_recall | 0.8733 |
| manhattan_ap | 0.8868 |
| euclidean_accuracy | 0.867 |
| euclidean_accuracy_threshold | 9.2694 |
| euclidean_f1 | 0.8301 |
| euclidean_f1_threshold | 9.5257 |
| euclidean_precision | 0.7888 |
| euclidean_recall | 0.876 |
| euclidean_ap | 0.8884 |
| max_accuracy | 0.868 |
| max_accuracy_threshold | 208.0035 |
| max_f1 | 0.8308 |
| max_f1_threshold | 208.0035 |
| max_precision | 0.7922 |
| max_recall | 0.9407 |
| **max_ap** | **0.8884** |
#### Paraphrase Mining
* Dataset: `quora-duplicates-dev`
* Evaluated with [<code>ParaphraseMiningEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.ParaphraseMiningEvaluator)
| Metric | Value |
|:----------------------|:-----------|
| **average_precision** | **0.5344** |
| f1 | 0.5448 |
| precision | 0.5311 |
| recall | 0.5592 |
| threshold | 0.8626 |
#### Information Retrieval
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:----------|
| cosine_accuracy@1 | 0.928 |
| cosine_accuracy@3 | 0.9712 |
| cosine_accuracy@5 | 0.9782 |
| cosine_accuracy@10 | 0.9874 |
| cosine_precision@1 | 0.928 |
| cosine_precision@3 | 0.4151 |
| cosine_precision@5 | 0.2666 |
| cosine_precision@10 | 0.1417 |
| cosine_recall@1 | 0.7994 |
| cosine_recall@3 | 0.9342 |
| cosine_recall@5 | 0.9561 |
| cosine_recall@10 | 0.9766 |
| cosine_ndcg@10 | 0.9516 |
| cosine_mrr@10 | 0.9509 |
| **cosine_map@100** | **0.939** |
| dot_accuracy@1 | 0.8926 |
| dot_accuracy@3 | 0.9518 |
| dot_accuracy@5 | 0.9658 |
| dot_accuracy@10 | 0.9768 |
| dot_precision@1 | 0.8926 |
| dot_precision@3 | 0.4027 |
| dot_precision@5 | 0.2608 |
| dot_precision@10 | 0.1388 |
| dot_recall@1 | 0.768 |
| dot_recall@3 | 0.9106 |
| dot_recall@5 | 0.9402 |
| dot_recall@10 | 0.9623 |
| dot_ndcg@10 | 0.9264 |
| dot_mrr@10 | 0.9243 |
| dot_map@100 | 0.9094 |
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## Training Details
### Training Dataset
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 100,000 training samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:---------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.5 tokens</li><li>max: 45 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.46 tokens</li><li>max: 78 tokens</li></ul> | <ul><li>0: ~64.10%</li><li>1: ~35.90%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:---------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------|
| <code>What are the best ecommerce blogs to do guest posts on about SEO to gain new clients?</code> | <code>Interested in being a guest blogger for an ecommerce marketing blog?</code> | <code>0</code> |
| <code>How do I learn Informatica online training?</code> | <code>What is Informatica online training?</code> | <code>0</code> |
| <code>What effects does marijuana use have on the flu?</code> | <code>What effects does Marijuana use have on the common cold?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
### Evaluation Dataset
#### sentence-transformers/quora-duplicates
* Dataset: [sentence-transformers/quora-duplicates](https://huggingface.co/datasets/sentence-transformers/quora-duplicates) at [451a485](https://huggingface.co/datasets/sentence-transformers/quora-duplicates/tree/451a4850bd141edb44ade1b5828c259abd762cdb)
* Size: 1,000 evaluation samples
* Columns: <code>sentence1</code>, <code>sentence2</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence1 | sentence2 | label |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:------------------------------------------------|
| type | string | string | int |
| details | <ul><li>min: 6 tokens</li><li>mean: 15.82 tokens</li><li>max: 46 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 15.91 tokens</li><li>max: 72 tokens</li></ul> | <ul><li>0: ~62.90%</li><li>1: ~37.10%</li></ul> |
* Samples:
| sentence1 | sentence2 | label |
|:------------------------------------------------------|:---------------------------------------------------|:---------------|
| <code>How should I prepare for JEE Mains 2017?</code> | <code>How do I prepare for the JEE 2016?</code> | <code>0</code> |
