Split (1)

train · 844k rows

modelId stringlengths 5 139	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]date 2020-02-15 11:33:14 2025-06-26 18:27:55	downloads int64 0 223M	likes int64 0 11.7k	library_name stringclasses 499 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 54 values	createdAt timestamp[us, tz=UTC]date 2022-03-02 23:29:04 2025-06-26 18:27:32	card stringlengths 11 1.01M
trapoom555/Gemma-2B-Text-Embedding-cft-checkpoints	trapoom555	2024-05-08T09:11:30Z	0	1	transformers	[ "transformers", "safetensors", "sentence-embedding", "sentence-similarity", "feature-extraction", "en", "license:mit", "endpoints_compatible", "region:us" ]	sentence-similarity	2024-05-08T06:09:29Z	--- license: mit language: - en tags: - sentence-embedding - sentence-similarity - transformers - feature-extraction pipeline_tag: sentence-similarity --- # Gemma-2B-Text-Embedding-cft-checkpoints All checkpoints of [trapoom555/Gemma-2B-Text-Embedding-cft](https://huggingface.co/trapoom555/Gemma-2B-Text-Embedding-cft).
lupobricco/irony_classification_single_label_base	lupobricco	2024-05-08T09:10:14Z	105	0	transformers	[ "transformers", "safetensors", "camembert", "text-classification", "generated_from_trainer", "base_model:Musixmatch/umberto-commoncrawl-cased-v1", "base_model:finetune:Musixmatch/umberto-commoncrawl-cased-v1", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T08:52:38Z	--- base_model: Musixmatch/umberto-commoncrawl-cased-v1 tags: - generated_from_trainer metrics: - accuracy - f1 model-index: - name: irony_classification_single_label_base results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # irony_classification_single_label_base This model is a fine-tuned version of [Musixmatch/umberto-commoncrawl-cased-v1](https://huggingface.co/Musixmatch/umberto-commoncrawl-cased-v1) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.9822 - Accuracy: 0.6227 - F1: 0.5853 ## 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: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.9554 \| 1.0 \| 718 \| 0.8483 \| 0.6247 \| 0.5794 \| \| 0.6941 \| 2.0 \| 1436 \| 0.9822 \| 0.6227 \| 0.5853 \| \| 0.3184 \| 3.0 \| 2154 \| 1.5308 \| 0.6206 \| 0.5835 \| \| 0.2401 \| 4.0 \| 2872 \| 2.0444 \| 0.6093 \| 0.5714 \| \| 0.1284 \| 5.0 \| 3590 \| 2.1603 \| 0.6124 \| 0.5643 \| \| 0.0646 \| 6.0 \| 4308 \| 2.3836 \| 0.6041 \| 0.5571 \| \| 0.0362 \| 7.0 \| 5026 \| 2.5046 \| 0.6268 \| 0.5635 \| \| 0.0232 \| 8.0 \| 5744 \| 2.6831 \| 0.6072 \| 0.5534 \| \| 0.024 \| 9.0 \| 6462 \| 2.7345 \| 0.6165 \| 0.5546 \| \| 0.0084 \| 10.0 \| 7180 \| 2.7679 \| 0.6144 \| 0.5616 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.3.0+cu118 - Datasets 2.19.0 - Tokenizers 0.19.1
jlbaker361/dcgan-k-text	jlbaker361	2024-05-08T09:09:25Z	0	0	null	[ "region:us" ]	null	2024-03-05T16:51:38Z	--- {} --- Creative Adversarial Network epochs: 100 dataset jlbaker361/wikiart n classes 5 batch_size 64 images where resized to 768 and then center cropped to: 512 used clip=False conditional =False discriminator parameters: init_dim: 32 final_dim 512 generator parameters: input noise_dim: 100 wandb project: https://wandb.ai/jlbaker361/creativity/runs/2lbof3jh
lilzzz/dbbuc_30p	lilzzz	2024-05-08T09:05:45Z	107	0	transformers	[ "transformers", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T09:05:26Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: dbbuc_30p results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dbbuc_30p This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1597 - Precision: 0.5256 - Recall: 0.5222 - F1: 0.5239 - Accuracy: 0.9675 ## 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 267 \| 0.1502 \| 0.3872 \| 0.3270 \| 0.3546 \| 0.9595 \| \| 0.1891 \| 2.0 \| 534 \| 0.1349 \| 0.4992 \| 0.4825 \| 0.4907 \| 0.9650 \| \| 0.1891 \| 3.0 \| 801 \| 0.1412 \| 0.4708 \| 0.5254 \| 0.4966 \| 0.9642 \| \| 0.056 \| 4.0 \| 1068 \| 0.1539 \| 0.5055 \| 0.5143 \| 0.5098 \| 0.9667 \| \| 0.056 \| 5.0 \| 1335 \| 0.1597 \| 0.5256 \| 0.5222 \| 0.5239 \| 0.9675 \| ### Framework versions - Transformers 4.39.0 - Pytorch 2.1.2 - Datasets 2.19.0 - Tokenizers 0.15.2
LiteLLMs/Meta-Llama-3-13B-Instruct-GGUF	LiteLLMs	2024-05-08T09:04:44Z	347	1	transformers	[ "transformers", "gguf", "mergekit", "merge", "GGUF", "en", "base_model:meta-llama/Meta-Llama-3-8B-Instruct", "base_model:quantized:meta-llama/Meta-Llama-3-8B-Instruct", "license:other", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-08T07:02:43Z	--- language: - en license: other library_name: transformers tags: - mergekit - merge - GGUF base_model: - meta-llama/Meta-Llama-3-8B-Instruct quantized_by: andrijdavid --- # Meta-Llama-3-13B-Instruct-GGUF - Original model: [Meta-Llama-3-13B-Instruct](https://huggingface.co/andrijdavid/Meta-Llama-3-13B-Instruct) <!-- description start --> ## Description This repo contains GGUF format model files for [Meta-Llama-3-13B-Instruct](https://huggingface.co/andrijdavid/Meta-Llama-3-13B-Instruct). <!-- description end --> <!-- README_GGUF.md-about-gguf start --> ### About GGUF GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. Here is an incomplete list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). This is the source project for GGUF, providing both a Command Line Interface (CLI) and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), Known as the most widely used web UI, this project boasts numerous features and powerful extensions, and supports GPU acceleration. * [Ollama](https://github.com/jmorganca/ollama) Ollama is a lightweight and extensible framework designed for building and running language models locally. It features a simple API for creating, managing, and executing models, along with a library of pre-built models for use in various applications * [KoboldCpp](https://github.com/LostRuins/koboldcpp), A comprehensive web UI offering GPU acceleration across all platforms and architectures, particularly renowned for storytelling. * [GPT4All](https://gpt4all.io), This is a free and open source GUI that runs locally, supporting Windows, Linux, and macOS with full GPU acceleration. * [LM Studio](https://lmstudio.ai/) An intuitive and powerful local GUI for Windows and macOS (Silicon), featuring GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui). A notable web UI with a variety of unique features, including a comprehensive model library for easy model selection. * [Faraday.dev](https://faraday.dev/), An attractive, user-friendly character-based chat GUI for Windows and macOS (both Silicon and Intel), also offering GPU acceleration. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), A Python library equipped with GPU acceleration, LangChain support, and an OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), A Rust-based ML framework focusing on performance, including GPU support, and designed for ease of use. * [ctransformers](https://github.com/marella/ctransformers), A Python library featuring GPU acceleration, LangChain support, and an OpenAI-compatible AI server. * [localGPT](https://github.com/PromtEngineer/localGPT) An open-source initiative enabling private conversations with documents. <!-- README_GGUF.md-about-gguf end --> <!-- compatibility_gguf start --> ## Explanation of quantisation methods <details> <summary>Click to see details</summary> The new methods available are: * GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw) * GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw. * GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw. * GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw * GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw. </details> <!-- compatibility_gguf end --> <!-- README_GGUF.md-how-to-download start --> ## How to download GGUF files Note for manual downloaders: You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single folder. The following clients/libraries will automatically download models for you, providing a list of available models to choose from: * LM Studio * LoLLMS Web UI * Faraday.dev ### In `text-generation-webui` Under Download Model, you can enter the model repo: LiteLLMs/Meta-Llama-3-13B-Instruct-GGUF and below it, a specific filename to download, such as: Q4_0/Q4_0-00001-of-00009.gguf. Then click Download. ### On the command line, including multiple files at once I recommend using the `huggingface-hub` Python library: ```shell pip3 install huggingface-hub ``` Then you can download any individual model file to the current directory, at high speed, with a command like this: ```shell huggingface-cli download LiteLLMs/Meta-Llama-3-13B-Instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` <details> <summary>More advanced huggingface-cli download usage (click to read)</summary> You can also download multiple files at once with a pattern: ```shell huggingface-cli download LiteLLMs/Meta-Llama-3-13B-Instruct-GGUF --local-dir . --local-dir-use-symlinks False --include='Q4_Kgguf' ``` For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli). To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`: ```shell pip3 install huggingface_hub[hf_transfer] ``` And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`: ```shell HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download LiteLLMs/Meta-Llama-3-13B-Instruct-GGUF Q4_0/Q4_0-00001-of-00009.gguf --local-dir . --local-dir-use-symlinks False ``` Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command. </details> <!-- README_GGUF.md-how-to-download end --> <!-- README_GGUF.md-how-to-run start --> ## Example `llama.cpp` command Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later. ```shell ./main -ngl 35 -m Q4_0/Q4_0-00001-of-00009.gguf --color -c 8192 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "<PROMPT>" ``` Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration. Change `-c 8192` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value. If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins` For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md) ## How to run in `text-generation-webui` Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp). ## How to run from Python code You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python. ### How to load this model in Python code, using llama-cpp-python For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/). #### First install the package Run one of the following commands, according to your system: ```shell # Base ctransformers with no GPU acceleration pip install llama-cpp-python # With NVidia CUDA acceleration CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python # Or with OpenBLAS acceleration CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python # Or with CLBLast acceleration CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python # Or with AMD ROCm GPU acceleration (Linux only) CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python # Or with Metal GPU acceleration for macOS systems only CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python # In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA: $env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on" pip install llama-cpp-python ``` #### Simple llama-cpp-python example code ```python from llama_cpp import Llama # Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system. llm = Llama( model_path="./Q4_0/Q4_0-00001-of-00009.gguf", # Download the model file first n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available ) # Simple inference example output = llm( "<PROMPT>", # Prompt max_tokens=512, # Generate up to 512 tokens stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using. echo=True # Whether to echo the prompt ) # Chat Completion API llm = Llama(model_path="./Q4_0/Q4_0-00001-of-00009.gguf", chat_format="llama-2") # Set chat_format according to the model you are using llm.create_chat_completion( messages = [ {"role": "system", "content": "You are a story writing assistant."}, { "role": "user", "content": "Write a story about llamas." } ] ) ``` ## How to use with LangChain Here are guides on using llama-cpp-python and ctransformers with LangChain: * [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp) * [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers) <!-- README_GGUF.md-how-to-run end --> <!-- footer end --> <!-- original-model-card start --> # Original model card: Meta-Llama-3-13B-Instruct # Meta-Llama-3-13B-Instruct Meta-Llama-3-13B-Instruct is a [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) self-merge made with [MergeKit](https://github.com/arcee-ai/mergekit/tree/main). ## Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - layer_range: [0, 16] model: meta-llama/Meta-Llama-3-8B-Instruct - sources: - layer_range: [4, 24] model: meta-llama/Meta-Llama-3-8B-Instruct - sources: - layer_range: [8, 31] model: meta-llama/Meta-Llama-3-8B-Instruct merge_method: passthrough dtype: float16 ``` ## Usage ```python from transformers import AutoTokenizer, AutoModelForCausalLM import torch model_id = "andrijdavid/Meta-Llama-3-13B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto", ) messages = [ {"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"}, {"role": "user", "content": "Who are you?"}, ] input_ids = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) terminators = [ tokenizer.eos_token_id, tokenizer.convert_tokens_to_ids("<\|eot_id\|>") ] outputs = model.generate( input_ids, max_new_tokens=256, eos_token_id=terminators, do_sample=True, temperature=0.6, top_p=0.9, ) response = outputs[0][input_ids.shape[-1]:] print(tokenizer.decode(response, skip_special_tokens=True)) ``` <!-- original-model-card end -->
cria111/dbbuc_5p	cria111	2024-05-08T09:04:33Z	107	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T09:04:03Z	--- license: apache-2.0 base_model: distilbert/distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: dbbuc_5p results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # dbbuc_5p This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1537 - Precision: 0.5208 - Recall: 0.5159 - F1: 0.5183 - Accuracy: 0.9670 ## 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| No log \| 1.0 \| 216 \| 0.1629 \| 0.3631 \| 0.3159 \| 0.3379 \| 0.9584 \| \| No log \| 2.0 \| 432 \| 0.1414 \| 0.5027 \| 0.4429 \| 0.4709 \| 0.9653 \| \| 0.1826 \| 3.0 \| 648 \| 0.1419 \| 0.4870 \| 0.5365 \| 0.5106 \| 0.9656 \| \| 0.1826 \| 4.0 \| 864 \| 0.1527 \| 0.5222 \| 0.5048 \| 0.5133 \| 0.9670 \| \| 0.0512 \| 5.0 \| 1080 \| 0.1537 \| 0.5208 \| 0.5159 \| 0.5183 \| 0.9670 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.0+cpu - Datasets 2.18.0 - Tokenizers 0.15.2
clio-ai/recipes20M_gpt2tok	clio-ai	2024-05-08T09:04:07Z	147	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T08:51:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
aiaustin/llama-3-8b-Instruct-bnb-4bit-aiaustin-demo3	aiaustin	2024-05-08T09:02:38Z	4	0	transformers	[ "transformers", "gguf", "llama", "text-generation-inference", "unsloth", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "base_model:quantized:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-03T08:15:40Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - gguf base_model: unsloth/llama-3-8b-Instruct-bnb-4bit --- # Uploaded model - Developed by: aiaustin - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit - Trained to: convert a prompt to a team of agents into a python list of tasks that need to be completed using first principle reasoning. To get the desired effects, use the system prompt that the model was trained with: ```python system_prompt = "You are an AI task automator. You will take a users prompt and use first principle reasoning to break the prompt into tasks that you must accomplish within another chat. RESPOND TO THIS MESSAGE ONLY WITH A PYTHON FORMATTED LIST OF TASKS THAT YOU MUST COMPLETE TO TRUTHFULLY AND INTELLIGENTLY ACCOMPLISH THE USERS REQUEST. ASSUME YOU CAN SEARCH THE WEB, WRITE CODE, RUN CODE, DEBUG CODE, AND AUTOMATE ANYTHING ON THE USERS COMPUTER TO ACCOMPLISH THE PROMPT. CORRECT RESPONSE FORMAT: ['task 1', 'task 2', 'task 3']" ```
vanderlist/distilbert-base-uncased-finetuned-emotion	vanderlist	2024-05-08T09:00:06Z	121	0	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "generated_from_trainer", "dataset:emotion", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-07T13:49:52Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer datasets: - emotion metrics: - accuracy - f1 model-index: - name: distilbert-base-uncased-finetuned-emotion results: - task: name: Text Classification type: text-classification dataset: name: emotion type: emotion config: split split: validation args: split metrics: - name: Accuracy type: accuracy value: 0.9295 - name: F1 type: f1 value: 0.9294838225405171 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-emotion This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset. It achieves the following results on the evaluation set: - Loss: 0.2208 - Accuracy: 0.9295 - F1: 0.9295 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 64 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 2 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\| \| 0.8275 \| 1.0 \| 250 \| 0.3187 \| 0.907 \| 0.9061 \| \| 0.2597 \| 2.0 \| 500 \| 0.2208 \| 0.9295 \| 0.9295 \| ### Framework versions - Transformers 4.39.1 - Pytorch 2.2.1+cpu - Datasets 2.18.0 - Tokenizers 0.15.2