| <code>What is the gate exam?</code> | <code>What is the GATE exam in engineering?</code> | <code>0</code> |
| <code>Where do IRS officers get posted?</code> | <code>Does IRS Officers get posted abroad?</code> | <code>0</code> |
* Loss: [<code>OnlineContrastiveLoss</code>](https://sbert.net/docs/package_reference/losses.html#onlinecontrastiveloss)
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: False
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-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`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `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`: True
- `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`: None
- `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_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | loss | cosine_map@100 | quora-duplicates-dev_average_precision | quora-duplicates_max_ap |
|:------:|:----:|:-------------:|:------:|:--------------:|:--------------------------------------:|:-----------------------:|
| 0 | 0 | - | - | 0.9235 | 0.4200 | 0.7276 |
| 0.0640 | 100 | 2.5123 | - | - | - | - |
| 0.1280 | 200 | 2.0534 | - | - | - | - |
| 0.1599 | 250 | - | 1.7914 | 0.9127 | 0.4082 | 0.8301 |
| 0.1919 | 300 | 1.9505 | - | - | - | - |
| 0.2559 | 400 | 1.9836 | - | - | - | - |
| 0.3199 | 500 | 1.8462 | 1.5923 | 0.9190 | 0.4445 | 0.8688 |
| 0.3839 | 600 | 1.7734 | - | - | - | - |
| 0.4479 | 700 | 1.7918 | - | - | - | - |
| 0.4798 | 750 | - | 1.5461 | 0.9291 | 0.4943 | 0.8707 |
| 0.5118 | 800 | 1.6157 | - | - | - | - |
| 0.5758 | 900 | 1.7244 | - | - | - | - |
| 0.6398 | 1000 | 1.7322 | 1.5294 | 0.9309 | 0.5048 | 0.8808 |
| 0.7038 | 1100 | 1.6825 | - | - | - | - |
| 0.7678 | 1200 | 1.6823 | - | - | - | - |
| 0.7997 | 1250 | - | 1.4812 | 0.9351 | 0.5126 | 0.8865 |
| 0.8317 | 1300 | 1.5707 | - | - | - | - |
| 0.8957 | 1400 | 1.6145 | - | - | - | - |
| 0.9597 | 1500 | 1.5795 | 1.4705 | 0.9390 | 0.5344 | 0.8884 |
### Environmental Impact
Carbon emissions were measured using [CodeCarbon](https://github.com/mlco2/codecarbon).
- **Energy Consumed**: 0.040 kWh
- **Carbon Emitted**: 0.016 kg of CO2
- **Hours Used**: 0.202 hours
### Training Hardware
- **On Cloud**: No
- **GPU Model**: 1 x NVIDIA GeForce RTX 3090
- **CPU Model**: 13th Gen Intel(R) Core(TM) i7-13700K
- **RAM Size**: 31.78 GB
### Framework Versions
- Python: 3.11.6
- Sentence Transformers: 3.0.0.dev0
- Transformers: 4.41.0.dev0
- PyTorch: 2.3.0+cu121
- Accelerate: 0.26.1
- Datasets: 2.18.0
- 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",
}
```
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--> | {"language": ["en"], "library_name": "sentence-transformers", "tags": ["sentence-transformers", "sentence-similarity", "feature-extraction", "loss:OnlineContrastiveLoss"], "metrics": ["cosine_accuracy", "cosine_accuracy_threshold", "cosine_f1", "cosine_f1_threshold", "cosine_precision", "cosine_recall", "cosine_ap", "dot_accuracy", "dot_accuracy_threshold", "dot_f1", "dot_f1_threshold", "dot_precision", "dot_recall", "dot_ap", "manhattan_accuracy", "manhattan_accuracy_threshold", "manhattan_f1", "manhattan_f1_threshold", "manhattan_precision", "manhattan_recall", "manhattan_ap", "euclidean_accuracy", "euclidean_accuracy_threshold", "euclidean_f1", "euclidean_f1_threshold", "euclidean_precision", "euclidean_recall", "euclidean_ap", "max_accuracy", "max_accuracy_threshold", "max_f1", "max_f1_threshold", "max_precision", "max_recall", "max_ap", "average_precision", "f1", "precision", "recall", "threshold", "cosine_accuracy@1", "cosine_accuracy@3", "cosine_accuracy@5", "cosine_accuracy@10", "cosine_precision@1", "cosine_precision@3", "cosine_precision@5", "cosine_precision@10", "cosine_recall@1", "cosine_recall@3", "cosine_recall@5", "cosine_recall@10", "cosine_ndcg@10", "cosine_mrr@10", "cosine_map@100", "dot_accuracy@1", "dot_accuracy@3", "dot_accuracy@5", "dot_accuracy@10", "dot_precision@1", "dot_precision@3", "dot_precision@5", "dot_precision@10", "dot_recall@1", "dot_recall@3", "dot_recall@5", "dot_recall@10", "dot_ndcg@10", "dot_mrr@10", "dot_map@100"], "base_model": "sentence-transformers/stsb-distilbert-base", "widget": [{"source_sentence": "Why did he go MIA?", "sentences": ["Why did Yahoo kill Konfabulator?", "Why do people get angry with me?", "What are the best waterproof guns?"]