tedad09/PolizzeDonut-RifaGDMarks-5Epochs	tedad09	2024-05-08T08:56:55Z	49	0	transformers	[ "transformers", "tensorboard", "safetensors", "vision-encoder-decoder", "image-text-to-text", "generated_from_trainer", "dataset:imagefolder", "base_model:naver-clova-ix/donut-base", "base_model:finetune:naver-clova-ix/donut-base", "license:mit", "endpoints_compatible", "region:us" ]	image-text-to-text	2024-05-08T07:24:00Z	--- license: mit base_model: naver-clova-ix/donut-base tags: - generated_from_trainer datasets: - imagefolder model-index: - name: PolizzeDonut-RifaGDMarks-5Epochs results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # PolizzeDonut-RifaGDMarks-5Epochs 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. ## 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: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.38.2 - Pytorch 2.2.2+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2
rj1ALINT/raining-weather	rj1ALINT	2024-05-08T08:51:35Z	30	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-08T08:50:30Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### Raining_Weather on Stable Diffusion via Dreambooth #### model by rj1ALINT This your the Stable Diffusion model fine-tuned the Raining_Weather concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: <dashcam footage > of a car driving in Raining Weather You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/rj1ALINT/raining-weather/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/rj1ALINT/raining-weather/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/rj1ALINT/raining-weather/resolve/main/concept_images/2.jpeg) ![image 3](https://huggingface.co/rj1ALINT/raining-weather/resolve/main/concept_images/1.jpeg)
HausaNLP/afrisenti-yor-regression	HausaNLP	2024-05-08T08:51:14Z	110	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-07T22:42:16Z	--- library_name: transformers tags: [] --- ## AfriSenti Yoruba Sentiment Regressor Description Takes a text and predicts the sentiment value between -1 (Negative) to 1 (Positive) with 0 being Neutral. Regression Value Description: \| Value \| Sentiment \| \|--\|--\| \| -1 \| Negative \| \| 0 \| Neutral \| \| 1 \| Positive \| ## How to Get Started with the Model Use the code below to get started with the model. ``` import math import torch import pandas as pd from transformers import AutoModelForSequenceClassification, AutoTokenizer BATCH_SIZE = 32 ds = pd.read_csv('test.csv') BASE_MODEL = 'HausaNLP/afrisenti-yor-regression' device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL) nb_batches = math.ceil(len(ds)/BATCH_SIZE) y_preds = [] for i in range(nb_batches): input_texts = ds[i * BATCH_SIZE: (i+1) * BATCH_SIZE]["tweet"] encoded = tokenizer(input_texts, truncation=True, padding="max_length", max_length=256, return_tensors="pt").to(device) y_preds += model(**encoded).logits.reshape(-1).tolist() df = pd.DataFrame([ds['tweet'], ds['label'], y_preds], ["Text", "Label", "Prediction"]).T df.to_csv('predictions.csv', index=False) ```
yee0930/llama3-8b-oig-unsloth-merged	yee0930	2024-05-08T08:51:04Z	2	0	transformers	[ "transformers", "pytorch", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "sft", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "base_model:finetune:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:23:36Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl - sft base_model: unsloth/llama-3-8b-bnb-4bit --- # Uploaded model - Developed by: yee0930 - 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)
HausaNLP/afrisenti-kin-regression	HausaNLP	2024-05-08T08:49:53Z	113	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-07T21:08:17Z	--- library_name: transformers tags: [] --- ## AfriSenti Kinyarwanda Sentiment Regressor Description Takes a text and predicts the sentiment value between -1 (Negative) to 1 (Positive) with 0 being Neutral. Regression Value Description: \| Value \| Sentiment \| \|--\|--\| \| -1 \| Negative \| \| 0 \| Neutral \| \| 1 \| Positive \| ## How to Get Started with the Model Use the code below to get started with the model. ``` import math import torch import pandas as pd from transformers import AutoModelForSequenceClassification, AutoTokenizer BATCH_SIZE = 32 ds = pd.read_csv('test.csv') BASE_MODEL = 'HausaNLP/afrisenti-kin-regression' device = 'cuda' if torch.cuda.is_available() else 'cpu' tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL) model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL) nb_batches = math.ceil(len(ds)/BATCH_SIZE) y_preds = [] for i in range(nb_batches): input_texts = ds[i * BATCH_SIZE: (i+1) * BATCH_SIZE]["tweet"] encoded = tokenizer(input_texts, truncation=True, padding="max_length", max_length=256, return_tensors="pt").to(device) y_preds += model(**encoded).logits.reshape(-1).tolist() df = pd.DataFrame([ds['tweet'], ds['label'], y_preds], ["Text", "Label", "Prediction"]).T df.to_csv('predictions.csv', index=False) ```
annamalai-s/bertopic_newsgroup_minilm	annamalai-s	2024-05-08T08:49:41Z	6	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-05-08T08:49:40Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # bertopic_newsgroup_minilm 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("annamalai-s/bertopic_newsgroup_minilm") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 362 * Number of training documents: 18846 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| the - to - and - of - for \| 10 \| -1_the_to_and_of \| \| 0 \| gun - guns - firearms - weapons - militia \| 6635 \| 0_gun_guns_firearms_weapons \| \| 1 \| cramer - optilink - gay - clayton - homosexual \| 424 \| 1_cramer_optilink_gay_clayton \| \| 2 \| atheism - atheists - god - atheist - religion \| 226 \| 2_atheism_atheists_god_atheist \| \| 3 \| espn - game - abc - games - hockey \| 154 \| 3_espn_game_abc_games \| \| 4 \| monitor - monitors - vga - nanao - nec \| 146 \| 4_monitor_monitors_vga_nanao \| \| 5 \| printer - deskjet - printers - laser - hp \| 142 \| 5_printer_deskjet_printers_laser \| \| 6 \| amp - sale - speakers - sony - stereo \| 140 \| 6_amp_sale_speakers_sony \| \| 7 \| drivers - diamond - card - ati - driver \| 140 \| 7_drivers_diamond_card_ati \| \| 8 \| lib - x11r5 - usr - libxmu - ndet_loop \| 139 \| 8_lib_x11r5_usr_libxmu \| \| 9 \| 55 - 25 - pit - det - bos \| 125 \| 9_55_25_pit_det \| \| 10 \| cosmo - angmar - internet - address - mit \| 112 \| 10_cosmo_angmar_internet_address \| \| 11 \| armenian - turkish - armenians - genocide - serdar \| 111 \| 11_armenian_turkish_armenians_genocide \| \| 12 \| sky - space - billboard - vandalizing - advertising \| 109 \| 12_sky_space_billboard_vandalizing \| \| 13 \| modem - modems - fax - courier - baud \| 104 \| 13_modem_modems_fax_courier \| \| 14 \| fire - atf - fbi - survivors - dividian \| 103 \| 14_fire_atf_fbi_survivors \| \| 15 \| jews - zionism - jewish - israel - holocaust \| 103 \| 15_jews_zionism_jewish_israel \| \| 16 \| forged - locutus - colorado - infante - posts \| 102 \| 16_forged_locutus_colorado_infante \| \| 17 \| muslims - serbs - bosnia - bosnian - muslim \| 101 \| 17_muslims_serbs_bosnia_bosnian \| \| 18 \| rushdie - islam - islamic - jaeger - gregg \| 95 \| 18_rushdie_islam_islamic_jaeger \| \| 19 \| simms - simm - vram - 256k - ram \| 95 \| 19_simms_simm_vram_256k \| \| 20 \| objective - morality - moral - frank - values \| 85 \| 20_objective_morality_moral_frank \| \| 21 \| hell - eternal - god - heaven - jesus \| 83 \| 21_hell_eternal_god_heaven \| \| 22 \| microsoft - os - challenge - supporters - ms \| 82 \| 22_microsoft_os_challenge_supporters \| \| 23 \| dos - windows - window - widget - microsoft \| 80 \| 23_dos_windows_window_widget \| \| 24 \| homosexuality - homosexual - gay - paul - boswell \| 78 \| 24_homosexuality_homosexual_gay_paul \| \| 25 \| israel - arab - jews - arabs - israeli \| 78 \| 25_israel_arab_jews_arabs \| \| 26 \| clipper - phone - escrow - tap - keys \| 78 \| 26_clipper_phone_escrow_tap \| \| 27 \| dos - allocation - windows - linked - vpic46 \| 78 \| 27_dos_allocation_windows_linked \| \| 28 \| moon - billion - prize - henry - alaska \| 77 \| 28_moon_billion_prize_henry \| \| 29 \| leafs - game - wings - goal - habs \| 76 \| 29_leafs_game_wings_goal \| \| 30 \| radar - detector - detectors - alarm - valentine \| 72 \| 30_radar_detector_detectors_alarm \| \| 31 \| clipper - encryption - chip - intercon - amanda \| 70 \| 31_clipper_encryption_chip_intercon \| \| 32 \| msg - food - sensitivity - chinese - superstition \| 67 \| 32_msg_food_sensitivity_chinese \| \| 33 \| morality - moral - keith - livesey - cobb \| 64 \| 33_morality_moral_keith_livesey \| \| 34 \| nmm - traffic - behind - bike - lane \| 61 \| 34_nmm_traffic_behind_bike \| \| 35 \| games - sega - genesis - snes - cd \| 61 \| 35_games_sega_genesis_snes \| \| 36 \| swap - memory - emm386 - windows - file \| 61 \| 36_swap_memory_emm386_windows \| \| 37 \| president - stephanopoulos - myers - mr - ms \| 60 \| 37_president_stephanopoulos_myers_mr \| \| 38 \| mary - she - her - immaculate - sin \| 60 \| 38_mary_she_her_immaculate \| \| 39 \| hst - mission - servicing - solar - shuttle \| 59 \| 39_hst_mission_servicing_solar \| \| 40 \| copy - protected - protection - disks - sehari \| 59 \| 40_copy_protected_protection_disks \| \| 41 \| bmw - moa - rider - cactus - requests \| 58 \| 41_bmw_moa_rider_cactus \| \| 42 \| colormap - dpy - visual - color - window \| 58 \| 42_colormap_dpy_visual_color \| \| 43 \| points - sphere - den - p3 - p1 \| 57 \| 43_points_sphere_den_p3 \| \| 44 \| batf - warrant - assault - waco - they \| 56 \| 44_batf_warrant_assault_waco \| \| 45 \| nsa - encryption - cryptosystems - sternlight - government \| 56 \| 45_nsa_encryption_cryptosystems_sternlight \| \| 46 \| israel - lebanese - lebanon - israeli - hernlem \| 55 \| 46_israel_lebanese_lebanon_israeli \| \| 47 \| gaza - israel - palestinian - israeli - peace \| 55 \| 47_gaza_israel_palestinian_israeli \| \| 48 \| yuk - motorcycling - east - rtsg - riders \| 55 \| 48_yuk_motorcycling_east_rtsg \| \| 49 \| science - methodology - scientific - sas - fulk \| 54 \| 49_science_methodology_scientific_sas \| \| 50 \| shift - shifting - manual - transmission - automatic \| 53 \| 50_shift_shifting_manual_transmission \| \| 51 \| tax - taxes - income - vat - deficit \| 53 \| 51_tax_taxes_income_vat \| \| 52 \| window - manager - main_win - xsizehints - expose \| 52 \| 52_window_manager_main_win_xsizehints \| \| 53 \| drive - controller - drives - disk - ide \| 52 \| 53_drive_controller_drives_disk \| \| 54 \| gif - format - linux - convert - files \| 52 \| 54_gif_format_linux_convert \| \| 55 \| israeli - israel - hamid - mcrcim - israelis \| 51 \| 55_israeli_israel_hamid_mcrcim \| \| 56 \| pin - ethernet - board - card - asante \| 51 \| 56_pin_ethernet_board_card \| \| 57 \| gamma - bursters - oort - ray - cloud \| 51 \| 57_gamma_bursters_oort_ray \| \| 58 \| drive - floptical - drives - disks - hard \| 50 \| 58_drive_floptical_drives_disks \| \| 59 \| serial - modem - dtr - uart - rts \| 50 \| 59_serial_modem_dtr_uart \| \| 60 \| finland - sweden - ericsson - czech - finnish \| 49 \| 60_finland_sweden_ericsson_czech \| \| 61 \| lankford - torre - he - gilkey - hitter \| 49 \| 61_lankford_torre_he_gilkey \| \| 62 \| cd - rom - toshiba - cd300 - cdrom \| 47 \| 62_cd_rom_toshiba_cd300 \| \| 63 \| dog - dogs - springer - dod - bike \| 47 \| 63_dog_dogs_springer_dod \| \| 64 \| clutch - runs - hit - batting - rbis \| 47 \| 64_clutch_runs_hit_batting \| \| 65 \| candida - yeast - noring - systemic - infections \| 47 \| 65_candida_yeast_noring_systemic \| \| 66 \| lopez - year - he - catchers - players \| 46 \| 66_lopez_year_he_catchers \| \| 67 \| battery - batteries - concrete - acid - lead \| 46 \| 67_battery_batteries_concrete_acid \| \| 68 \| 50 - 486 - 486dx2 - cyrix - mhz \| 46 \| 68_50_486_486dx2_cyrix \| \| 69 \| scsi - ide - dma - bus - controller \| 46 \| 69_scsi_ide_dma_bus \| \| 70 \| font - fonts - truetype - atm - tt \| 45 \| 70_font_fonts_truetype_atm \| \| 71 \| drugs - drug - cocaine - illegal - marijuana \| 45 \| 71_drugs_drug_cocaine_illegal \| \| 72 \| helmet - helmets - shoei - jacket - fit \| 44 \| 72_helmet_helmets_shoei_jacket \| \| 73 \| mormon - mormons - lds - church - ceremonies \| 44 \| 73_mormon_mormons_lds_church \| \| 74 \| br - isc - steveh - thor - government \| 44 \| 74_br_isc_steveh_thor \| \| 75 \| allergy - antihistamine - shots - dyer - sleep \| 44 \| 75_allergy_antihistamine_shots_dyer \| \| 76 \| pens - caps - cup - jets - canucks \| 44 \| 76_pens_caps_cup_jets \| \| 77 \| petch - god - love - gvg47 - gvg \| 44 \| 77_petch_god_love_gvg47 \| \| 78 \| mazda - toyota - miles - car - camry \| 44 \| 78_mazda_toyota_miles_car \| \| 79 \| truth - arrogance - absolutes - absolute - christians \| 43 \| 79_truth_arrogance_absolutes_absolute \| \| 80 \| shaft - wheelies - stafford - wheelie - winona \| 43 \| 80_shaft_wheelies_stafford_wheelie \| \| 81 \| crypt - key - cryptography - des - ciphers \| 43 \| 81_crypt_key_cryptography_des \| \| 82 \| oil - drain - changing - ohio - plug \| 42 \| 82_oil_drain_changing_ohio \| \| 83 \| jewish - baseball - vb30 - lafibm - players \| 42 \| 83_jewish_baseball_vb30_lafibm \| \| 84 \| sleeve - sale - picture - cd - 45 \| 42 \| 84_sleeve_sale_picture_cd \| \| 85 \| morris - team - jays - maynard - viola \| 42 \| 85_morris_team_jays_maynard \| \| 86 \| cable - antenna - receiver - distance - tv \| 41 \| 86_cable_antenna_receiver_distance \| \| 87 \| black - king - kyle - adjective - kkopp \| 41 \| 87_black_king_kyle_adjective \| \| 88 \| countersteering - mjs - bike - countersteering_faq - lean \| 41 \| 88_countersteering_mjs_bike_countersteering_faq \| \| 89 \| cpu - fan - heat - sink - fans \| 41 \| 89_cpu_fan_heat_sink \| \| 90 \| jesus - tomb - magi - resurrection - disciples \| 41 \| 90_jesus_tomb_magi_resurrection \| \| 91 \| canon - scripture - books - bible - septuagint \| 40 \| 91_canon_scripture_books_bible \| \| 92 \| mac - disks - 800k - 44mb - read \| 40 \| 92_mac_disks_800k_44mb \| \| 93 \| keenan - rangers - hockey - messier - roger \| 40 \| 93_keenan_rangers_hockey_messier \| \| 94 \| xv - bit - 24bit - image - images \| 39 \| 94_xv_bit_24bit_image \| \| 95 \| greek - greece - turkish - greeks - turks \| 39 \| 95_greek_greece_turkish_greeks \| \| 96 \| drive - meg - ram - sale - scherf \| 39 \| 96_drive_meg_ram_sale \| \| 97 \| photography - krillean - kirlian - pictures - unlv \| 39 \| 97_photography_krillean_kirlian_pictures \| \| 98 \| monitors - hours - day - nevai - monitor \| 39 \| 98_monitors_hours_day_nevai \| \| 99 \| card - orchid - p9000 - vlb - cards \| 38 \| 99_card_orchid_p9000_vlb \| \| 100 \| sale - list - 00 - guide - shipping \| 38 \| 100_sale_list_00_guide \| \| 101 \| monitor - screen - problem - 610 - video \| 38 \| 101_monitor_screen_problem_610 \| \| 102 \| baptism - sin - aaron - infants - baptized \| 38 \| 102_baptism_sin_aaron_infants \| \| 103 \| kuwait - saudi - iraq - gulf - war \| 37 \| 103_kuwait_saudi_iraq_gulf \| \| 104 \| station - redesign - dc - shuttle - space \| 37 \| 104_station_redesign_dc_shuttle \| \| 105 \| marriage - married - marry - ceremony - marriages \| 37 \| 105_marriage_married_marry_ceremony \| \| 106 \| polygon - polygons - ___ - routine - fast \| 37 \| 106_polygon_polygons_____routine \| \| 107 \| space - shuttle - launch - afit - astronomy \| 37 \| 107_space_shuttle_launch_afit \| \| 108 \| sabres - buffalo - fuhr - boston - bruins \| 36 \| 108_sabres_buffalo_fuhr_boston \| \| 109 \| waco - reno - federal - fbi - batf \| 36 \| 109_waco_reno_federal_fbi \| \| 110 \| bike - 805 - motorcycle - ride - motorcycles \| 36 \| 110_bike_805_motorcycle_ride \| \| 111 \| phone - hook - number - line - tip \| 36 \| 111_phone_hook_number_line \| \| 112 \| phillies - phils - 1964 - bunning - reds \| 36 \| 112_phillies_phils_1964_bunning \| \| 113 \| roby - fbi - udel - chopin - compound \| 35 \| 113_roby_fbi_udel_chopin \| \| 114 \| hernia - pain - bone - radiologist - arm \| 35 \| 114_hernia_pain_bone_radiologist \| \| 115 \| sco - split - newsgroup - graphics - comp \| 35 \| 115_sco_split_newsgroup_graphics \| \| 116 \| irq - interrupt - port - com4 - com3 \| 34 \| 116_irq_interrupt_port_com4 \| \| 117 \| gopher - search - images - ftp - data \| 34 \| 117_gopher_search_images_ftp \| \| 118 \| 3d - grafsys - library - graphics - shading \| 34 \| 118_3d_grafsys_library_graphics \| \| 119 \| comet - jupiter - gehrels - orbit - sq \| 34 \| 119_comet_jupiter_gehrels_orbit \| \| 120 \| gtoal - celp - speech - compression - voice \| 34 \| 120_gtoal_celp_speech_compression \| \| 121 \| insurance - health - private - care - gld \| 34 \| 121_insurance_health_private_care \| \| 122 \| centaur - proton - energy - uranium - ryukoku \| 34 \| 122_centaur_proton_energy_uranium \| \| 123 \| easter - goddess - mithras - resurrection - pagan \| 33 \| 123_easter_goddess_mithras_resurrection \| \| 124 \| cult - cults - freemasonry - baptists - baptist \| 32 \| 124_cult_cults_freemasonry_baptists \| \| 125 \| ticket - airline - hotel - tickets - voucher \| 32 \| 125_ticket_airline_hotel_tickets \| \| 126 \| nhl - stars - team - minnesota - franchise \| 32 \| 126_nhl_stars_team_minnesota \| \| 127 \| sox - red - bosio - bosox - clemens \| 32 \| 127_sox_red_bosio_bosox \| \| 128 \| ashok - slip - packet - cwru - slipper \| 32 \| 128_ashok_slip_packet_cwru \| \| 129 \| jehovah - elohim - lord - pope - father \| 32 \| 129_jehovah_elohim_lord_pope \| \| 130 \| spacecraft - baalke - mission - galileo - pluto \| 31 \| 130_spacecraft_baalke_mission_galileo \| \| 131 \| speed - 680x0 - x86 - clock - 68040 \| 31 \| 131_speed_680x0_x86_clock \| \| 132 \| escrow - key - agencies - keys - secure \| 31 \| 132_escrow_key_agencies_keys \| \| 133 \| doctor - clinic - surgery - patient - japanese \| 31 \| 133_doctor_clinic_surgery_patient \| \| 134 \| bike - bikes - mower - sale - honda \| 31 \| 134_bike_bikes_mower_sale \| \| 135 \| wave - bikers - cage - squid - waved \| 31 \| 135_wave_bikers_cage_squid \| \| 136 \| insurance - fault - car - hail - rates \| 31 \| 136_insurance_fault_car_hail \| \| 137 \| garrett - ingres - ibm - rickert - turkey \| 30 \| 137_garrett_ingres_ibm_rickert \| \| 138 \| theism - fanatism - frank - dwyer - belief \| 30 \| 138_theism_fanatism_frank_dwyer \| \| 139 \| migraine - pain - migraines - zisfein - headache \| 30 \| 139_migraine_pain_migraines_zisfein \| \| 140 \| 130 - boyle - road - speed - roads \| 28 \| 140_130_boyle_road_speed \| \| 141 \| satellite - digex - satellites - access - drag \| 28 \| 141_satellite_digex_satellites_access \| \| 142 \| 610 - centris - iivx - lciii - c610 \| 28 \| 142_610_centris_iivx_lciii \| \| 143 \| depression - prozac - thyroid - thyroxin - nutrition \| 28 \| 143_depression_prozac_thyroid_thyroxin \| \| 144 \| journalism - baseball - dwarner - bolick - dodgers \| 28 \| 144_journalism_baseball_dwarner_bolick \| \| 145 \| tempest - holland - northeastern - monitor - colostate \| 28 \| 145_tempest_holland_northeastern_monitor \| \| 146 \| 00 - wolverine - 1st - 50 - comics \| 28 \| 146_00_wolverine_1st_50 \| \| 147 \| murray - gm - wings - ottawa - lindros \| 28 \| 147_murray_gm_wings_ottawa \| \| 148 \| duo - 230 - beeps - chimes - machine \| 27 \| 148_duo_230_beeps_chimes \| \| 149 \| mr2 - engine - clutch - eliot - noisy \| 27 \| 149_mr2_engine_clutch_eliot \| \| 150 \| christianity - convenient - christian - definition - christians \| 27 \| 150_christianity_convenient_christian_definition \| \| 151 \| satan - ra - god - lucifer - heaven \| 27 \| 151_satan_ra_god_lucifer \| \| 152 \| summer - room - sublet - jhuvm - bedroom \| 26 \| 152_summer_room_sublet_jhuvm \| \| 153 \| software - wingert - level - sci - space \| 26 \| 153_software_wingert_level_sci \| \| 154 \| god - jesus - malcolm - royalroads - law \| 26 \| 154_god_jesus_malcolm_royalroads \| \| 155 \| europeans - nhl - rauser - players - european \| 26 \| 155_europeans_nhl_rauser_players \| \| 156 \| mustang - camaro - ford - howell - firebird \| 25 \| 156_mustang_camaro_ford_howell \| \| 157 \| stove - wpi - irvine - stratus - electric \| 25 \| 157_stove_wpi_irvine_stratus \| \| 158 \| scope - scopes - oscilloscope - fluke - phosphor \| 25 \| 158_scope_scopes_oscilloscope_fluke \| \| 159 \| odometer - bmw - sensor - car - dealer \| 25 \| 159_odometer_bmw_sensor_car \| \| 160 \| koresh - utarlg - sbc - uta - backing \| 25 \| 160_koresh_utarlg_sbc_uta \| \| 161 \| tape - backup - adaptec - aspi4dos - 1542 \| 25 \| 161_tape_backup_adaptec_aspi4dos \| \| 162 \| mask - goalie - gtd597a - votes - hrivnak \| 25 \| 162_mask_goalie_gtd597a_votes \| \| 163 \| astros - houston - games - rbi - sweda \| 24 \| 163_astros_houston_games_rbi \| \| 164 \| icon - icons - program - manager - vpnet \| 24 \| 164_icon_icons_program_manager \| \| 165 \| solvent - adhesive - duct - tape - ruck \| 24 \| 165_solvent_adhesive_duct_tape \| \| 166 \| keymap - key - numlock - keyboard - xterm \| 24 \| 166_keymap_key_numlock_keyboard \| \| 167 \| ir - dres - dnd - detector - cycle \| 24 \| 167_ir_dres_dnd_detector \| \| 168 \| car - dealer - price - blue - sales \| 24 \| 168_car_dealer_price_blue \| \| 169 \| midi - sound - blaster - driver - soundblaster \| 24 \| 169_midi_sound_blaster_driver \| \| 170 \| blue - boards - leds - led - green \| 24 \| 170_blue_boards_leds_led \| \| 171 \| wax - scratches - plastic - finish - paint \| 24 \| 171_wax_scratches_plastic_finish \| \| 172 \| motif - linux - bindings - xact - cose \| 24 \| 172_motif_linux_bindings_xact \| \| 173 \| v4 - v12 - cdac - v8 - ole \| 24 \| 173_v4_v12_cdac_v8 \| \| 174 \| officers - cop - mcguire - xxxx - police \| 23 \| 174_officers_cop_mcguire_xxxx \| \| 175 \| gant - hirschbeck - umpire - strike - duke \| 23 \| 175_gant_hirschbeck_umpire_strike \| \| 176 \| abortion - abortions - nyikos - choice - landreneau \| 23 \| 176_abortion_abortions_nyikos_choice \| \| 177 \| sharks - season - chuq - grade - acquired \| 23 \| 177_sharks_season_chuq_grade \| \| 178 \| punishment - penalty - capital - death - innocent \| 23 \| 178_punishment_penalty_capital_death \| \| 179 \| mouse - windows - driver - stuttgart - com3 \| 23 \| 179_mouse_windows_driver_stuttgart \| \| 180 \| processing - image - imaging - mishra - hendrix \| 23 \| 180_processing_image_imaging_mishra \| \| 181 \| freedom - virginia - beyer - ucla - ab4z \| 23 \| 181_freedom_virginia_beyer_ucla \| \| 182 \| seizures - corn - paulson - seizure - cereals \| 23 \| 182_seizures_corn_paulson_seizure \| \| 183 \| crohn - ibd - inflammation - diet - wiesel \| 23 \| 183_crohn_ibd_inflammation_diet \| \| 184 \| barbecued - foods - carcinogenic - food - meat \| 23 \| 184_barbecued_foods_carcinogenic_food \| \| 185 \| pillion - riding - advice - passenger - ride \| 22 \| 185_pillion_riding_advice_passenger \| \| 186 \| key - chip - clipper - session - encrypted \| 22 \| 186_key_chip_clipper_session \| \| 187 \| powerbook - portable - pb100 - pb - peirce \| 22 \| 187_powerbook_portable_pb100_pb \| \| 188 \| ear - ears - hearing - earwax - dizziness \| 22 \| 188_ear_ears_hearing_earwax \| \| 189 \| photoshop - adobe - rot - dgf1 - qc \| 22 \| 189_photoshop_adobe_rot_dgf1 \| \| 190 \| evolution - theory - rawlins - scharle - science \| 22 \| 190_evolution_theory_rawlins_scharle \| \| 191 \| ftp - nonibm - puff - glp - minivas \| 22 \| 191_ftp_nonibm_puff_glp \| \| 192 \| scanner - scanners - logitech - scanman - grayscale \| 22 \| 192_scanner_scanners_logitech_scanman \| \| 193 \| games - baseball - game - pitches - pitcher \| 22 \| 193_games_baseball_game_pitches \| \| 194 \| ham - interference - surges - alternator - watts \| 22 \| 194_ham_interference_surges_alternator \| \| 195 \| weight - omen - chromium - diet - fat \| 22 \| 195_weight_omen_chromium_diet \| \| 196 \| pregnency - teacher - oswego - biology - sperm \| 21 \| 196_pregnency_teacher_oswego_biology \| \| 197 \| ghostscript - postscript - ghostview - pageview - ftms \| 21 \| 197_ghostscript_postscript_ghostview_pageview \| \| 198 \| 3do - 3d - lightwave - list - imagine \| 21 \| 198_3do_3d_lightwave_list \| \| 199 \| polio - disease - alzheimer - syndrome - patients \| 21 \| 199_polio_disease_alzheimer_syndrome \| \| 200 \| motherboard - 386 - 386dx - murli - sale \| 21 \| 200_motherboard_386_386dx_murli \| \| 201 \| des - key - bits - block - attack \| 21 \| 201_des_key_bits_block \| \| 202 \| ax - max - g9v - b8f - a86 \| 21 \| 202_ax_max_g9v_b8f \| \| 203 \| israeli - biased - israel - media - none \| 21 \| 203_israeli_biased_israel_media \| \| 204 \| exhaust - carbs - intake - engine - air \| 21 \| 204_exhaust_carbs_intake_engine \| \| 205 \| tickets - 05pm - 35pm - june - ticket \| 21 \| 205_tickets_05pm_35pm_june \| \| 206 \| chain - wax - maxima - cookson - mitre \| 21 \| 206_chain_wax_maxima_cookson \| \| 207 \| toyota - cruiser - suv - jeep - explorer \| 21 \| 207_toyota_cruiser_suv_jeep \| \| 208 \| lipman - visualization - navy - graphics - seminar \| 20 \| 208_lipman_visualization_navy_graphics \| \| 209 \| dwi - speedy - driving - svoboda - liquor \| 20 \| 209_dwi_speedy_driving_svoboda \| \| 210 \| dialing - phones - tone - hugo - sweden \| 20 \| 210_dialing_phones_tone_hugo \| \| 211 \| image - processing - plplot - analysis - plotting \| 20 \| 211_image_processing_plplot_analysis \| \| 212 \| convertible - wife - car - targa - convertibles \| 20 \| 212_convertible_wife_car_targa \| \| 213 \| vuille - babb - synapse - ic - pcmcia \| 20 \| 213_vuille_babb_synapse_ic \| \| 214 \| nt - windows - chicogo - os - reimert \| 20 \| 214_nt_windows_chicogo_os \| \| 215 \| alomar - defensive - sandberg - average - career \| 20 \| 215_alomar_defensive_sandberg_average \| \| 216 \| blues - hawks - joseph - blackhawks - shanahan \| 20 \| 216_blues_hawks_joseph_blackhawks \| \| 217 \| graphics - pub - 128 - ray - ftp \| 20 \| 217_graphics_pub_128_ray \| \| 218 \| w4wg - network - windows - workgroups - lastdrive \| 20 \| 218_w4wg_network_windows_workgroups \| \| 219 \| tank - bag - goldberg - fj1100 - pouch \| 20 \| 219_tank_bag_goldberg_fj1100 \| \| 220 \| mailing - list - detweiler - mail - rdetweil \| 20 \| 220_mailing_list_detweiler_mail \| \| 221 \| gas - tear - unb - riddle - j979 \| 20 \| 221_gas_tear_unb_riddle \| \| 222 \| ide - bus - controller - vlb - scsi \| 20 \| 222_ide_bus_controller_vlb \| \| 223 \| saturn - dealer - profit - warranty - sl2 \| 19 \| 223_saturn_dealer_profit_warranty \| \| 224 \| cursor - xterm - blinking - cursors - allbery \| 19 \| 224_cursor_xterm_blinking_cursors \| \| 225 \| joystick - joysticks - arcade - port - int15h \| 19 \| 225_joystick_joysticks_arcade_port \| \| 226 \| lyme - disease - ld - infectious - patients \| 19 \| 226_lyme_disease_ld_infectious \| \| 227 \| context - jim - joslin - meritt - mwunix \| 19 \| 227_context_jim_joslin_meritt \| \| 228 \| qualcomm - clinton - qualcom - rdippold - clipper \| 19 \| 228_qualcomm_clinton_qualcom_rdippold \| \| 229 \| cancer - hiv - burzynski - breast - booklet \| 19 \| 229_cancer_hiv_burzynski_breast \| \| 230 \| kidney - stones - calcium - she - stone \| 19 \| 230_kidney_stones_calcium_she \| \| 231 \| rosicrucian - amorc - ch981 - alicea - tony \| 19 \| 231_rosicrucian_amorc_ch981_alicea \| \| 232 \| henrik - armenia - bm - armenians - karabakh \| 19 \| 232_henrik_armenia_bm_armenians \| \| 233 \| geico - insurance - claim - davew - wonnacott \| 19 \| 233_geico_insurance_claim_davew \| \| 234 \| eye - dominance - prk - handedness - rk \| 19 \| 234_eye_dominance_prk_handedness \| \| 235 \| church - churches - crossroads - movement - boston \| 19 \| 235_church_churches_crossroads_movement \| \| 236 \| water - mwra - phd - cellar - scoggin \| 19 \| 236_water_mwra_phd_cellar \| \| 237 \| integra - car - shadow - dodge - gtz \| 19 \| 237_integra_car_shadow_dodge \| \| 238 \| sabbath - worship - law - ceremonial - paul \| 19 \| 238_sabbath_worship_law_ceremonial \| \| 239 \| lobby - sammons - letter - ns111310 - colostate \| 19 \| 239_lobby_sammons_letter_ns111310 \| \| 240 \| henry - orion - film - prototype - toronto \| 18 \| 240_henry_orion_film_prototype \| \| 241 \| trinity - father - son - holy - god \| 18 \| 241_trinity_father_son_holy \| \| 242 \| captain - traded - captains - striped - resigned \| 18 \| 242_captain_traded_captains_striped \| \| 243 \| 42 - tiff - philosophical - significance - joachim \| 18 \| 243_42_tiff_philosophical_significance \| \| 244 \| space - mars - spaceflight - nick - fred \| 18 \| 244_space_mars_spaceflight_nick \| \| 245 \| astronaut - space - nasa - pilot - jemison \| 18 \| 245_astronaut_space_nasa_pilot \| \| 246 \| circumcision - cons - pros - penile - blix \| 18 \| 246_circumcision_cons_pros_penile \| \| 247 \| wire - wiring - ground - neutral - outlets \| 17 \| 247_wire_wiring_ground_neutral \| \| 248 \| women - men - monash - depression - sex \| 17 \| 248_women_men_monash_depression \| \| 249 \| prophecy - prophecies - earthquake - lord - prophesies \| 17 \| 249_prophecy_prophecies_earthquake_lord \| \| 250 \| cooling - towers - nuclear - plants - water \| 17 \| 250_cooling_towers_nuclear_plants \| \| 251 \| diesel - diesels - fuel - injector - emissions \| 17 \| 251_diesel_diesels_fuel_injector \| \| 252 \| windows - pif - dos - file - command \| 17 \| 252_windows_pif_dos_file \| \| 253 \| uv - bulb - flashlight - bulbs - neon \| 17 \| 253_uv_bulb_flashlight_bulbs \| \| 254 \| tires - tire - fluids - abs - dot \| 17 \| 254_tires_tire_fluids_abs \| \| 255 \| mhz - clock - operational - iisi - cpu \| 17 \| 255_mhz_clock_operational_iisi \| \| 256 \| cubs - braves - team - america - talent \| 17 \| 256_cubs_braves_team_america \| \| 257 \| lens - rupin - camera - dang - dartmouth \| 17 \| 257_lens_rupin_camera_dang \| \| 258 \| dock - duo - apple - bredell - deguzman \| 16 \| 258_dock_duo_apple_bredell \| \| 259 \| janet - reno - madman - children - she \| 16 \| 259_janet_reno_madman_children \| \| 260 \| lock - locks - cobra - kryptonite - cable \| 16 \| 260_lock_locks_cobra_kryptonite \| \| 261 \| mouse - jumpy - motion - byu - smoothly \| 16 \| 261_mouse_jumpy_motion_byu \| \| 262 \| god - creates - omnipotence - shaped - omnipotent \| 16 \| 262_god_creates_omnipotence_shaped \| \| 263 \| yassin - deir - irgun - dir - village \| 16 \| 263_yassin_deir_irgun_dir \| \| 264 \| xv - julian - copyright - lancs - escaped \| 16 \| 264_xv_julian_copyright_lancs \| \| 265 \| mjm - fm - circuits - mixer - musone \| 16 \| 265_mjm_fm_circuits_mixer \| \| 266 \| tga - rle - pov - povray - tmp \| 16 \| 266_tga_rle_pov_povray \| \| 267 \| workspace - managers - workspaces - manager - zip \| 16 \| 267_workspace_managers_workspaces_manager \| \| 268 \| quadra - scsi - nodine - cartridge - mac \| 16 \| 268_quadra_scsi_nodine_cartridge \| \| 269 \| hpgl - ilmenau - naplps - vuw - schmidt \| 16 \| 269_hpgl_ilmenau_naplps_vuw \| \| 270 \| jumper - 2190 - maxtor - thad - drive \| 16 \| 270_jumper_2190_maxtor_thad \| \| 271 \| dxf - iff - format - autocad - pei \| 16 \| 271_dxf_iff_format_autocad \| \| 272 \| mode - vesa - vga - svga - 640x400 \| 16 \| 272_mode_vesa_vga_svga \| \| 273 \| mosques - mosque - jerusalem - eggertj - jake \| 16 \| 273_mosques_mosque_jerusalem_eggertj \| \| 274 \| ulf - erau - huot - players - drozinst \| 15 \| 274_ulf_erau_huot_players \| \| 275 \| algorithm - secret - chip - reverse - clipper \| 15 \| 275_algorithm_secret_chip_reverse \| \| 276 \| font - fonts - alavi - ssa - 8514 \| 15 \| 276_font_fonts_alavi_ssa \| \| 277 \| gauge - nancy - gauges - temp - cigarette \| 15 \| 277_gauge_nancy_gauges_temp \| \| 278 \| octopus - detroit - ice - cunyvm - hammerl \| 15 \| 278_octopus_detroit_ice_cunyvm \| \| 279 \| cview - temp - moscom - directory - zenkar \| 15 \| 279_cview_temp_moscom_directory \| \| 280 \| drive - cable - quantum - disk - internal \| 15 \| 280_drive_cable_quantum_disk \| \| 281 \| logo - vgalogo - rle - startup - lgo \| 15 \| 281_logo_vgalogo_rle_startup \| \| 282 \| ini - updating - svein - sysedit - utility \| 15 \| 282_ini_updating_svein_sysedit \| \| 283 \| sin - hate - sinner - love - scott \| 15 \| 283_sin_hate_sinner_love \| \| 284 \| administration - privacy - eff - government - inquiry \| 15 \| 284_administration_privacy_eff_government \| \| 285 \| bonds - williams - batting - giants - clark \| 15 \| 285_bonds_williams_batting_giants \| \| 286 \| 02106 - chemistry - udel - paperback - ravel \| 15 \| 286_02106_chemistry_udel_paperback \| \| 287 \| cherry - coach - don - he - him \| 15 \| 287_cherry_coach_don_he \| \| 288 \| drink - drinking - riding - alcohol - hours \| 15 \| 288_drink_drinking_riding_alcohol \| \| 289 \| ether - planets - twist - sci - mnemonics \| 14 \| 289_ether_planets_twist_sci \| \| 290 \| keys - des - lokkur - nanosecond - keyseach \| 14 \| 290_keys_des_lokkur_nanosecond \| \| 291 \| virginia - uva - partying - andi - schools \| 14 \| 291_virginia_uva_partying_andi \| \| 292 \| hiram - dk - vhs - kou - koutd \| 14 \| 292_hiram_dk_vhs_kou \| \| 293 \| eliot - flat - boxer - 180 - v12 \| 14 \| 293_eliot_flat_boxer_180 \| \| 294 \| neilson - triumf - seoul - deadly - kids \| 14 \| 294_neilson_triumf_seoul_deadly \| \| 295 \| cruel - keith - caltech - constitution - painful \| 14 \| 295_cruel_keith_caltech_constitution \| \| 296 \| luminosity - red - rgb - hue - green \| 14 \| 296_luminosity_red_rgb_hue \| \| 297 \| she - were - they - her - sumgait \| 14 \| 297_she_were_they_her \| \| 298 \| jagr - francis - minus - uvic - player \| 14 \| 298_jagr_francis_minus_uvic \| \| 299 \| adl - bullock - gerard - francisco - arens \| 14 \| 299_adl_bullock_gerard_francisco \| \| 300 \| widgets - gadgets - dealy - motif - widget \| 14 \| 300_widgets_gadgets_dealy_motif \| \| 301 \| print - printer - file - claebaur - portal \| 14 \| 301_print_printer_file_claebaur \| \| 302 \| hacker - ethic - hackers - dorsai - carlos \| 14 \| 302_hacker_ethic_hackers_dorsai \| \| 303 \| weick - dana - him - cpu - sturges \| 14 \| 303_weick_dana_him_cpu \| \| 304 \| xputimage - server - sunview - cam - animation \| 14 \| 304_xputimage_server_sunview_cam \| \| 305 \| god - evil - serbian - saved - genocide \| 14 \| 305_god_evil_serbian_saved \| \| 306 \| nubus - pds - lc - marvin - higgins \| 13 \| 306_nubus_pds_lc_marvin \| \| 307 \| zeos - gateway - murthy - service - vasudev \| 13 \| 307_zeos_gateway_murthy_service \| \| 308 \| temperature - henry - interstellar - sky - radiation \| 13 \| 308_temperature_henry_interstellar_sky \| \| 309 \| uniforms - marlins - lloyd - reiniger - reds \| 13 \| 309_uniforms_marlins_lloyd_reiniger \| \| 310 \| faith - saved - romans - lukewarm - deeds \| 13 \| 310_faith_saved_romans_lukewarm \| \| 311 \| scsi - drive - ide - oracle - adaptec \| 13 \| 311_scsi_drive_ide_oracle \| \| 312 \| fifth - keyphrase - amendment - key - passwords \| 13 \| 312_fifth_keyphrase_amendment_key \| \| 313 \| tongues - language - tounges - gifted - bjorn \| 13 \| 313_tongues_language_tounges_gifted \| \| 314 \| rocks - overpass - ejv2j - erik - kids \| 13 \| 314_rocks_overpass_ejv2j_erik \| \| 315 \| biggest - disappointment - smale - mvp - surprise \| 13 \| 315_biggest_disappointment_smale_mvp \| \| 316 \| nicknames - nickname - healy - tammy - berg \| 13 \| 316_nicknames_nickname_healy_tammy \| \| 317 \| ampere - amp - db - bell - ohmite \| 13 \| 317_ampere_amp_db_bell \| \| 318 \| handling - ntuvax - ntu - handson - ba7116326 \| 13 \| 318_handling_ntuvax_ntu_handson \| \| 319 \| air - r12 - conditioning - substitutes - freon \| 13 \| 319_air_r12_conditioning_substitutes \| \| 320 \| soenke - bielefeld - widget - savela - masc0442 \| 13 \| 320_soenke_bielefeld_widget_savela \| \| 321 \| eisa - isa - bus - board - video \| 13 \| 321_eisa_isa_bus_board \| \| 322 \| wrench - srb - thiokol - pliers - tool \| 13 \| 322_wrench_srb_thiokol_pliers \| \| 323 \| oilers - pocklington - edmonton - northlands - yadallee \| 13 \| 323_oilers_pocklington_edmonton_northlands \| \| 324 \| sound - stereo - channel - mac - soundbase \| 13 \| 324_sound_stereo_channel_mac \| \| 325 \| movies - bikes - csundh30 - cassidy - ursa \| 13 \| 325_movies_bikes_csundh30_cassidy \| \| 326 \| haldol - elderly - lithium - drugs - hospital \| 13 \| 326_haldol_elderly_lithium_drugs \| \| 327 \| 8051 - oscar - mont - speth - spock \| 13 \| 327_8051_oscar_mont_speth \| \| 328 \| cache - iisi - powercache - card - fpu \| 13 \| 328_cache_iisi_powercache_card \| \| 329 \| bryce - bike - manish - arches - touring \| 13 \| 329_bryce_bike_manish_arches \| \| 330 \| skate - carol - malarchuk - sei - neck \| 13 \| 330_skate_carol_malarchuk_sei \| \| 331 \| rush - compuserve - jongsma - anovak - henson \| 12 \| 331_rush_compuserve_jongsma_anovak \| \| 332 \| date - clock - dos - bios - cmos \| 12 \| 332_date_clock_dos_bios \| \| 333 \| mcadams - sale - suresh - mattress - aj008 \| 12 \| 333_mcadams_sale_suresh_mattress \| \| 334 \| silence - moment - prayer - eeb1 - opposing \| 12 \| 334_silence_moment_prayer_eeb1 \| \| 335 \| jesus - prayers - god - name - prayer \| 12 \| 335_jesus_prayers_god_name \| \| 336 \| habitable - planets - atmosphere - oxygen - planet \| 12 \| 336_habitable_planets_atmosphere_oxygen \| \| 337 \| sunset - sunrise - drexel - cbis - wetstein \| 12 \| 337_sunset_sunrise_drexel_cbis \| \| 338 \| selective - borden - service - abolish - naval \| 12 \| 338_selective_borden_service_abolish \| \| 339 \| illustrator - diablo - autotrace - points - drawing \| 12 \| 339_illustrator_diablo_autotrace_points \| \| 340 \| love - kodak - god - dps - ico \| 12 \| 340_love_kodak_god_dps \| \| 341 \| koresh - griffen - batf - children - fbi \| 12 \| 341_koresh_griffen_batf_children \| \| 342 \| needles - acupuncture - needle - aids - hypodermic \| 12 \| 342_needles_acupuncture_needle_aids \| \| 343 \| accelerations - acceleration - 45g - deaddio - amruth \| 12 \| 343_accelerations_acceleration_45g_deaddio \| \| 344 \| tape - copy - vcr - video - destructing \| 12 \| 344_tape_copy_vcr_video \| \| 345 \| pmy - sword - royalroads - yadlowsky - malcolm \| 12 \| 345_pmy_sword_royalroads_yadlowsky \| \| 346 \| educational - price - newsbytes - cda - eu \| 12 \| 346_educational_price_newsbytes_cda \| \| 347 \| liar - lunatic - he - christian - bible \| 11 \| 347_liar_lunatic_he_christian \| \| 348 \| eff - minerva - yale - jgfoot - tarl \| 11 \| 348_eff_minerva_yale_jgfoot \| \| 349 \| seema - hannover - madvlsi - varma - columbia \| 11 \| 349_seema_hannover_madvlsi_varma \| \| 350 \| eugenics - memes - genes - genome - ruegg \| 11 \| 350_eugenics_memes_genes_genome \| \| 351 \| lunar - ltm1 - manned - tele - exploration \| 11 \| 351_lunar_ltm1_manned_tele \| \| 352 \| switch - beams - st11 - bimmer - cookson \| 11 \| 352_switch_beams_st11_bimmer \| \| 353 \| commandment - christians - temper - inference - jesus \| 11 \| 353_commandment_christians_temper_inference \| \| 354 \| harkey - dl - oscs - cubs - wetteland \| 11 \| 354_harkey_dl_oscs_cubs \| \| 355 \| fourd - 0565 - 494 - dimension - cute \| 11 \| 355_fourd_0565_494_dimension \| \| 356 \| mattingly - tesla - njit - drm6640 - baseman \| 11 \| 356_mattingly_tesla_njit_drm6640 \| \| 357 \| placebo - roth - rr - medicine - jb \| 11 \| 357_placebo_roth_rr_medicine \| \| 358 \| tempest - c650 - cyclone - price - drop \| 10 \| 358_tempest_c650_cyclone_price \| \| 359 \| ssf - overhead - nasa - tax - billion \| 10 \| 359_ssf_overhead_nasa_tax \| \| 360 \| mining - freaks - alaska - eco - miners \| 10 \| 360_mining_freaks_alaska_eco \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * 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
ganbold13/roberta-base-ner-demo	ganbold13	2024-05-08T08:47:56Z	109	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "token-classification", "generated_from_trainer", "mn", "base_model:bayartsogt/mongolian-roberta-base", "base_model:finetune:bayartsogt/mongolian-roberta-base", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T08:47:36Z	--- language: - mn base_model: bayartsogt/mongolian-roberta-base tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: roberta-base-ner-demo results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # roberta-base-ner-demo This model is a fine-tuned version of [bayartsogt/mongolian-roberta-base](https://huggingface.co/bayartsogt/mongolian-roberta-base) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1372 - Precision: 0.9235 - Recall: 0.9342 - F1: 0.9288 - Accuracy: 0.9800 ## 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: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:------:\|:--------:\| \| 0.1652 \| 1.0 \| 477 \| 0.0832 \| 0.8915 \| 0.9136 \| 0.9024 \| 0.9762 \| \| 0.0512 \| 2.0 \| 954 \| 0.0828 \| 0.9071 \| 0.9244 \| 0.9156 \| 0.9778 \| \| 0.0268 \| 3.0 \| 1431 \| 0.0909 \| 0.9179 \| 0.9274 \| 0.9226 \| 0.9787 \| \| 0.0146 \| 4.0 \| 1908 \| 0.0975 \| 0.9217 \| 0.9322 \| 0.9269 \| 0.9798 \| \| 0.008 \| 5.0 \| 2385 \| 0.1127 \| 0.9178 \| 0.9313 \| 0.9245 \| 0.9793 \| \| 0.0053 \| 6.0 \| 2862 \| 0.1255 \| 0.9207 \| 0.9295 \| 0.9251 \| 0.9790 \| \| 0.0034 \| 7.0 \| 3339 \| 0.1292 \| 0.9235 \| 0.9335 \| 0.9285 \| 0.9797 \| \| 0.0024 \| 8.0 \| 3816 \| 0.1339 \| 0.9186 \| 0.9332 \| 0.9258 \| 0.9795 \| \| 0.0015 \| 9.0 \| 4293 \| 0.1359 \| 0.9239 \| 0.9343 \| 0.9291 \| 0.9800 \| \| 0.0011 \| 10.0 \| 4770 \| 0.1372 \| 0.9235 \| 0.9342 \| 0.9288 \| 0.9800 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
AbhiKadoor/distilbert-base-uncased-finetuned-squad	AbhiKadoor	2024-05-08T08:47:27Z	3	0	null	[ "distilbert", "generated_from_trainer", "base_model:distilbert/distilbert-base-uncased", "base_model:finetune:distilbert/distilbert-base-uncased", "license:apache-2.0", "region:us" ]	null	2024-02-29T10:42:12Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer model-index: - name: distilbert-base-uncased-finetuned-squad results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-squad This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.8093 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 16 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 56 \| 4.2940 \| \| No log \| 2.0 \| 112 \| 3.8714 \| \| No log \| 3.0 \| 168 \| 3.8093 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cpu - Datasets 2.19.1 - Tokenizers 0.19.1
annamalai-s/bertopic_newsgroup_mpnet	annamalai-s	2024-05-08T08:46:04Z	8	0	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-05-08T08:46:02Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # bertopic_newsgroup_mpnet 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("annamalai-s/bertopic_newsgroup_mpnet") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 445 * Number of training documents: 18846 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| -1 \| to - the - for - from - is \| 10 \| -1_to_the_for_from \| \| 0 \| gun - guns - firearms - crime - handgun \| 5381 \| 0_gun_guns_firearms_crime \| \| 1 \| cramer - optilink - gay - clayton - sexual \| 266 \| 1_cramer_optilink_gay_clayton \| \| 2 \| fbi - batf - waco - compound - koresh \| 229 \| 2_fbi_batf_waco_compound \| \| 3 \| db - mov - bh - si - bl \| 134 \| 3_db_mov_bh_si \| \| 4 \| atf - fire - survivors - ranch - dividian \| 132 \| 4_atf_fire_survivors_ranch \| \| 5 \| drive - slave - drives - master - tape \| 127 \| 5_drive_slave_drives_master \| \| 6 \| moon - lunar - billion - alaska - prize \| 127 \| 6_moon_lunar_billion_alaska \| \| 7 \| armenian - turkish - armenians - serdar - argic \| 117 \| 7_armenian_turkish_armenians_serdar \| \| 8 \| espn - game - abc - coverage - hockey \| 112 \| 8_espn_game_abc_coverage \| \| 9 \| 3d - phigs - graphics - navy - lipman \| 112 \| 9_3d_phigs_graphics_navy \| \| 10 \| israeli - israel - israelis - arab - soldiers \| 104 \| 10_israeli_israel_israelis_arab \| \| 11 \| dos - xfree86 - windows - server - tcp \| 100 \| 11_dos_xfree86_windows_server \| \| 12 \| sale - drive - meg - ram - floppy \| 98 \| 12_sale_drive_meg_ram \| \| 13 \| homosexuality - homosexual - paul - christians - sin \| 97 \| 13_homosexuality_homosexual_paul_christians \| \| 14 \| clutch - alomar - runs - baerga - average \| 93 \| 14_clutch_alomar_runs_baerga \| \| 15 \| os - microsoft - challenge - supporters - windows \| 91 \| 15_os_microsoft_challenge_supporters \| \| 16 \| migraine - sleep - dyer - thyroid - geb \| 91 \| 16_migraine_sleep_dyer_thyroid \| \| 17 \| drive - ide - scsi - drives - controller \| 87 \| 17_drive_ide_scsi_drives \| \| 18 \| modem - modems - fax - courier - sportster \| 85 \| 18_modem_modems_fax_courier \| \| 19 \| msg - food - sensitivity - chinese - superstition \| 84 \| 19_msg_food_sensitivity_chinese \| \| 20 \| objective - morality - larson - frank - values \| 84 \| 20_objective_morality_larson_frank \| \| 21 \| windows - swap - memory - emm386 - file \| 84 \| 21_windows_swap_memory_emm386 \| \| 22 \| sale - speakers - stereo - offer - amp \| 83 \| 22_sale_speakers_stereo_offer \| \| 23 \| jpeg - gif - image - format - file \| 82 \| 23_jpeg_gif_image_format \| \| 24 \| jewish - zionism - israel - jews - jew \| 82 \| 24_jewish_zionism_israel_jews \| \| 25 \| space - nasa - venus - planet - earth \| 80 \| 25_space_nasa_venus_planet \| \| 26 \| encryption - clipper - chip - government - wiretap \| 78 \| 26_encryption_clipper_chip_government \| \| 27 \| polygon - polygons - ___ - routine - algorithm \| 76 \| 27_polygon_polygons_____routine \| \| 28 \| car - miles - toyota - sale - mazda \| 73 \| 28_car_miles_toyota_sale \| \| 29 \| scsi - ide - dma - bus - isa \| 71 \| 29_scsi_ide_dma_bus \| \| 30 \| 25 - pit - pts - det - la \| 69 \| 30_25_pit_pts_det \| \| 31 \| stephanopoulos - president - mr - myers - ms \| 68 \| 31_stephanopoulos_president_mr_myers \| \| 32 \| rushdie - islam - jaeger - islamic - gregg \| 66 \| 32_rushdie_islam_jaeger_islamic \| \| 33 \| dumbest - automotive - lights - concepts - continental \| 66 \| 33_dumbest_automotive_lights_concepts \| \| 34 \| motif - openwindows - xview - olit - x11 \| 66 \| 34_motif_openwindows_xview_olit \| \| 35 \| games - sega - genesis - snes - sale \| 65 \| 35_games_sega_genesis_snes \| \| 36 \| bosnia - muslims - bosnian - serbs - bosnians \| 65 \| 36_bosnia_muslims_bosnian_serbs \| \| 37 \| mary - her - she - immaculate - conception \| 64 \| 37_mary_her_she_immaculate \| \| 38 \| israel - lebanese - lebanon - israeli - hezbollah \| 64 \| 38_israel_lebanese_lebanon_israeli \| \| 39 \| existence - evolution - theory - science - exist \| 62 \| 39_existence_evolution_theory_science \| \| 40 \| hell - eternal - heaven - god - jesus \| 62 \| 40_hell_eternal_heaven_god \| \| 41 \| simms - simm - meg - pin - ram \| 61 \| 41_simms_simm_meg_pin \| \| 42 \| br - isc - government - steveh - thor \| 61 \| 42_br_isc_government_steveh \| \| 43 \| dos - stacker - windows - dos6 - disk \| 60 \| 43_dos_stacker_windows_dos6 \| \| 44 \| clutch - shifting - shift - manual - transmission \| 60 \| 44_clutch_shifting_shift_manual \| \| 45 \| radar - detector - detectors - valentine - ka \| 59 \| 45_radar_detector_detectors_valentine \| \| 46 \| tax - taxes - income - deficit - vat \| 59 \| 46_tax_taxes_income_deficit \| \| 47 \| keyboard - key - keys - keycode - accelerators \| 58 \| 47_keyboard_key_keys_keycode \| \| 48 \| copy - protected - protection - disks - sehari \| 58 \| 48_copy_protected_protection_disks \| \| 49 \| station - redesign - space - nasa - option \| 57 \| 49_station_redesign_space_nasa \| \| 50 \| lib - libxmu - ndet_loop - xmu - usr \| 56 \| 50_lib_libxmu_ndet_loop_xmu \| \| 51 \| dog - dogs - parr - ucalgary - attack \| 55 \| 51_dog_dogs_parr_ucalgary \| \| 52 \| leafs - wings - game - detroit - goal \| 52 \| 52_leafs_wings_game_detroit \| \| 53 \| cancer - water - medical - mwra - circumcision \| 51 \| 53_cancer_water_medical_mwra \| \| 54 \| sleeve - cd - cds - sale - picture \| 51 \| 54_sleeve_cd_cds_sale \| \| 55 \| sharks - season - keenan - rangers - chuq \| 50 \| 55_sharks_season_keenan_rangers \| \| 56 \| battery - batteries - concrete - acid - lead \| 49 \| 56_battery_batteries_concrete_acid \| \| 57 \| drugs - drug - marijuana - legalization - cigarettes \| 49 \| 57_drugs_drug_marijuana_legalization \| \| 58 \| exhaust - carbs - bike - carb - honda \| 49 \| 58_exhaust_carbs_bike_carb \| \| 59 \| shaft - wheelies - stafford - wheelie - winona \| 48 \| 59_shaft_wheelies_stafford_wheelie \| \| 60 \| key - chip - clipper - algorithm - chips \| 48 \| 60_key_chip_clipper_algorithm \| \| 61 \| photography - krillean - kirlian - pictures - sol1 \| 48 \| 61_photography_krillean_kirlian_pictures \| \| 62 \| bike - bikes - motorcycle - motorcycles - buying \| 48 \| 62_bike_bikes_motorcycle_motorcycles \| \| 63 \| lynn - riders - rtsg - motorcycling - bike \| 47 \| 63_lynn_riders_rtsg_motorcycling \| \| 64 \| church - churches - christianity - christian - movement \| 47 \| 64_church_churches_christianity_christian \| \| 65 \| hst - mission - servicing - shuttle - boost \| 47 \| 65_hst_mission_servicing_shuttle \| \| 66 \| nissan - wagon - villager - altima - vw \| 47 \| 66_nissan_wagon_villager_altima \| \| 67 \| helmet - helmets - shoei - jacket - eskimo \| 47 \| 67_helmet_helmets_shoei_jacket \| \| 68 \| finland - sweden - wc - czech - ericsson \| 47 \| 68_finland_sweden_wc_czech \| \| 69 \| gamma - oort - bursters - ray - cloud \| 46 \| 69_gamma_oort_bursters_ray \| \| 70 \| jewish - baseball - vb30 - lafibm - players \| 46 \| 70_jewish_baseball_vb30_lafibm \| \| 71 \| sky - vandalizing - night - pollution - enzo \| 46 \| 71_sky_vandalizing_night_pollution \| \| 72 \| nanao - monitors - viewsonic - monitor - inches \| 46 \| 72_nanao_monitors_viewsonic_monitor \| \| 73 \| militia - amendment - arms - regulated - bear \| 45 \| 73_militia_amendment_arms_regulated \| \| 74 \| rocks - teenagers - warning - overpass - kids \| 45 \| 74_rocks_teenagers_warning_overpass \| \| 75 \| des - key - keyseach - bits - shelf \| 45 \| 75_des_key_keyseach_bits \| \| 76 \| dl - wetteland - harkey - franco - plymouth \| 45 \| 76_dl_wetteland_harkey_franco \| \| 77 \| petch - gvg47 - love - god - gvg \| 44 \| 77_petch_gvg47_love_god \| \| 78 \| pin - card - connector - ethernet - board \| 44 \| 78_pin_card_connector_ethernet \| \| 79 \| leds - uv - led - blue - subliminal \| 43 \| 79_leds_uv_led_blue \| \| 80 \| theism - fanatism - atheism - belief - theists \| 42 \| 80_theism_fanatism_atheism_belief \| \| 81 \| freedom - forged - locutus - colorado - speech \| 42 \| 81_freedom_forged_locutus_colorado \| \| 82 \| moral - morality - keith - livesey - caltech \| 41 \| 82_moral_morality_keith_livesey \| \| 83 \| phd - environmentalism - environmental - heath - pantheism \| 41 \| 83_phd_environmentalism_environmental_heath \| \| 84 \| buffalo - sabres - blues - bruins - boston \| 41 \| 84_buffalo_sabres_blues_bruins \| \| 85 \| countersteering - mjs - bike - countersteering_faq - lean \| 41 \| 85_countersteering_mjs_bike_countersteering_faq \| \| 86 \| nmm - behind - traffic - lane - bike \| 41 \| 86_nmm_behind_traffic_lane \| \| 87 \| games - game - baseball - pitches - pitcher \| 41 \| 87_games_game_baseball_pitches \| \| 88 \| cpu - fan - heat - sink - fans \| 41 \| 88_cpu_fan_heat_sink \| \| 89 \| jehovah - elohim - father - lord - son \| 41 \| 89_jehovah_elohim_father_lord \| \| 90 \| cruel - punishment - keith - penalty - death \| 40 \| 