}, {"source_sentence": "Who is a soulmate?", "sentences": ["Is she the \u201cOne\u201d?", "Who is Pakistan's biggest enemy?", "Will smoking weed help with my anxiety?"]}, {"source_sentence": "Is this poem good?", "sentences": ["Is my poem any good?", "How can I become a good speaker?", "What is feminism?"]}, {"source_sentence": "Who invented Yoga?", "sentences": ["How was yoga invented?", "Who owns this number 3152150252?", "What is Dynamics CRM Services?"]}, {"source_sentence": "Is stretching bad?", "sentences": ["Is stretching good for you?", "If i=0; what will i=i++ do to i?", "What is the Output of this C program ?"]}], "pipeline_tag": "sentence-similarity", "co2_eq_emissions": {"emissions": 15.707175691967695, "energy_consumed": 0.040409299905757354, "source": "codecarbon", "training_type": "fine-tuning", "on_cloud": false, "cpu_model": "13th Gen Intel(R) Core(TM) i7-13700K", "ram_total_size": 31.777088165283203, "hours_used": 0.202, "hardware_used": "1 x NVIDIA GeForce RTX 3090"}, "model-index": [{"name": "SentenceTransformer based on sentence-transformers/stsb-distilbert-base", "results": [{"task": {"type": "binary-classification", "name": "Binary Classification"}, "dataset": {"name": "quora duplicates", "type": "quora-duplicates"}, "metrics": [{"type": "cosine_accuracy", "value": 0.86, "name": "Cosine Accuracy"}, {"type": "cosine_accuracy_threshold", "value": 0.8104104995727539, "name": "Cosine Accuracy Threshold"}, {"type": "cosine_f1", "value": 0.8250591016548463, "name": "Cosine F1"}, {"type": "cosine_f1_threshold", "value": 0.7247534394264221, "name": "Cosine F1 Threshold"}, {"type": "cosine_precision", "value": 0.7347368421052631, "name": "Cosine Precision"}, {"type": "cosine_recall", "value": 0.9407008086253369, "name": "Cosine Recall"}, {"type": "cosine_ap", "value": 0.887247904332921, "name": "Cosine Ap"}, {"type": "dot_accuracy", "value": 0.828, "name": "Dot Accuracy"}, {"type": "dot_accuracy_threshold", "value": 157.35491943359375, "name": "Dot Accuracy Threshold"}, {"type": "dot_f1", "value": 0.7898550724637681, "name": "Dot F1"}, {"type": "dot_f1_threshold", "value": 145.7113037109375, "name": "Dot F1 Threshold"}, {"type": "dot_precision", "value": 0.7155361050328227, "name": "Dot Precision"}, {"type": "dot_recall", "value": 0.8814016172506739, "name": "Dot Recall"}, {"type": "dot_ap", "value": 0.8369433397850002, "name": "Dot Ap"}, {"type": "manhattan_accuracy", "value": 0.868, "name": "Manhattan Accuracy"}, {"type": "manhattan_accuracy_threshold", "value": 208.00347900390625, "name": "Manhattan Accuracy Threshold"}, {"type": "manhattan_f1", "value": 0.8307692307692308, "name": "Manhattan F1"}, {"type": "manhattan_f1_threshold", "value": 208.00347900390625, "name": "Manhattan F1 Threshold"}, {"type": "manhattan_precision", "value": 0.7921760391198044, "name": "Manhattan Precision"}, {"type": "manhattan_recall", "value": 0.8733153638814016, "name": "Manhattan Recall"}, {"type": "manhattan_ap", "value": 0.8868217413983182, "name": "Manhattan Ap"}, {"type": "euclidean_accuracy", "value": 0.867, "name": "Euclidean Accuracy"}, {"type": "euclidean_accuracy_threshold", "value": 9.269388198852539, "name": "Euclidean Accuracy Threshold"}, {"type": "euclidean_f1", "value": 0.8301404853128991, "name": "Euclidean F1"}, {"type": "euclidean_f1_threshold", "value": 9.525729179382324, "name": "Euclidean F1 Threshold"}, {"type": "euclidean_precision", "value": 0.7888349514563107, "name": "Euclidean Precision"}, {"type": "euclidean_recall", "value": 0.876010781671159, "name": "Euclidean Recall"}, {"type": "euclidean_ap", "value": 