90_cruel_punishment_keith_penalty \| \| 91 \| insurance - health - private - care - gld \| 40 \| 91_insurance_health_private_care \| \| 92 \| powerbook - duo - portable - pb - pb100 \| 40 \| 92_powerbook_duo_portable_pb \| \| 93 \| bike - sale - miles - mower - fork \| 39 \| 93_bike_sale_miles_mower \| \| 94 \| postscript - ghostscript - ghostview - pageview - files \| 39 \| 94_postscript_ghostscript_ghostview_pageview \| \| 95 \| candida - yeast - noring - systemic - infections \| 39 \| 95_candida_yeast_noring_systemic \| \| 96 \| card - p9000 - orchid - weitek - vlb \| 39 \| 96_card_p9000_orchid_weitek \| \| 97 \| jews - israel - arabs - land - arab \| 38 \| 97_jews_israel_arabs_land \| \| 98 \| radiosity - pov - raytracing - ray - amann \| 38 \| 98_radiosity_pov_raytracing_ray \| \| 99 \| oil - drain - changing - ohio - magnus \| 38 \| 99_oil_drain_changing_ohio \| \| 100 \| scope - scopes - oscilloscope - fluke - meter \| 38 \| 100_scope_scopes_oscilloscope_fluke \| \| 101 \| faith - god - exist - proof - burden \| 37 \| 101_faith_god_exist_proof \| \| 102 \| sox - rbi - games - game - win \| 37 \| 102_sox_rbi_games_game \| \| 103 \| greek - greece - greeks - turkish - turks \| 37 \| 103_greek_greece_greeks_turkish \| \| 104 \| science - methodology - sas - fulk - lady \| 37 \| 104_science_methodology_sas_fulk \| \| 105 \| hockey - nhl - team - league - stars \| 37 \| 105_hockey_nhl_team_league \| \| 106 \| koresh - fbi - compound - fire - cult \| 37 \| 106_koresh_fbi_compound_fire \| \| 107 \| lens - camera - rupin - dang - goldberg \| 37 \| 107_lens_camera_rupin_dang \| \| 108 \| xv - escaped - g3states - endif - define \| 37 \| 108_xv_escaped_g3states_endif \| \| 109 \| mormons - jews - lds - sword - brigham \| 36 \| 109_mormons_jews_lds_sword \| \| 110 \| resurrection - jesus - tomb - rise - luke \| 36 \| 110_resurrection_jesus_tomb_rise \| \| 111 \| monitors - hours - nevai - day - monitor \| 36 \| 111_monitors_hours_nevai_day \| \| 112 \| window - dialog - widget - xlib - application \| 36 \| 112_window_dialog_widget_xlib \| \| 113 \| arrogance - truth - christians - arrogant - darren \| 36 \| 113_arrogance_truth_christians_arrogant \| \| 114 \| gas - tear - unb - cs - jupiter \| 36 \| 114_gas_tear_unb_cs \| \| 115 \| winfield - mattingly - peak - henderson - robinson \| 35 \| 115_winfield_mattingly_peak_henderson \| \| 116 \| escrow - key - agencies - aclu - branch \| 35 \| 116_escrow_key_agencies_aclu \| \| 117 \| judas - tyre - prophecy - prophecies - decenso \| 35 \| 117_judas_tyre_prophecy_prophecies \| \| 118 \| image - processing - plplot - tools - analysis \| 35 \| 118_image_processing_plplot_tools \| \| 119 \| eisa - isa - bus - vlb - motherboard \| 35 \| 119_eisa_isa_bus_vlb \| \| 120 \| clipper - phone - phones - key - escrow \| 35 \| 120_clipper_phone_phones_key \| \| 121 \| morris - team - jays - clemens - viola \| 35 \| 121_morris_team_jays_clemens \| \| 122 \| space - moscow - shuttle - spaceflight - term \| 34 \| 122_space_moscow_shuttle_spaceflight \| \| 123 \| hotel - voucher - ticket - hiram - airline \| 34 \| 123_hotel_voucher_ticket_hiram \| \| 124 \| paint - wax - scratches - plastic - lisa \| 34 \| 124_paint_wax_scratches_plastic \| \| 125 \| zeos - gateway - 486 - monitor - murthy \| 34 \| 125_zeos_gateway_486_monitor \| \| 126 \| space - advertising - marketing - sky - billboard \| 34 \| 126_space_advertising_marketing_sky \| \| 127 \| gopher - search - ftp - sites - exhibit \| 34 \| 127_gopher_search_ftp_sites \| \| 128 \| 0d - _o - cx - c_ - 145 \| 34 \| 128_0d__o_cx_c_ \| \| 129 \| gtoal - celp - speech - compression - toal \| 33 \| 129_gtoal_celp_speech_compression \| \| 130 \| air - freon - aftermarket - behanna - r12 \| 33 \| 130_air_freon_aftermarket_behanna \| \| 131 \| 3do - quicktime - ricardo - playback - mcmains \| 33 \| 131_3do_quicktime_ricardo_playback \| \| 132 \| v4 - v6 - v8 - v12 - cdac \| 33 \| 132_v4_v6_v8_v12 \| \| 133 \| font - fonts - character - truetype - windows \| 33 \| 133_font_fonts_character_truetype \| \| 134 \| insurance - car - fault - rates - deductible \| 32 \| 134_insurance_car_fault_rates \| \| 135 \| drivers - driver - card - jmarttila - actix \| 32 \| 135_drivers_driver_card_jmarttila \| \| 136 \| tempest - holland - northeastern - utsa - cam \| 32 \| 136_tempest_holland_northeastern_utsa \| \| 137 \| mustang - ford - camaro - howell - car \| 32 \| 137_mustang_ford_camaro_howell \| \| 138 \| com4 - modem - com3 - port - 16550 \| 31 \| 138_com4_modem_com3_port \| \| 139 \| deskjet - bubblejet - ink - printers - printer \| 31 \| 139_deskjet_bubblejet_ink_printers \| \| 140 \| expose - window - event - buzz - main_win \| 31 \| 140_expose_window_event_buzz \| \| 141 \| europeans - nhl - rauser - players - european \| 31 \| 141_europeans_nhl_rauser_players \| \| 142 \| anonymous - privacy - anonymity - eff - internet \| 31 \| 142_anonymous_privacy_anonymity_eff \| \| 143 \| vs - winner - bos - cal - chi \| 31 \| 143_vs_winner_bos_cal \| \| 144 \| random - key - passwords - fifth - security \| 31 \| 144_random_key_passwords_fifth \| \| 145 \| doctor - clinic - med - hoss - medicine \| 31 \| 145_doctor_clinic_med_hoss \| \| 146 \| dc - shuttle - sdio - ssto - flight \| 31 \| 146_dc_shuttle_sdio_ssto \| \| 147 \| split - newsgroup - cdrom - comp - graphics \| 30 \| 147_split_newsgroup_cdrom_comp \| \| 148 \| nsa - cryptosystems - nea - paranoia - encryption \| 30 \| 148_nsa_cryptosystems_nea_paranoia \| \| 149 \| colormap - visual - color - colormaps - dpy \| 30 \| 149_colormap_visual_color_colormaps \| \| 150 \| jesus - brian - life - sandvik - kendig \| 30 \| 150_jesus_brian_life_sandvik \| \| 151 \| atheism - asimov - timmons - alt - bake \| 30 \| 151_atheism_asimov_timmons_alt \| \| 152 \| monitor - vga - monitors - lc - svga \| 30 \| 152_monitor_vga_monitors_lc \| \| 153 \| eye - dominance - prk - handedness - rk \| 29 \| 153_eye_dominance_prk_handedness \| \| 154 \| clinton - administration - qualcomm - tapped - drug \| 29 \| 154_clinton_administration_qualcomm_tapped \| \| 155 \| fpu - c650 - coprocessor - 040 - 650 \| 29 \| 155_fpu_c650_coprocessor_040 \| \| 156 \| cherry - coach - hockey - don - gilmour \| 29 \| 156_cherry_coach_hockey_don \| \| 157 \| baptism - sin - aaron - baptized - infants \| 29 \| 157_baptism_sin_aaron_baptized \| \| 158 \| car - dealer - price - sps - blue \| 28 \| 158_car_dealer_price_sps \| \| 159 \| ir - dres - dnd - detector - detection \| 28 \| 159_ir_dres_dnd_detector \| \| 160 \| rosicrucian - order - ch981 - amorc - tony \| 28 \| 160_rosicrucian_order_ch981_amorc \| \| 161 \| health - tobacco - cesarean - cancer - smokeless \| 28 \| 161_health_tobacco_cesarean_cancer \| \| 162 \| nt - windows - chicogo - os - rajiev \| 28 \| 162_nt_windows_chicogo_os \| \| 163 \| king - kyle - adjective - nc - cramm \| 28 \| 163_king_kyle_adjective_nc \| \| 164 \| muslims - serbs - croats - muslim - bosnian \| 28 \| 164_muslims_serbs_croats_muslim \| \| 165 \| torre - hitter - gilkey - lankford - manager \| 27 \| 165_torre_hitter_gilkey_lankford \| \| 166 \| bit - 24 - deniaud - bits - images \| 27 \| 166_bit_24_deniaud_bits \| \| 167 \| dwi - infante - driving - drunk - speedy \| 27 \| 167_dwi_infante_driving_drunk \| \| 168 \| xdm - server - login - graphic_display - error \| 27 \| 168_xdm_server_login_graphic_display \| \| 169 \| 92 - hiv - aids - needles - 12 \| 27 \| 169_92_hiv_aids_needles \| \| 170 \| diamond - stealth - drivers - card - speedstar \| 27 \| 170_diamond_stealth_drivers_card \| \| 171 \| lopez - catchers - olson - braves - players \| 27 \| 171_lopez_catchers_olson_braves \| \| 172 \| books - 02106 - 00 - chemistry - udel \| 27 \| 172_books_02106_00_chemistry \| \| 173 \| duo - dock - apple - 230 - bredell \| 27 \| 173_duo_dock_apple_230 \| \| 174 \| cable - antenna - tv - td - antennas \| 27 \| 174_cable_antenna_tv_td \| \| 175 \| stadium - baseball - oswego - shea - mets \| 26 \| 175_stadium_baseball_oswego_shea \| \| 176 \| images - image - geosphere - earth - unocal \| 26 \| 176_images_image_geosphere_earth \| \| 177 \| sci - space - prado - henry - permanet \| 26 \| 177_sci_space_prado_henry \| \| 178 \| peace - israel - palestinian - palestinians - talks \| 26 \| 178_peace_israel_palestinian_palestinians \| \| 179 \| speed - x86 - 040 - 68040 - 680x0 \| 26 \| 179_speed_x86_040_68040 \| \| 180 \| adcom - amp - amps - sound - microphone \| 26 \| 180_adcom_amp_amps_sound \| \| 181 \| ati - ultra - drivers - gateway - 1280x1024 \| 26 \| 181_ati_ultra_drivers_gateway \| \| 182 \| clipper - screw - chip - encryption - initiative \| 26 \| 182_clipper_screw_chip_encryption \| \| 183 \| analog - seema - converter - hannover - 4066 \| 26 \| 183_analog_seema_converter_hannover \| \| 184 \| mask - goalie - gtd597a - votes - hrivnak \| 26 \| 184_mask_goalie_gtd597a_votes \| \| 185 \| 130 - rush - fast - lane - roads \| 25 \| 185_130_rush_fast_lane \| \| 186 \| ashok - biochemistry - winqvt - kuleuven - liris \| 25 \| 186_ashok_biochemistry_winqvt_kuleuven \| \| 187 \| room - summer - sublet - jhuvm - kitchen \| 25 \| 187_room_summer_sublet_jhuvm \| \| 188 \| war - gulf - hussein - bombing - iraqi \| 25 \| 188_war_gulf_hussein_bombing \| \| 189 \| ulf - erau - player - huot - shot \| 25 \| 189_ulf_erau_player_huot \| \| 190 \| window - manager - xsizehints - bading - position \| 25 \| 190_window_manager_xsizehints_bading \| \| 191 \| henrik - armenia - bm - planes - armenians \| 25 \| 191_henrik_armenia_bm_planes \| \| 192 \| crypt - key - cryptography - des - ciphers \| 25 \| 192_crypt_key_cryptography_des \| \| 193 \| amd - cyrix - 486dx2 - 486 - mhz \| 25 \| 193_amd_cyrix_486dx2_486 \| \| 194 \| midi - sound - blaster - speaker - driver \| 25 \| 194_midi_sound_blaster_speaker \| \| 195 \| mode - vga - tiang - svga - modes \| 25 \| 195_mode_vga_tiang_svga \| \| 196 \| accelerations - acceleration - breathing - 45g - deaddio \| 25 \| 196_accelerations_acceleration_breathing_45g \| \| 197 \| wire - wiring - ground - neutral - outlets \| 24 \| 197_wire_wiring_ground_neutral \| \| 198 \| pain - bone - almanac - rib - massager \| 24 \| 198_pain_bone_almanac_rib \| \| 199 \| reno - janet - madman - children - she \| 24 \| 199_reno_janet_madman_children \| \| 200 \| barbecued - carcinogenic - meat - foods - risk \| 24 \| 200_barbecued_carcinogenic_meat_foods \| \| 201 \| cmos - beeps - chimes - memory - error \| 24 \| 201_cmos_beeps_chimes_memory \| \| 202 \| crohn - diet - ibd - inflammation - eat \| 24 \| 202_crohn_diet_ibd_inflammation \| \| 203 \| wave - bikers - waved - cage - waving \| 24 \| 203_wave_bikers_waved_cage \| \| 204 \| batf - warrant - knock - hallam - police \| 24 \| 204_batf_warrant_knock_hallam \| \| 205 \| hacker - ethic - computer - hackers - programming \| 23 \| 205_hacker_ethic_computer_hackers \| \| 206 \| mouse - motion - jumpy - smoothly - jump \| 23 \| 206_mouse_motion_jumpy_smoothly \| \| 207 \| comet - jupiter - gehrels - sq - baalke \| 23 \| 207_comet_jupiter_gehrels_sq \| \| 208 \| machines - precision - comments - contact - version \| 23 \| 208_machines_precision_comments_contact \| \| 209 \| cosmo - angmar - alfalfa - pro - tsk \| 23 \| 209_cosmo_angmar_alfalfa_pro \| \| 210 \| scsi - quadra - nodine - mac - cartridge \| 23 \| 210_scsi_quadra_nodine_mac \| \| 211 \| adl - bullock - gerard - francisco - arens \| 23 \| 211_adl_bullock_gerard_francisco \| \| 212 \| pgp - rsa - cryptography - code - patents \| 23 \| 212_pgp_rsa_cryptography_code \| \| 213 \| koresh - sbc - backing - utarlg - enclosed \| 23 \| 213_koresh_sbc_backing_utarlg \| \| 214 \| solvent - adhesive - duct - ruck - tape \| 23 \| 214_solvent_adhesive_duct_ruck \| \| 215 \| command - spacecraft - galileo - baalke - timer \| 23 \| 215_command_spacecraft_galileo_baalke \| \| 216 \| skin - dry - vaseline - rutin - acne \| 23 \| 216_skin_dry_vaseline_rutin \| \| 217 \| gaza - gazans - ghetto - israeli - jews \| 23 \| 217_gaza_gazans_ghetto_israeli \| \| 218 \| 03 - 02 - 04 - 01 - 05 \| 22 \| 218_03_02_04_01 \| \| 219 \| ra - mormon - lds - bible - jesus \| 22 \| 219_ra_mormon_lds_bible \| \| 220 \| abortion - child - fetus - margoli - abortions \| 22 \| 220_abortion_child_fetus_margoli \| \| 221 \| 00 - wolverine - 1st - comics - hulk \| 22 \| 221_00_wolverine_1st_comics \| \| 222 \| mac - 32 - os - stuffit - 800 \| 22 \| 222_mac_32_os_stuffit \| \| 223 \| lyme - disease - fever - ld - infectious \| 22 \| 223_lyme_disease_fever_ld \| \| 224 \| cobb - moral - morality - alexia - lis \| 22 \| 224_cobb_moral_morality_alexia \| \| 225 \| sphere - den - p3 - p1 - p2 \| 22 \| 225_sphere_den_p3_p1 \| \| 226 \| xputimage - shared - server - memory - animation \| 22 \| 226_xputimage_shared_server_memory \| \| 227 \| rgb - luminosity - hue - red - green \| 21 \| 227_rgb_luminosity_hue_red \| \| 228 \| pillion - riding - advice - passenger - ride \| 21 \| 228_pillion_riding_advice_passenger \| \| 229 \| mouse - stuttgart - windows - driver - kasajian \| 21 \| 229_mouse_stuttgart_windows_driver \| \| 230 \| gant - hirschbeck - umpire - strike - cox \| 21 \| 230_gant_hirschbeck_umpire_strike \| \| 231 \| cursor - xterm - blinking - taylor - emu \| 21 \| 231_cursor_xterm_blinking_taylor \| \| 232 \| tickets - 05pm - 35pm - june - ticket \| 21 \| 232_tickets_05pm_35pm_june \| \| 233 \| ham - surges - alternator - interference - power \| 21 \| 233_ham_surges_alternator_interference \| \| 234 \| marriage - married - ceremony - eyes - marry \| 21 \| 234_marriage_married_ceremony_eyes \| \| 235 \| moa - bmw - rider - cactus - bmwmoa \| 21 \| 235_moa_bmw_rider_cactus \| \| 236 \| number - phone - umass - ecs - line \| 21 \| 236_number_phone_umass_ecs \| \| 237 \| bible - text - translations - texts - septuagint \| 21 \| 237_bible_text_translations_texts \| \| 238 \| cop - officers - lmsc - lockheed - police \| 21 \| 238_cop_officers_lmsc_lockheed \| \| 239 \| dxf - iff - format - autocad - pei \| 20 \| 239_dxf_iff_format_autocad \| \| 240 \| roger - maynard - names - letter - laurentian \| 20 \| 240_roger_maynard_names_letter \| \| 241 \| atheism - sapienza - atheists - fil - alt \| 20 \| 241_atheism_sapienza_atheists_fil \| \| 242 \| video - verity - hdtv - compariators - input \| 20 \| 242_video_verity_hdtv_compariators \| \| 243 \| yassin - deir - irgun - dir - village \| 20 \| 243_yassin_deir_irgun_dir \| \| 244 \| god - predestination - saved - evil - grace \| 20 \| 244_god_predestination_saved_evil \| \| 245 \| dialing - phones - tone - hugo - sweden \| 20 \| 245_dialing_phones_tone_hugo \| \| 246 \| irq - interrupt - soundblaster - port - lpt1 \| 20 \| 246_irq_interrupt_soundblaster_port \| \| 247 \| tongues - language - tounges - languages - koberg \| 20 \| 247_tongues_language_tounges_languages \| \| 248 \| jsn104 - psuvm - hell - psu - damnation \| 20 \| 248_jsn104_psuvm_hell_psu \| \| 249 \| chain - wax - behanna - maxima - cookson \| 20 \| 249_chain_wax_behanna_maxima \| \| 250 \| bus - dx2 - 50mhz - dx - dx50 \| 19 \| 250_bus_dx2_50mhz_dx \| \| 251 \| islamic - bcci - bank - jaeger - gregg \| 19 \| 251_islamic_bcci_bank_jaeger \| \| 252 \| performa - lciii - iici - lc - pnet16 \| 19 \| 252_performa_lciii_iici_lc \| \| 253 \| list - requests - bmw - request - mailing \| 19 \| 253_list_requests_bmw_request \| \| 254 \| logo - rle - vgalogo - startup - lgo \| 19 \| 254_logo_rle_vgalogo_startup \| \| 255 \| kidney - stones - calcium - she - stone \| 19 \| 255_kidney_stones_calcium_she \| \| 256 \| phillies - phils - braves - wins - division \| 19 \| 256_phillies_phils_braves_wins \| \| 257 \| monitor - lcd - screen - display - jiggles \| 19 \| 257_monitor_lcd_screen_display \| \| 258 \| women - bobby - men - islamic - mozumder \| 19 \| 258_women_bobby_men_islamic \| \| 259 \| ax - max - g9v - b8f - a86 \| 19 \| 259_ax_max_g9v_b8f \| \| 260 \| koresh - mathew - bittrolff - david - risen \| 19 \| 260_koresh_mathew_bittrolff_david \| \| 261 \| biggest - disappointment - smale - mvp - surprise \| 19 \| 261_biggest_disappointment_smale_mvp \| \| 262 \| batf - oldham - blast - fokes - compound \| 19 \| 262_batf_oldham_blast_fokes \| \| 263 \| sabbath - law - worship - paul - ceremonial \| 19 \| 263_sabbath_law_worship_paul \| \| 264 \| joystick - joysticks - arcade - port - int15h \| 19 \| 264_joystick_joysticks_arcade_port \| \| 265 \| captain - traded - captains - striped - resigned \| 18 \| 265_captain_traded_captains_striped \| \| 266 \| mjm - fm - circuits - mixer - fsk \| 18 \| 266_mjm_fm_circuits_mixer \| \| 267 \| cooling - towers - nuclear - plants - water \| 18 \| 267_cooling_towers_nuclear_plants \| \| 268 \| she - were - her - apartment - they \| 18 \| 268_she_were_her_apartment \| \| 269 \| pens - caps - eos - penguins - cdkaupan \| 18 \| 269_pens_caps_eos_penguins \| \| 270 \| toyota - cruiser - suv - 4runner - cisco \| 18 \| 270_toyota_cruiser_suv_4runner \| \| 271 \| love - god - dps - kodak - logic \| 18 \| 271_love_god_dps_kodak \| \| 272 \| w4wg - network - workgroups - windows - lastdrive \| 18 \| 272_w4wg_network_workgroups_windows \| \| 273 \| ticket - cop - speeding - chp - plates \| 18 \| 273_ticket_cop_speeding_chp \| \| 274 \| lobby - sammons - ns111310 - colostate - letter \| 18 \| 274_lobby_sammons_ns111310_colostate \| \| 275 \| ndw - spss - norton - ini - desktop \| 18 \| 275_ndw_spss_norton_ini \| \| 276 \| uio - ifi - thomasp - parsli - quisling \| 18 \| 276_uio_ifi_thomasp_parsli \| \| 277 \| motherboard - 386 - halcyon - 386dx - ruggiero \| 18 \| 277_motherboard_386_halcyon_386dx \| \| 278 \| monitor - video - 610 - colors - screen \| 18 \| 278_monitor_video_610_colors \| \| 279 \| oil - wd - 20w50 - 10w40 - militech \| 18 \| 279_oil_wd_20w50_10w40 \| \| 280 \| printer - postscript - laser - laserjet - print \| 18 \| 280_printer_postscript_laser_laserjet \| \| 281 \| probe - ford - car - newman - gt \| 17 \| 281_probe_ford_car_newman \| \| 282 \| geico - insurance - davew - wonnacott - claim \| 17 \| 282_geico_insurance_davew_wonnacott \| \| 283 \| 42 - tiff - philosophical - significance - joachim \| 17 \| 283_42_tiff_philosophical_significance \| \| 284 \| omen - weight - fat - wa7kgx - forsberg \| 17 \| 284_omen_weight_fat_wa7kgx \| \| 285 \| workspace - manager - managers - zip - workspaces \| 17 \| 285_workspace_manager_managers_zip \| \| 286 \| fourd - vinge - vernor - 0565 - _the \| 17 \| 286_fourd_vinge_vernor_0565 \| \| 287 \| mithras - pegasus - cunyvm - uoregon - magick \| 17 \| 287_mithras_pegasus_cunyvm_uoregon \| \| 288 \| printer - adisak - pochanayon - pin - dot \| 17 \| 288_printer_adisak_pochanayon_pin \| \| 289 \| gainey - bob - player - gilmour - maynard \| 16 \| 289_gainey_bob_player_gilmour \| \| 290 \| adobe - photoshop - photo - platforms - shop \| 16 \| 290_adobe_photoshop_photo_platforms \| \| 291 \| tank - tankbag - zipper - fj1100 - bgardner \| 16 \| 291_tank_tankbag_zipper_fj1100 \| \| 292 \| disks - mac - 800k - binkley - 44mb \| 16 \| 292_disks_mac_800k_binkley \| \| 293 \| graphics - pub - 128 - ray - rayshade \| 16 \| 293_graphics_pub_128_ray \| \| 294 \| nubus - pds - lc - slot - marvin \| 16 \| 294_nubus_pds_lc_slot \| \| 295 \| odometer - mileage - odometers - dealer - speedo \| 16 \| 295_odometer_mileage_odometers_dealer \| \| 296 \| s1 - s2 - serial - key - unit \| 16 \| 296_s1_s2_serial_key \| \| 297 \| lehigh - car - sports - ns1 - cars \| 16 \| 297_lehigh_car_sports_ns1 \| \| 298 \| kjell - driver - hut - printer - backgrounder \| 16 \| 298_kjell_driver_hut_printer \| \| 299 \| weapons - militia - weapon - foxvog - destruction \| 16 \| 299_weapons_militia_weapon_foxvog \| \| 300 \| corn - seizures - paulson - seizure - cereals \| 16 \| 300_corn_seizures_paulson_seizure \| \| 301 \| jagr - francis - minus - player - uvic \| 16 \| 301_jagr_francis_minus_player \| \| 302 \| ingres - garrett - nixon - cambodia - tantrums \| 16 \| 302_ingres_garrett_nixon_cambodia \| \| 303 \| 8051 - oscar - mont - 68hc16 - speth \| 16 \| 303_8051_oscar_mont_68hc16 \| \| 304 \| tie - breaker - devils - islanders - record \| 16 \| 304_tie_breaker_devils_islanders \| \| 305 \| motto - keith - caltech - pompous - schneider \| 16 \| 305_motto_keith_caltech_pompous \| \| 306 \| ear - ears - ringing - earwax - vida \| 16 \| 306_ear_ears_ringing_earwax \| \| 307 \| saturn - dealer - profit - sl2 - sc2 \| 16 \| 307_saturn_dealer_profit_sl2 \| \| 308 \| tires - tire - fluids - abs - dot \| 16 \| 308_tires_tire_fluids_abs \| \| 309 \| software - level - wingert - shuttle - process \| 16 \| 309_software_level_wingert_shuttle \| \| 310 \| network - localtalk - ethernet - macs - appletalk \| 16 \| 310_network_localtalk_ethernet_macs \| \| 311 \| mailing - list - bait - detweiler - rdetweil \| 16 \| 311_mailing_list_bait_detweiler \| \| 312 \| satan - heaven - kicked - tyre - thou \| 16 \| 312_satan_heaven_kicked_tyre \| \| 313 \| wip - sports - wfan - eagles - lupica \| 15 \| 313_wip_sports_wfan_eagles \| \| 314 \| silence - moment - prayer - eeb1 - opposing \| 15 \| 314_silence_moment_prayer_eeb1 \| \| 315 \| octopus - detroit - ice - hammerl - octopi \| 15 \| 315_octopus_detroit_ice_hammerl \| \| 316 \| selective - borden - pork - service - abolish \| 15 \| 316_selective_borden_pork_service \| \| 317 \| gajarsky - yogi - njin - stark - pilot \| 15 \| 317_gajarsky_yogi_njin_stark \| \| 318 \| car - safety - centerline - saftey - collisions \| 15 \| 318_car_safety_centerline_saftey \| \| 319 \| orion - film - prototype - henry - goltz \| 15 \| 319_orion_film_prototype_henry \| \| 320 \| print - printer - file - claebaur - notepad \| 15 \| 320_print_printer_file_claebaur \| \| 321 \| dod - denizens - kotl - doom - muck \| 15 \| 321_dod_denizens_kotl_doom \| \| 322 \| display - remote - bielefeld - uphya001 - chooser \| 15 \| 322_display_remote_bielefeld_uphya001 \| \| 323 \| spacecraft - funding - cuts - calpoly - digex \| 15 \| 323_spacecraft_funding_cuts_calpoly \| \| 324 \| diesel - diesels - emissions - fuel - particulate \| 15 \| 324_diesel_diesels_emissions_fuel \| \| 325 \| uva - partying - virginia - schools - beyer \| 15 \| 325_uva_partying_virginia_schools \| \| 326 \| floptical - syquest - floppy - drives - floppies \| 15 \| 326_floptical_syquest_floppy_drives \| \| 327 \| placebo - gr - roth - medicine - ron \| 15 \| 327_placebo_gr_roth_medicine \| \| 328 \| canon - books - scripture - sirach - deuterocanonicals \| 15 \| 328_canon_books_scripture_sirach \| \| 329 \| eliot - flat - boxer - 180 - v12 \| 15 \| 329_eliot_flat_boxer_180 \| \| 330 \| firearms - smuggle - pound - guns - ban \| 15 \| 330_firearms_smuggle_pound_guns \| \| 331 \| paradox - borland - quicken - sql - access \| 15 \| 331_paradox_borland_quicken_sql \| \| 332 \| gun - buy - guns - stolen - buyback \| 15 \| 332_gun_buy_guns_stolen \| \| 333 \| uranium - plutonium - nuclear - ryukoku - mccall \| 15 \| 333_uranium_plutonium_nuclear_ryukoku \| \| 334 \| mosques - mosque - jerusalem - eggertj - jake \| 15 \| 334_mosques_mosque_jerusalem_eggertj \| \| 335 \| clock - mhz - quadra - oscillator - centris \| 15 \| 335_clock_mhz_quadra_oscillator \| \| 336 \| nixon - sternlight - mbeckman - crypto - strnlght \| 15 \| 336_nixon_sternlight_mbeckman_crypto \| \| 337 \| african - workers - blacks - employees - crime \| 15 \| 337_african_workers_blacks_employees \| \| 338 \| candida - vitamin - quack - pms - bloom \| 14 \| 338_candida_vitamin_quack_pms \| \| 339 \| pluto - mission - alaska - probes - aurora \| 14 \| 339_pluto_mission_alaska_probes \| \| 340 \| sabbath - salaris - black - lyrics - hell_2 \| 14 \| 340_sabbath_salaris_black_lyrics \| \| 341 \| cd - rom - cdrom - adaptec - 3401 \| 14 \| 341_cd_rom_cdrom_adaptec \| \| 342 \| fire - davidians - atf - fbi - napalm \| 14 \| 342_fire_davidians_atf_fbi \| \| 343 \| drink - drinking - riding - ride - pnakada \| 14 \| 343_drink_drinking_riding_ride \| \| 344 \| kubey - walks - obp - sac - hit \| 14 \| 344_kubey_walks_obp_sac \| \| 345 \| cache - iisi - powercache - card - fpu \| 14 \| 345_cache_iisi_powercache_card \| \| 346 \| murray - gm - quinn - vela - oakland \| 14 \| 346_murray_gm_quinn_vela \| \| 347 \| simms - 256k - jh - cciw - csx \| 14 \| 347_simms_256k_jh_cciw \| \| 348 \| 610 - centris - c610 - flaky - problems \| 14 \| 348_610_centris_c610_flaky \| \| 349 \| cview - temp - moscom - zenkar - urc \| 14 \| 349_cview_temp_moscom_zenkar \| \| 350 \| mhz - operational - clock - cpu - iisi \| 14 \| 350_mhz_operational_clock_cpu \| \| 351 \| lock - locks - cobra - kryptonite - cable \| 14 \| 351_lock_locks_cobra_kryptonite \| \| 352 \| wave - riceburner - squids - icomsim - squid \| 14 \| 352_wave_riceburner_squids_icomsim \| \| 353 \| alarm - viper - alarms - sensor - car \| 14 \| 353_alarm_viper_alarms_sensor \| \| 354 \| cubs - america - team - braves - talent \| 14 \| 354_cubs_america_team_braves \| \| 355 \| pope - schism - church - catholic - sspx \| 14 \| 355_pope_schism_church_catholic \| \| 356 \| christian - definition - christianity - jesus - christ \| 14 \| 356_christian_definition_christianity_jesus \| \| 357 \| bonds - williams - batting - giants - punjabi \| 14 \| 357_bonds_williams_batting_giants \| \| 358 \| bryce - arches - touring - dayton - fatcity \| 14 \| 358_bryce_arches_touring_dayton \| \| 359 \| sound - stereo - channel - quadra - microphone \| 14 \| 359_sound_stereo_channel_quadra \| \| 360 \| mormon - ceremonies - temple - temples - eusebius \| 14 \| 360_mormon_ceremonies_temple_temples \| \| 361 \| reincarnation - elijah - karma - palo - gerry \| 13 \| 361_reincarnation_elijah_karma_palo \| \| 362 \| fractal - fractals - compression - jr0930 - auckland \| 13 \| 362_fractal_fractals_compression_jr0930 \| \| 363 \| marriage - marry - mormon - eternal - parents \| 13 \| 363_marriage_marry_mormon_eternal \| \| 364 \| homeruns - boell - hit - hpcc01 - field \| 13 \| 364_homeruns_boell_hit_hpcc01 \| \| 365 \| tv - flyback - exploding - prasad - emerson \| 13 \| 365_tv_flyback_exploding_prasad \| \| 366 \| key - clarinet - tap - brad - proposal \| 13 \| 366_key_clarinet_tap_brad \| \| 367 \| costly - memorial - museum - holocaust - techbook \| 13 \| 367_costly_memorial_museum_holocaust \| \| 368 \| atm - fonts - tt - font - truetype \| 13 \| 368_atm_fonts_tt_font \| \| 369 \| solder - boards - mask - green - silver \| 13 \| 369_solder_boards_mask_green \| \| 370 \| temperature - henry - interstellar - sky - radiation \| 13 \| 370_temperature_henry_interstellar_sky \| \| 371 \| answerfax - harris - rrrrr - select - wwerner \| 13 \| 371_answerfax_harris_rrrrr_select \| \| 372 \| sale - suresh - mattress - table - rajaram \| 13 \| 372_sale_suresh_mattress_table \| \| 373 \| handling - ntuvax - ntu - ba7116326 - handson \| 13 \| 373_handling_ntuvax_ntu_ba7116326 \| \| 374 \| negev - bedouin - river - water - nysernet \| 13 \| 374_negev_bedouin_river_water \| \| 375 \| cults - cult - muttiah - religions - religion \| 13 \| 375_cults_cult_muttiah_religions \| \| 376 \| faith - saved - romans - lukewarm - deeds \| 13 \| 376_faith_saved_romans_lukewarm \| \| 377 \| rh - liar - lunatic - he - bissell \| 13 \| 377_rh_liar_lunatic_he \| \| 378 \| uart - 16550 - n5ial - uarts - modems \| 13 \| 378_uart_16550_n5ial_uarts \| \| 379 \| rens - overreacting - dgbt - tapped - doc \| 13 \| 379_rens_overreacting_dgbt_tapped \| \| 380 \| bible - language - commentary - christian - church \| 13 \| 380_bible_language_commentary_christian \| \| 381 \| xclrp - mydisplay - palette_colors - drawindex - draw \| 13 \| 381_xclrp_mydisplay_palette_colors_drawindex \| \| 382 \| oilers - pocklington - edmonton - northlands - yadallee \| 13 \| 382_oilers_pocklington_edmonton_northlands \| \| 383 \| clinton - clipper - bush - rwing - pat \| 13 \| 383_clinton_clipper_bush_rwing \| \| 384 \| easter - resurrection - celebration - pagan - goddess \| 13 \| 384_easter_resurrection_celebration_pagan \| \| 385 \| ampere - amp - db - ohmite - company \| 13 \| 385_ampere_amp_db_ohmite \| \| 386 \| logistician - 77 - wpi - ching - borque \| 13 \| 386_logistician_77_wpi_ching \| \| 387 \| vram - simms - quadra - 512k - slots \| 13 \| 387_vram_simms_quadra_512k \| \| 388 \| sin - hate - sinner - love - scott \| 13 \| 388_sin_hate_sinner_love \| \| 389 \| prayers - jesus - prayer - jayne - husband \| 12 \| 389_prayers_jesus_prayer_jayne \| \| 390 \| eridan - er1 - chuvashia - su - equip \| 12 \| 390_eridan_er1_chuvashia_su \| \| 391 \| context - jim - joslin - meritt - mwunix \| 12 \| 391_context_jim_joslin_meritt \| \| 392 \| mr2 - engine - eliot - noisy - shafts \| 12 \| 392_mr2_engine_eliot_noisy \| \| 393 \| habitable - planets - atmosphere - oxygen - everest \| 12 \| 393_habitable_planets_atmosphere_oxygen \| \| 394 \| sho - taurus - car - shifter - gk \| 12 \| 394_sho_taurus_car_shifter \| \| 395 \| hall - fame - kingman - winfield - garvey \| 12 \| 395_hall_fame_kingman_winfield \| \| 396 \| date - clock - dos - menu - stuck \| 12 \| 396_date_clock_dos_menu \| \| 397 \| cd300i - umcc - apple - cdrom - cd \| 12 \| 397_cd300i_umcc_apple_cdrom \| \| 398 \| beast - 666 - boylan - profile - usr \| 12 \| 398_beast_666_boylan_profile \| \| 399 \| printer - imagewriter - appletalk - laserwriter - uchile \| 12 \| 399_printer_imagewriter_appletalk_laserwriter \| \| 400 \| mpeg - quicktime - avi - melbourne - gregory \| 12 \| 400_mpeg_quicktime_avi_melbourne \| \| 401 \| zarathushtra - magi - josephus - jesus - iranian \| 12 \| 401_zarathushtra_magi_josephus_jesus \| \| 402 \| movies - bikes - csundh30 - cassidy - ursa \| 12 \| 402_movies_bikes_csundh30_cassidy \| \| 403 \| satan - evil - lucifer - god - free \| 12 \| 403_satan_evil_lucifer_god \| \| 404 \| solar - sail - sails - auburn - node \| 12 \| 404_solar_sail_sails_auburn \| \| 405 \| limbaugh - rush - nlns - hitler - sahl \| 12 \| 405_limbaugh_rush_nlns_hitler \| \| 406 \| warranty - techworks - credit - thacker - comtrade \| 12 \| 406_warranty_techworks_credit_thacker \| \| 407 \| hiram - vhs - dk - kou - koutd \| 12 \| 407_hiram_vhs_dk_kou \| \| 408 \| qur - koran - monash - bucaille - holy \| 12 \| 408_qur_koran_monash_bucaille \| \| 409 \| bike - shipping - manish - ups - ship \| 12 \| 409_bike_shipping_manish_ups \| \| 410 \| uniforms - marlins - lloyd - reds - mets \| 12 \| 410_uniforms_marlins_lloyd_reds \| \| 411 \| rle - tga - povray - tmp - pov \| 12 \| 411_rle_tga_povray_tmp \| \| 412 \| sunset - sunrise - drexel - cbis - rouben \| 12 \| 412_sunset_sunrise_drexel_cbis \| \| 413 \| virtual - mfltd - sts - reality - vr \| 11 \| 413_virtual_mfltd_sts_reality \| \| 414 \| ether - twist - mcaloon - dmcaloon - planets \| 11 \| 414_ether_twist_mcaloon_dmcaloon \| \| 415 \| witnesses - trial - gm - new - judge \| 11 \| 415_witnesses_trial_gm_new \| \| 416 \| disk - bios - drives - floppy - drive \| 11 \| 416_disk_bios_drives_floppy \| \| 417 \| hook - phone - led - ring - hok \| 11 \| 417_hook_phone_led_ring \| \| 418 \| pif - batch - bat - windows - environment \| 11 \| 418_pif_batch_bat_windows \| \| 419 \| opel - manta - kadett - uiuc - gibbonsa \| 11 \| 419_opel_manta_kadett_uiuc \| \| 420 \| winbench - winmarks - balog - diamond - stealth \| 11 \| 420_winbench_winmarks_balog_diamond \| \| 421 \| iran - gulf - iranian - uae - iraq \| 11 \| 421_iran_gulf_iranian_uae \| \| 422 \| voltage - current - supply - 12v - rooi \| 11 \| 422_voltage_current_supply_12v \| \| 423 \| wrench - srb - thiokol - pliers - tool \| 11 \| 423_wrench_srb_thiokol_pliers \| \| 424 \| xv - 24bit - image - 8bit - lilley \| 11 \| 424_xv_24bit_image_8bit \| \| 425 \| baptists - trincoll - banging - sociopaths - marrying \| 11 \| 425_baptists_trincoll_banging_sociopaths \| \| 426 \| jb - diabetes - ron - roth - anello \| 11 \| 426_jb_diabetes_ron_roth \| \| 427 \| jesus - commandments - god - law - commandment \| 11 \| 427_jesus_commandments_god_law \| \| 428 \| hitler - nazis - roehm - chancellor - nazi \| 11 \| 428_hitler_nazis_roehm_chancellor \| \| 429 \| freemasonry - masonry - masonic - baptist - southern \| 11 \| 429_freemasonry_masonry_masonic_baptist \| \| 430 \| cd300 - bauer - cd - multisession - toshiba \| 11 \| 430_cd300_bauer_cd_multisession \| \| 431 \| x11r5 - xsun - o_rdonly - fonts - 0666 \| 11 \| 431_x11r5_xsun_o_rdonly_fonts \| \| 432 \| controller - ide - bus - fdd - sec \| 11 \| 432_controller_ide_bus_fdd \| \| 433 \| gusto - heart - cardiac - uts - pvc \| 11 \| 433_gusto_heart_cardiac_uts \| \| 434 \| licensed - 2a42dubinski - carlos - change - hex \| 11 \| 434_licensed_2a42dubinski_carlos_change \| \| 435 \| convertible - wife - targa - wants - car \| 11 \| 435_convertible_wife_targa_wants \| \| 436 \| scores - posts - savoy - brock - hernandez \| 11 \| 436_scores_posts_savoy_brock \| \| 437 \| lcd - malouf - monitor - damico - projector \| 10 \| 437_lcd_malouf_monitor_damico \| \| 438 \| dtr - rts - dsr - cts - dce \| 10 \| 438_dtr_rts_dsr_cts \| \| 439 \| 2600 - atari - tia - 5200 - 4k \| 10 \| 439_2600_atari_tia_5200 \| \| 440 \| rs232 - ttl - ka3uww - loopback - ic \| 10 \| 440_rs232_ttl_ka3uww_loopback \| \| 441 \| contradictions - medtronic - archer - skiba - biblical \| 10 \| 441_contradictions_medtronic_archer_skiba \| \| 442 \| princeton - fester - black - roger - lazy \| 10 \| 442_princeton_fester_black_roger \| \| 443 \| wordbasic - filenames - format - file - word \| 10 \| 443_wordbasic_filenames_format_file \| </details> ## Training hyperparameters * calculate_probabilities: False * language: None * 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
boapps/szurkemarha-samba-lora	boapps	2024-05-08T08:43:17Z	4	0	peft	[ "peft", "safetensors", "text-generation", "hu", "dataset:boapps/szurkemarha", "base_model:sambanovasystems/SambaLingo-Hungarian-Base", "base_model:adapter:sambanovasystems/SambaLingo-Hungarian-Base", "license:apache-2.0", "region:us" ]	text-generation	2024-05-08T07:51:00Z	--- library_name: peft base_model: sambanovasystems/SambaLingo-Hungarian-Base license: apache-2.0 datasets: - boapps/szurkemarha language: - hu widget: - messages: - role: user content: Mennyi 2+2? pipeline_tag: text-generation --- Ez a repo csak a lora adaptert tartalmazza. A [sambanovasystems/SambaLingo-Hungarian-Base](https://huggingface.co/sambanovasystems/SambaLingo-Hungarian-Base) finomhangolásával jött létre. A modell semmilyen etikai/biztonsági tesztelésen nem esett át. Éles használata nem ajánlott.
youngsangroh/whisper-small-finetuned-atco2-asr-atcosim	youngsangroh	2024-05-08T08:43:06Z	89	1	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:jlvdoorn/atco2-asr-atcosim", "base_model:openai/whisper-small", "base_model:finetune:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-08T05:52:29Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - jlvdoorn/atco2-asr-atcosim metrics: - wer model-index: - name: Whisper Small En - Whisper with atco2-asr-atcosim results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: 'This is a dataset constructed from two datasets: ATCO2-ASR and ATCOSIM.' type: jlvdoorn/atco2-asr-atcosim args: 'config: en, split: test' metrics: - name: Wer type: wer value: 0.02577651759247326 --- <!-- 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 En - Whisper with atco2-asr-atcosim This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the This is a dataset constructed from two datasets: ATCO2-ASR and ATCOSIM. dataset. It achieves the following results on the evaluation set: - Loss: 0.0010 - Wer: 0.0258 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 4000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:------:\| \| 0.0637 \| 1.9763 \| 1000 \| 0.0962 \| 7.4365 \| \| 0.0154 \| 3.9526 \| 2000 \| 0.0163 \| 2.3972 \| \| 0.002 \| 5.9289 \| 3000 \| 0.0027 \| 1.5015 \| \| 0.0003 \| 7.9051 \| 4000 \| 0.0010 \| 0.0258 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
jsingh/autoflow-math-v0.3	jsingh	2024-05-08T08:37:30Z	0	1	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-04-27T00:32:14Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
qminh369/token-classification-llmlingua2-xlm-roberta-41k_remove_stop_word_10_epoch	qminh369	2024-05-08T08:36:14Z	137	0	transformers	[ "transformers", "tensorboard", "safetensors", "xlm-roberta", "token-classification", "generated_from_trainer", "base_model:FacebookAI/xlm-roberta-large", "base_model:finetune:FacebookAI/xlm-roberta-large", "license:mit", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T08:04:48Z	--- license: mit base_model: FacebookAI/xlm-roberta-large tags: - generated_from_trainer model-index: - name: token-classification-llmlingua2-xlm-roberta-41k_remove_stop_word_10_epoch results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # token-classification-llmlingua2-xlm-roberta-41k_remove_stop_word_10_epoch This model is a fine-tuned version of [FacebookAI/xlm-roberta-large](https://huggingface.co/FacebookAI/xlm-roberta-large) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2501 ## 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: 96 - eval_batch_size: 96 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| No log \| 1.0 \| 345 \| 0.2501 \| \| 0.1406 \| 2.0 \| 690 \| 0.2848 \| \| 0.1101 \| 3.0 \| 1035 \| 0.2821 \| \| 0.1101 \| 4.0 \| 1380 \| 0.3145 \| \| 0.1016 \| 5.0 \| 1725 \| 0.3281 \| \| 0.0965 \| 6.0 \| 2070 \| 0.3272 \| \| 0.0965 \| 7.0 \| 2415 \| 0.3236 \| \| 0.093 \| 8.0 \| 2760 \| 0.3298 \| \| 0.0907 \| 9.0 \| 3105 \| 0.3336 \| \| 0.0907 \| 10.0 \| 3450 \| 0.3396 \| ### Framework versions - Transformers 4.39.0.dev0 - Pytorch 2.2.1+cu118 - Datasets 2.18.0 - Tokenizers 0.15.2
boapps/szurkemarha-samba	boapps	2024-05-08T08:32:27Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "hu", "dataset:boapps/szurkemarha", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:48:14Z	--- license: apache-2.0 datasets: - boapps/szurkemarha language: - hu --- A [sambanovasystems/SambaLingo-Hungarian-Base](https://huggingface.co/sambanovasystems/SambaLingo-Hungarian-Base) finomhangolásával jött létre. A modell semmilyen etikai/biztonsági tesztelésen nem esett át. Éles használata nem ajánlott.
SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw	SicariusSicariiStuff	2024-05-08T08:32:02Z	6	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-08T05:52:57Z	--- language: - en license: apache-2.0 --- <div align="center"> <b style="font-size: 40px;">Tenebra_30B_Alpha01_FP16</b> </div> <img src="https://i.imgur.com/WkkCtZL.png" alt="Tenebră" style="width: 50%; min-width: 400px; display: block; margin: auto;"> # Model Details Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach. Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing. While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră! ## Tenebră is available at the following size and flavours: - 13B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_4BIT) \| [GPTQ_4-BIT_group-size-32](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_32g_4BIT) \| [GGUF-Many_Quants](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GGUF) - 30B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_4BIT) \| [GPTQ_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT) \| [EXL2_2.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-50bpw) \| [EXL2_2.8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-80bpw) \| [EXL2_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw) \| [EXL2_5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5bpw) \| [EXL2_5.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5-50bpw) \| [EXL2_6-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6bpw) \| [EXL2_6.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw) \| [EXL2_8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw) ### Support <img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;"> - [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻 - [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻 ## Disclaimer *This model is pretty uncensored, use responsibly ## Other stuff - [Experemental TTS extension for oobabooga](https://github.com/SicariusSicariiStuff/Diffusion_TTS) Based on Tortoise, EXTREMELY good quality, IF, and that's a big if, you can make it to work! - [Demonstration of the TTS capabilities](https://www.youtube.com/watch?v=V6ewxU6c1W8) Charsi narrates her story, Diablo2 (18+) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.18\| \|AI2 Reasoning Challenge (25-Shot)\|64.51\| \|HellaSwag (10-Shot) \|84.79\| \|MMLU (5-Shot) \|54.29\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|24.64\|
asiansoul/YachtRP-Llama-3-KoEn-8B	asiansoul	2024-05-08T08:30:20Z	8	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "arxiv:2311.03099", "arxiv:2306.01708", "base_model:NousResearch/Meta-Llama-3-8B", "base_model:merge:NousResearch/Meta-Llama-3-8B", "base_model:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:merge:NousResearch/Meta-Llama-3-8B-Instruct", "base_model:beomi/Llama-3-KoEn-8B", "base_model:merge:beomi/Llama-3-KoEn-8B", "base_model:beomi/Llama-3-KoEn-8B-Instruct-preview", "base_model:merge:beomi/Llama-3-KoEn-8B-Instruct-preview", "base_model:dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2", "base_model:merge:dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2", "base_model:dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5", "base_model:merge:dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5", "base_model:elyn-dev/Llama-3-Soliloquy-8B-v2", "base_model:merge:elyn-dev/Llama-3-Soliloquy-8B-v2", "base_model:lodrick-the-lafted/Olethros-8B", "base_model:merge:lodrick-the-lafted/Olethros-8B", "base_model:saltlux/Ko-Llama3-Luxia-8B", "base_model:merge:saltlux/Ko-Llama3-Luxia-8B", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-07T18:01:44Z	--- base_model: - saltlux/Ko-Llama3-Luxia-8B - beomi/Llama-3-KoEn-8B-preview - NousResearch/Meta-Llama-3-8B - dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5 - openlynn/Llama-3-Soliloquy-8B-v2 - lodrick-the-lafted/Olethros-8B - dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 - NousResearch/Meta-Llama-3-8B-Instruct - beomi/Llama-3-KoEn-8B-Instruct-preview library_name: transformers tags: - mergekit - merge --- # YachtRP-Llama-3-KoEn-8B <a href="https://ibb.co/jD17fJ9"><img src="https://i.ibb.co/6Ff6wXc/Screenshot-2024-05-08-at-5-07-53-PM.png" alt="Screenshot-2024-05-08-at-5-07-53-PM" border="0"></a> 🚨 Yacht Korean / English RP Merge Test Model. Please note that this version is an English/Korean RP test version, so it may not operate properly. The answers may contain inappropriate content, so please use them carefully for testing purposes only. model_stock method is not good performance by my human rp test. so use dare_tie for both kr / en All licenses belong to those below, so please use it for personal and academic use only.🚨 ## Merge Details ### Merge Method This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [NousResearch/Meta-Llama-3-8B](https://huggingface.co/NousResearch/Meta-Llama-3-8B) as a base. ### Models Merged The following models were included in the merge: * [saltlux/Ko-Llama3-Luxia-8B](https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B) * [beomi/Llama-3-KoEn-8B-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-preview) * [dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5](https://huggingface.co/dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5) * [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2) * [lodrick-the-lafted/Olethros-8B](https://huggingface.co/lodrick-the-lafted/Olethros-8B) * [dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2](https://huggingface.co/dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2) * [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct) * [beomi/Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview) ### Configuration The following YAML configuration was used to produce this model: ```yaml models: - model: NousResearch/Meta-Llama-3-8B - model: NousResearch/Meta-Llama-3-8B-Instruct parameters: density: 0.60 weight: 0.25 - model: beomi/Llama-3-KoEn-8B-preview parameters: density: 0.55 weight: 0.2 - model: saltlux/Ko-Llama3-Luxia-8B parameters: density: 0.55 weight: 0.1 - model: beomi/Llama-3-KoEn-8B-Instruct-preview parameters: density: 0.55 weight: 0.15 - model: dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2 parameters: density: 0.55 weight: 0.1 - model: dreamgen-preview/opus-v1.2-llama-3-8b-instruct-run3.5-epoch2.5 parameters: density: 0.55 weight: 0.1 - model: openlynn/Llama-3-Soliloquy-8B-v2 parameters: density: 0.55 weight: 0.1 - model: lodrick-the-lafted/Olethros-8B parameters: density: 0.55 weight: 0.1 merge_method: dare_ties base_model: NousResearch/Meta-Llama-3-8B parameters: int8_mask: true dtype: bfloat16 ``` ### Test <a href="https://ibb.co/whh7Stk"><img src="https://i.ibb.co/k22J4Z7/Screenshot-2024-05-08-at-4-27-33-PM.png" alt="Screenshot-2024-05-08-at-4-27-33-PM" border="0"></a> ### Citation instructions Ko-Llama3-Luxia-8B ``` @article{kollama3luxiamodelcard, title={Ko Llama 3 Luxia Model Card}, author={AILabs@Saltux}, year={2024}, url={https://huggingface.co/saltlux/Ko-Llama3-Luxia-8B/blob/main/README.md} } ``` Original Llama-3 ``` @article{llama3modelcard, title={Llama 3 Model Card}, author={AI@Meta}, year={2024}, url={https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md} } ``` Llama-3-Open-Ko ``` @article{llama3koen, title={Llama-3-KoEn}, author={L, Junbum}, year={2024}, url={https://huggingface.co/beomi/Llama-3-KoEn-8B} } ```
4season/sft_model_test1	4season	2024-05-08T08:30:00Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:16:03Z	--- license: apache-2.0 language: - en --- # 4season/sft_model_test1 # Introduction This model is test version, sft model. We utilize state-of-the-art instruction fine-tuning methods. ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-05 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 8 - total_train_batch_size: 128 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - num_epochs: 1.0
imagepipeline/cun	imagepipeline	2024-05-08T08:27:35Z	0	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-08T08:27:33Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## cun <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - cunnilingus [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/cun?id=4452aee9-6998-46de-9323-1a5a05db5c3c/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "4452aee9-6998-46de-9323-1a5a05db5c3c", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
huynq3Cyradar/bert-large-finetuned-phishing-webpage-version	huynq3Cyradar	2024-05-08T08:26:01Z	110	0	transformers	[ "transformers", "tensorboard", "safetensors", "bert", "text-classification", "generated_from_trainer", "base_model:google-bert/bert-base-multilingual-cased", "base_model:finetune:google-bert/bert-base-multilingual-cased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-06T09:43:34Z	--- license: apache-2.0 base_model: google-bert/bert-base-multilingual-cased tags: - generated_from_trainer metrics: - accuracy - precision - recall model-index: - name: bert-large-finetuned-phishing-webpage-version results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # bert-large-finetuned-phishing-webpage-version This model is a fine-tuned version of [google-bert/bert-base-multilingual-cased](https://huggingface.co/google-bert/bert-base-multilingual-cased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.2062 - Accuracy: 0.9188 - Precision: 0.9517 - Recall: 0.8689 - False Positive Rate: 0.0381 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - gradient_accumulation_steps: 8 - total_train_batch_size: 256 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| Precision \| Recall \| False Positive Rate \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:---------:\|:------:\|:-------------------:\| \| No log \| 1.0 \| 394 \| 0.2675 \| 0.8918 \| 0.9680 \| 0.7926 \| 0.0226 \| \| 0.3256 \| 2.0 \| 788 \| 0.2225 \| 0.9124 \| 0.9640 \| 0.8424 \| 0.0272 \| \| 0.2008 \| 3.0 \| 1182 \| 0.2062 \| 0.9188 \| 0.9517 \| 0.8689 \| 0.0381 \| ### Framework versions - Transformers 4.38.2 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
stablediffusionapi/03	stablediffusionapi	2024-05-08T08:23:16Z	29	1	diffusers	[ "diffusers", "modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-08T08:20:09Z	--- license: creativeml-openrail-m tags: - modelslab.com - stable-diffusion-api - text-to-image - ultra-realistic pinned: true --- # test03 API Inference ![generated from modelslab.com](https://pub-3626123a908346a7a8be8d9295f44e26.r2.dev/generations/5606217591715155776.png) ## Get API Key Get API key from [ModelsLab API](http://modelslab.com), No Payment needed. Replace Key in below code, change model_id to "03" Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs) Try model for free: [Generate Images](https://modelslab.com/models/03) Model link: [View model](https://modelslab.com/models/03) View all models: [View Models](https://modelslab.com/models) import requests import json url = "https://modelslab.com/api/v6/images/text2img" payload = json.dumps({ "key": "your_api_key", "model_id": "03", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None }) headers = { 'Content-Type': 'application/json' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) > Use this coupon code to get 25% off DMGG0RBN
Roselia-penguin/medical_llama3_8b	Roselia-penguin	2024-05-08T08:22:57Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code", "medical", "llama-factory", "conversational", "arxiv:1910.09700", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T06:55:26Z	--- license: apache-2.0 tags: - code - medical - llama-factory --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1). ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
ameerazam08/MuseTalk	ameerazam08	2024-05-08T08:20:48Z	0	3	diffusers	[ "diffusers", "onnx", "safetensors", "license:creativeml-openrail-m", "region:us" ]	null	2024-05-08T08:03:08Z	--- title: MuseTalkDemo emoji: 🌍 colorFrom: gray colorTo: purple sdk: docker pinned: false license: creativeml-openrail-m app_file: app.py app_port: 7860 --- ALL Setup for MuseTalk Clone and Run ``` Build environment We recommend a python version >=3.10 and cuda version =11.7. Then build environment as follows: pip install -r requirements.txt mmlab packages pip install --no-cache-dir -U openmim mim install mmengine mim install "mmcv>=2.0.1" mim install "mmdet>=3.1.0" mim install "mmpose>=1.1.0" Download ffmpeg-static Download the ffmpeg-static and export FFMPEG_PATH=/path/to/ffmpeg for example: export FFMPEG_PATH=/musetalk/ffmpeg-4.4-amd64-static Download weights You can download weights manually as follows: Download our trained weights. Download the weights of other components: sd-vae-ft-mse whisper dwpose face-parse-bisent resnet18 Finally, these weights should be organized in models as follows: ./models/ ├── musetalk │ └── musetalk.json │ └── pytorch_model.bin ├── dwpose │ └── dw-ll_ucoco_384.pth ├── face-parse-bisent │ ├── 79999_iter.pth │ └── resnet18-5c106cde.pth ├── sd-vae-ft-mse │ ├── config.json │ └── diffusion_pytorch_model.bin └── whisper └── tiny.pt Quickstart Inference Here, we provide the inference script. python -m scripts.inference --inference_config configs/inference/test.yaml configs/inference/test.yaml is the path to the inference configuration file, including video_path and audio_path. The video_path should be either a video file, an image file or a directory of images. You are recommended to input video with 25fps, the same fps used when training the model. If your video is far less than 25fps, you are recommended to apply frame interpolation or directly convert the video to 25fps using ffmpeg. Use of bbox_shift to have adjustable results 🔎 We have found that upper-bound of the mask has an important impact on mouth openness. Thus, to control the mask region, we suggest using the bbox_shift parameter. Positive values (moving towards the lower half) increase mouth openness, while negative values (moving towards the upper half) decrease mouth openness. You can start by running with the default configuration to obtain the adjustable value range, and then re-run the script within this range. For example, in the case of Xinying Sun, after running the default configuration, it shows that the adjustable value rage is [-9, 9]. Then, to decrease the mouth openness, we set the value to be -7. python -m scripts.inference --inference_config configs/inference/test.yaml --bbox_shift -7 📌 More technical details can be found in bbox_shift. Combining MuseV and MuseTalk As a complete solution to virtual human generation, you are suggested to first apply MuseV to generate a video (text-to-video, image-to-video or pose-to-video) by referring this. Frame interpolation is suggested to increase frame rate. Then, you can use MuseTalk to generate a lip-sync video by referring this. 🆕 Real-time inference Here, we provide the inference script. This script first applies necessary pre-processing such as face detection, face parsing and VAE encode in advance. During inference, only UNet and the VAE decoder are involved, which makes MuseTalk real-time. python -m scripts.realtime_inference --inference_config configs/inference/realtime.yaml --batch_size 4 configs/inference/realtime.yaml is the path to the real-time inference configuration file, including preparation, video_path , bbox_shift and audio_clips. Set preparation to True in realtime.yaml to prepare the materials for a new avatar. (If the bbox_shift has changed, you also need to re-prepare the materials.) After that, the avatar will use an audio clip selected from audio_clips to generate video. Inferring using: data/audio/yongen.wav While MuseTalk is inferring, sub-threads can simultaneously stream the results to the users. The generation process can achieve 30fps+ on an NVIDIA Tesla V100. Set preparation to False and run this script if you want to genrate more videos using the same avatar. Note for Real-time inference If you want to generate multiple videos using the same avatar/video, you can also use this script to SIGNIFICANTLY expedite the generation process. In the previous script, the generation time is also limited by I/O (e.g. saving images). If you just want to test the generation speed without saving the images, you can run python -m scripts.realtime_inference --inference_config configs/inference/realtime.yaml --skip_save_images ```