0.8884154240019244, "name": "Euclidean Ap"}, {"type": "max_accuracy", "value": 0.868, "name": "Max Accuracy"}, {"type": "max_accuracy_threshold", "value": 208.00347900390625, "name": "Max Accuracy Threshold"}, {"type": "max_f1", "value": 0.8307692307692308, "name": "Max F1"}, {"type": "max_f1_threshold", "value": 208.00347900390625, "name": "Max F1 Threshold"}, {"type": "max_precision", "value": 0.7921760391198044, "name": "Max Precision"}, {"type": "max_recall", "value": 0.9407008086253369, "name": "Max Recall"}, {"type": "max_ap", "value": 0.8884154240019244, "name": "Max Ap"}]}, {"task": {"type": "paraphrase-mining", "name": "Paraphrase Mining"}, "dataset": {"name": "quora duplicates dev", "type": "quora-duplicates-dev"}, "metrics": [{"type": "average_precision", "value": 0.534436244125929, "name": "Average Precision"}, {"type": "f1", "value": 0.5447997274541295, "name": "F1"}, {"type": "precision", "value": 0.5311002514589362, "name": "Precision"}, {"type": "recall", "value": 0.5592246590398161, "name": "Recall"}, {"type": "threshold", "value": 0.8626040816307068, "name": "Threshold"}]}, {"task": {"type": "information-retrieval", "name": "Information Retrieval"}, "dataset": {"name": "Unknown", "type": "unknown"}, "metrics": [{"type": "cosine_accuracy@1", "value": 0.928, "name": "Cosine Accuracy@1"}, {"type": "cosine_accuracy@3", "value": 0.9712, "name": "Cosine Accuracy@3"}, {"type": "cosine_accuracy@5", "value": 0.9782, "name": "Cosine Accuracy@5"}, {"type": "cosine_accuracy@10", "value": 0.9874, "name": "Cosine Accuracy@10"}, {"type": "cosine_precision@1", "value": 0.928, "name": "Cosine Precision@1"}, {"type": "cosine_precision@3", "value": 0.4151333333333334, "name": "Cosine Precision@3"}, {"type": "cosine_precision@5", "value": 0.26656, "name": "Cosine Precision@5"}, {"type": "cosine_precision@10", "value": 0.14166, "name": "Cosine Precision@10"}, {"type": "cosine_recall@1", "value": 0.7993523853760618, "name": "Cosine Recall@1"}, {"type": "cosine_recall@3", "value": 0.9341884771405065, "name": "Cosine Recall@3"}, {"type": "cosine_recall@5", "value": 0.9560896250710075, "name": "Cosine Recall@5"}, {"type": "cosine_recall@10", "value": 0.9766088525134997, "name": "Cosine Recall@10"}, {"type": "cosine_ndcg@10", "value": 0.9516150309696244, "name": "Cosine Ndcg@10"}, {"type": "cosine_mrr@10", "value": 0.9509392857142857, "name": "Cosine Mrr@10"}, {"type": "cosine_map@100", "value": 0.9390263696194139, "name": "Cosine Map@100"}, {"type": "dot_accuracy@1", "value": 0.8926, "name": "Dot Accuracy@1"}, {"type": "dot_accuracy@3", "value": 0.9518, "name": "Dot Accuracy@3"}, {"type": "dot_accuracy@5", "value": 0.9658, "name": "Dot Accuracy@5"}, {"type": "dot_accuracy@10", "value": 0.9768, "name": "Dot Accuracy@10"}, {"type": "dot_precision@1", "value": 0.8926, "name": "Dot Precision@1"}, {"type": "dot_precision@3", "value": 0.40273333333333333, "name": "Dot Precision@3"}, {"type": "dot_precision@5", "value": 0.26076, "name": "Dot Precision@5"}, {"type": "dot_precision@10", "value": 0.13882, "name": "Dot Precision@10"}, {"type": "dot_recall@1", "value": 0.7679620996617761, "name": "Dot Recall@1"}, {"type": "dot_recall@3", "value": 0.9105756956997251, "name": "Dot Recall@3"}, {"type": "dot_recall@5", "value": 0.9402185219519044, "name": "Dot Recall@5"}, {"type": "dot_recall@10", "value": 0.9623418143294613, "name": "Dot Recall@10"}, {"type": "dot_ndcg@10", "value": 0.9263520741106431, "name": "Dot Ndcg@10"}, {"type": "dot_mrr@10", "value": 0.9243020634920638, "name": "Dot Mrr@10"}, {"type": "dot_map@100", "value": 0.9094019438194247, "name": "Dot Map@100"}]}]}]} | tomaarsen/stsb-distilbert-base-ocl | null | [
"sentence-transformers",
"safetensors",
"distilbert",
"sentence-similarity",
"feature-extraction",
"loss:OnlineContrastiveLoss",
"en",
"arxiv:1908.10084",
"base_model:sentence-transformers/stsb-distilbert-base",
"model-index",
"co2_eq_emissions",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:02:38+00:00 |
text-to-image | diffusers | {} | arqamwadiwala/stable-diffusion-AOK1 | null | [
"diffusers",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-05-02T10:02:57+00:00 |
|
text-generation | transformers | # jarvis_v1_folder
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using Meta-Llama-3-8B as a base.
### Models Merged
The following models were included in the merge:
* Llama-3-SauerkrautLM-8b-Instruct
### Configuration
| {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | shyamieee/JARVIS-v1.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2212.04089",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T10:03:44+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** walid-iguider
- **License:** cc-by-nc-sa-4.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
## Evaluation
For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
Here's a breakdown of the performance metrics:
| Metric | hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
|:----------------------------|:----------------------|:----------------|:---------------------|:--------|
| **Accuracy Normalized** | 0.5841 | 0.4414 | 0.5389 | 0.5214 |
---
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = AutoTokenizer.from_pretrained("FairMind/Phi-3-mini-4k-instruct-bnb-4bit-Ita")
model = AutoModelForCausalLM.from_pretrained("FairMind/Phi-3-mini-4k-instruct-bnb-4bit-Ita")
model.to(device)
generation_config = GenerationConfig(
penalty_alpha=0.6, # The values balance the model confidence and the degeneration penalty in contrastive search decoding.
do_sample = True, # Whether or not to use sampling ; use greedy decoding otherwise.
top_k=5, # The number of highest probability vocabulary tokens to keep for top-k-filtering.
temperature=0.001, # The value used to modulate the next token probabilities.
repetition_penalty=1.7, # The parameter for repetition penalty. 1.0 means no penalty.
max_new_tokens = 64, # The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
eos_token_id=tokenizer.eos_token_id, # The id of the *end-of-sequence* token.
pad_token_id=tokenizer.eos_token_id, # The id of the *padding* token.
)
def generate_answer(question):
messages = [
{"role": "user", "content": question},
]
model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
outputs = model.generate(model_inputs, generation_config=generation_config)
result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
return result
question = """Quale è la torre più famosa di Parigi?"""
answer = generate_answer(question)
print(answer)
```
---
This 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) | {"language": ["it"], "license": "cc-by-nc-sa-4.0", "tags": ["text-generation-inference", "transformers", "unsloth", "trl", "sft", "phi-3", "phi-3-mini", "italian"], "datasets": ["mchl-labs/stambecco_data_it"], "base_model": "unsloth/Phi-3-mini-4k-instruct-bnb-4bit"} | FairMind/Phi-3-mini-4k-instruct-bnb-4bit-Ita | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"phi-3",
"phi-3-mini",
"italian",
"conversational",
"it",
"dataset:mchl-labs/stambecco_data_it",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:04:03+00:00 |
text-generation | transformers |
# 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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<!-- 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).
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## Technical Specifications [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/toggibf | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:04:31+00:00 |
text-generation | transformers |
# 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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This 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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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/uwvnhsl | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T10:04:36+00:00 |
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