dinhhung1508/Seallm-7b-v2.5-summary-vietnamese-article-v1-gguf	dinhhung1508	2024-05-08T08:20:35Z	13	0	transformers	[ "transformers", "gguf", "gemma", "text-generation-inference", "unsloth", "en", "base_model:SeaLLMs/SeaLLM-7B-v2.5", "base_model:quantized:SeaLLMs/SeaLLM-7B-v2.5", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-08T08:18:45Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - gguf base_model: SeaLLMs/SeaLLM-7B-v2.5 --- # Uploaded model - Developed by: dinhhung1508 - License: apache-2.0 - Finetuned from model : SeaLLMs/SeaLLM-7B-v2.5 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Sumail/Chalice15	Sumail	2024-05-08T08:17:07Z	131	0	transformers	[ "transformers", "safetensors", "stablelm", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T08:15:35Z	--- base_model: - vapegod/stable5 - vapegod/stable library_name: transformers tags: - mergekit - merge --- # merge This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit). ## Merge Details ### Merge Method This model was merged using the SLERP merge method. ### Models Merged The following models were included in the merge: * [vapegod/stable5](https://huggingface.co/vapegod/stable5) * [vapegod/stable](https://huggingface.co/vapegod/stable) ### Configuration The following YAML configuration was used to produce this model: ```yaml slices: - sources: - model: vapegod/stable5 layer_range: [0, 24] - model: vapegod/stable layer_range: [0, 24] merge_method: slerp base_model: vapegod/stable5 parameters: t: - filter: self_attn value: [0, 0.5, 0.3, 0.7, 1] - filter: mlp value: [1, 0.5, 0.7, 0.3, 0] - value: 0.5 dtype: bfloat16 ```
opencsg/csg-wukong-1B-sft-bf16	opencsg	2024-05-08T08:15:23Z	151	1	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-30T14:48:46Z	--- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- # csg-wukong-1B-sft-bf16 [[中文]](#chinese) [[English]](#english) <a id="english"></a> <p align="center"> <img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg"> </p> <p align="center"><a href="https://portal.opencsg.com/models">[OpenCSG Community]</a> <a href="hhttps://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p> </div> OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models. The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively. ## Model Description csg-wukong-1B-sft-bf16 was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B). <br> we will introduce more information about csg-wukong-1B. ## Model Evaluation results We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the results show our model ranked the 8th among the ~1.5B pretrained small language models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/_HRTxL6N0qnNPNt-P8k9k.png) # Training ## Hardware - GPUs: 16 H800 - Training time: 43days ## Software - Orchestration: [Deepspeed](https://github.com/OpenCSGs) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) <a id="chinese"></a> <p> </p> # OpenCSG介绍 <p align="center"> <img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p> </div> OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。 OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。 ## 模型介绍 csg-wukong-1B-sft-bf16 在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B)预训练模型上微调而成. <br> 我们将在后面介绍更多关于这个模型的信息。 ## 模型评测结果我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/ZfWZ1Fd7ccKrJVx0okV9z.png) # 训练 ## 硬件资源 - GPU数量： 16 H800 - 训练时间： 43天 ## 软件使用 - 微调训练框架： [Deepspeed](https://github.com/OpenCSGs) - 深度学习框架： [PyTorch](https://github.com/pytorch/pytorch) - BP16： [apex](https://github.com/NVIDIA/apex)
opencsg/csg-wukong-1B-sft-dpo-bf16	opencsg	2024-05-08T08:14:58Z	150	3	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code", "conversational", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-30T14:31:24Z	--- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- # csg-wukong-1B-sft-dpo-bf16 [[中文]](#chinese) [[English]](#english) <a id="english"></a> <p align="center"> <img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg"> </p> <p align="center"><a href="https://portal.opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p> </div> OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models. The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively. ## Model Description csg-wukong-1B-sft-dpo-bf16 was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B). <br> we will introduce more information about csg-wukong-1B. ## Model Evaluation results We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the results show our model ranked the 8th among the ~1.5B pretrained small language models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/_HRTxL6N0qnNPNt-P8k9k.png) # Training ## Hardware - GPUs: 16 H800 - Training time: 43days ## Software - Orchestration: [Deepspeed](https://github.com/OpenCSGs) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) <a id="chinese"></a> <p> </p> # OpenCSG介绍 <p align="center"> <img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMss">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p> </div> OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。 OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。 ## 模型介绍 csg-wukong-1B-sft-dpo-bf16 在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B)预训练模型上微调而成. <br> 我们将在后面介绍更多关于这个模型的信息。 ## 模型评测结果我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/ZfWZ1Fd7ccKrJVx0okV9z.png) # 训练 ## 硬件资源 - GPU数量： 16 H800 - 训练时间： 43天 ## 软件使用 - 微调训练框架： [Deepspeed](https://github.com/OpenCSGs) - 深度学习框架： [PyTorch](https://github.com/pytorch/pytorch) - BP16： [apex](https://github.com/NVIDIA/apex)
opencsg/csg-wukong-1B-chat-v0.1	opencsg	2024-05-08T08:14:33Z	161	6	transformers	[ "transformers", "safetensors", "llama", "text-generation", "code", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-04-12T10:18:45Z	--- language: - en pipeline_tag: text-generation tags: - code license: apache-2.0 --- # csg-wukong-1B-chat-v0.1 [[中文]](#chinese) [[English]](#english) <a id="english"></a> <p align="center"> <img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg"> </p> <p align="center"><a href="https://portal.opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p> </div> OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models. The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively. ## Model Description csg-wukong-1B-chat-v0.1 was finetuned on csg-wukong-1B <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/sZvOqCJY4gOEvVhpmlH_N.png) ## Model Evaluation results We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and the results show our model ranked the 8th among the ~1.5B pretrained small language models. ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/_HRTxL6N0qnNPNt-P8k9k.png) # Training ## Hardware - GPUs: 6 V100 - Training time: 6 hours ## Software - Orchestration: [Deepspeed](https://github.com/OpenCSGs) - Neural networks: [PyTorch](https://github.com/pytorch/pytorch) - BP16 if applicable: [apex](https://github.com/NVIDIA/apex) <a id="chinese"></a> <p> </p> # OpenCSG介绍 <p align="center"> <img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg"> </p> <p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/OpenCSGs/Awesome-SLMs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p> </div> OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。 OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。 ## 模型介绍 csg-wukong-1B-chat-v0.1 在csg-wukong-1B模型上微调而成。 <br> ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/YrpSwEsRGdaQj56__8o0U.png) ## 模型评测结果我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。 ![image/png](https://cdn-uploads.huggingface.co/production/uploads/661790397437201d78141856/ZfWZ1Fd7ccKrJVx0okV9z.png) # 训练 ## 硬件资源 - GPU数量： 6 V100 - 训练时间： 6小时 ## 软件使用 - 微调训练框架： [Deepspeed](https://github.com/OpenCSGs) - 深度学习框架： [PyTorch](https://github.com/pytorch/pytorch) - BP16： [apex](https://github.com/NVIDIA/apex)
PetroGPT/breeze-petro-7b-instruct-v1-q4_k_m.gguf	PetroGPT	2024-05-08T08:12:14Z	2	0	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-08T08:00:07Z	--- license: apache-2.0 ---
bllossom-advanced/bllossom-llama-3-8b-65k-base	bllossom-advanced	2024-05-08T08:09:44Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:46:33Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
iguanaYu/distilroberta-base-finetuned-wikitext2	iguanaYu	2024-05-08T08:08:26Z	163	0	transformers	[ "transformers", "tensorboard", "safetensors", "roberta", "fill-mask", "generated_from_trainer", "base_model:distilbert/distilroberta-base", "base_model:finetune:distilbert/distilroberta-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	fill-mask	2024-05-08T07:41:22Z	--- license: apache-2.0 base_model: distilroberta-base tags: - generated_from_trainer model-index: - name: distilroberta-base-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilroberta-base-finetuned-wikitext2 This model is a fine-tuned version of [distilroberta-base](https://huggingface.co/distilroberta-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.8611 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 2.0841 \| 1.0 \| 2406 \| 1.9362 \| \| 1.9866 \| 2.0 \| 4812 \| 1.8845 \| \| 1.9442 \| 3.0 \| 7218 \| 1.8355 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
gaianet/Nomic-embed-text-v1.5-Embedding-GGUF	gaianet	2024-05-08T08:04:43Z	35,640	5	null	[ "gguf", "license:apache-2.0", "endpoints_compatible", "region:us", "feature-extraction" ]	null	2024-05-08T07:49:18Z	--- license: apache-2.0 ---
dinhhung1508/Seallm-7b-v2.5-summary-vietnamese-article-v1	dinhhung1508	2024-05-08T07:59:36Z	0	0	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "gemma", "trl", "en", "base_model:SeaLLMs/SeaLLM-7B-v2.5", "base_model:finetune:SeaLLMs/SeaLLM-7B-v2.5", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-05-08T07:59:34Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: SeaLLMs/SeaLLM-7B-v2.5 --- # Uploaded model - Developed by: dinhhung1508 - License: apache-2.0 - Finetuned from model : SeaLLMs/SeaLLM-7B-v2.5 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
dinhhung1508/Seallm-7b-v2.5-summary-vietnamese-article-v1-merged_4bit	dinhhung1508	2024-05-08T07:57:06Z	81	0	transformers	[ "transformers", "safetensors", "gemma", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:SeaLLMs/SeaLLM-7B-v2.5", "base_model:quantized:SeaLLMs/SeaLLM-7B-v2.5", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "4-bit", "bitsandbytes", "region:us" ]	text-generation	2024-05-08T07:55:32Z	--- language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - gemma - trl base_model: SeaLLMs/SeaLLM-7B-v2.5 --- # Uploaded model - Developed by: dinhhung1508 - License: apache-2.0 - Finetuned from model : SeaLLMs/SeaLLM-7B-v2.5 This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
SinniDcat/LLAMA3-chnese-instrument-test-lora_model	SinniDcat	2024-05-08T07:54:15Z	0	0	null	[ "safetensors", "license:apache-2.0", "region:us" ]	null	2024-05-08T07:47:54Z	--- license: apache-2.0 ---
yweslakarep/vit-base-patch16-224-in21k-finetuned-lora-food101	yweslakarep	2024-05-08T07:52:37Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-08T07:52:35Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
chibeenot/lora_model_test	chibeenot	2024-05-08T07:52:30Z	1	0	peft	[ "peft", "arxiv:1910.09700", "base_model:ai-forever/FRED-T5-1.7B", "base_model:adapter:ai-forever/FRED-T5-1.7B", "region:us" ]	null	2024-05-08T06:26:45Z	--- library_name: peft base_model: ai-forever/FRED-T5-1.7B --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.7.1
ahmed-kh/superhero	ahmed-kh	2024-05-08T07:50:12Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-08T07:50:12Z	--- license: apache-2.0 ---
hustvl/yolos-small	hustvl	2024-05-08T07:49:12Z	49,030	61	transformers	[ "transformers", "pytorch", "safetensors", "yolos", "object-detection", "vision", "dataset:coco", "arxiv:2106.00666", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2022-04-26T09:38:22Z	--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # YOLOS (small-sized) model YOLOS model fine-tuned on COCO 2017 object detection (118k annotated images). It was introduced in the paper [You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection](https://arxiv.org/abs/2106.00666) by Fang et al. and first released in [this repository](https://github.com/hustvl/YOLOS). Disclaimer: The team releasing YOLOS did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description YOLOS is a Vision Transformer (ViT) trained using the DETR loss. Despite its simplicity, a base-sized YOLOS model is able to achieve 42 AP on COCO validation 2017 (similar to DETR and more complex frameworks such as Faster R-CNN). The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=hustvl/yolos) to look for all available YOLOS models. ### How to use Here is how to use this model: ```python from transformers import YolosFeatureExtractor, YolosForObjectDetection from PIL import Image import requests url = 'http://images.cocodataset.org/val2017/000000039769.jpg' image = Image.open(requests.get(url, stream=True).raw) feature_extractor = YolosFeatureExtractor.from_pretrained('hustvl/yolos-small') model = YolosForObjectDetection.from_pretrained('hustvl/yolos-small') inputs = feature_extractor(images=image, return_tensors="pt") outputs = model(inputs) # model predicts bounding boxes and corresponding COCO classes logits = outputs.logits bboxes = outputs.pred_boxes ``` Currently, both the feature extractor and model support PyTorch. ## Training data The YOLOS model was pre-trained on [ImageNet-1k](https://huggingface.co/datasets/imagenet2012) and fine-tuned on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### Training The model was pre-trained for 200 epochs on ImageNet-1k and fine-tuned for 150 epochs on COCO. ## Evaluation results This model achieves an AP (average precision) of 36.1** on COCO 2017 validation. For more details regarding evaluation results, we refer to table 1 of the original paper. ### BibTeX entry and citation info ```bibtex @article{DBLP:journals/corr/abs-2106-00666, author = {Yuxin Fang and Bencheng Liao and Xinggang Wang and Jiemin Fang and Jiyang Qi and Rui Wu and Jianwei Niu and Wenyu Liu}, title = {You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection}, journal = {CoRR}, volume = {abs/2106.00666}, year = {2021}, url = {https://arxiv.org/abs/2106.00666}, eprinttype = {arXiv}, eprint = {2106.00666}, timestamp = {Fri, 29 Apr 2022 19:49:16 +0200}, biburl = {https://dblp.org/rec/journals/corr/abs-2106-00666.bib}, bibsource = {dblp computer science bibliography, https://dblp.org} } ```
microsoft/conditional-detr-resnet-50	microsoft	2024-05-08T07:48:26Z	7,627	9	transformers	[ "transformers", "pytorch", "safetensors", "conditional_detr", "object-detection", "vision", "dataset:coco", "arxiv:2108.06152", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2022-09-09T06:11:40Z	--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # Conditional DETR model with ResNet-50 backbone Conditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Meng et al. and first released in [this repository](https://github.com/Atten4Vis/ConditionalDETR). ## Model description The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/conditional_detr_curve.jpg) ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=microsoft/conditional-detr) to look for all available Conditional DETR models. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, ConditionalDetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("microsoft/conditional-detr-resnet-50") model = ConditionalDetrForObjectDetection.from_pretrained("microsoft/conditional-detr-resnet-50") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.7 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected remote with confidence 0.833 at location [38.31, 72.1, 177.63, 118.45] Detected cat with confidence 0.831 at location [9.2, 51.38, 321.13, 469.0] Detected cat with confidence 0.804 at location [340.3, 16.85, 642.93, 370.95] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The Conditional DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### BibTeX entry and citation info ```bibtex @inproceedings{MengCFZLYS021, author = {Depu Meng and Xiaokang Chen and Zejia Fan and Gang Zeng and Houqiang Li and Yuhui Yuan and Lei Sun and Jingdong Wang}, title = {Conditional {DETR} for Fast Training Convergence}, booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV} 2021, Montreal, QC, Canada, October 10-17, 2021}, } ```
SenseTime/deformable-detr	SenseTime	2024-05-08T07:47:14Z	10,552	19	transformers	[ "transformers", "pytorch", "safetensors", "deformable_detr", "object-detection", "vision", "dataset:coco", "arxiv:2010.04159", "license:apache-2.0", "endpoints_compatible", "region:us" ]	object-detection	2022-03-02T23:29:05Z	--- license: apache-2.0 tags: - object-detection - vision datasets: - coco widget: - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg example_title: Savanna - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg example_title: Football Match - src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg example_title: Airport --- # Deformable DETR model with ResNet-50 backbone Deformable DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Deformable DETR: Deformable Transformers for End-to-End Object Detection](https://arxiv.org/abs/2010.04159) by Zhu et al. and first released in [this repository](https://github.com/fundamentalvision/Deformable-DETR). Disclaimer: The team releasing Deformable DETR did not write a model card for this model so this model card has been written by the Hugging Face team. ## Model description The DETR model is an encoder-decoder transformer with a convolutional backbone. Two heads are added on top of the decoder outputs in order to perform object detection: a linear layer for the class labels and a MLP (multi-layer perceptron) for the bounding boxes. The model uses so-called object queries to detect objects in an image. Each object query looks for a particular object in the image. For COCO, the number of object queries is set to 100. The model is trained using a "bipartite matching loss": one compares the predicted classes + bounding boxes of each of the N = 100 object queries to the ground truth annotations, padded up to the same length N (so if an image only contains 4 objects, 96 annotations will just have a "no object" as class and "no bounding box" as bounding box). The Hungarian matching algorithm is used to create an optimal one-to-one mapping between each of the N queries and each of the N annotations. Next, standard cross-entropy (for the classes) and a linear combination of the L1 and generalized IoU loss (for the bounding boxes) are used to optimize the parameters of the model. ![model image](https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/deformable_detr_architecture.png) ## Intended uses & limitations You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=sensetime/deformable-detr) to look for all available Deformable DETR models. ### How to use Here is how to use this model: ```python from transformers import AutoImageProcessor, DeformableDetrForObjectDetection import torch from PIL import Image import requests url = "http://images.cocodataset.org/val2017/000000039769.jpg" image = Image.open(requests.get(url, stream=True).raw) processor = AutoImageProcessor.from_pretrained("SenseTime/deformable-detr") model = DeformableDetrForObjectDetection.from_pretrained("SenseTime/deformable-detr") inputs = processor(images=image, return_tensors="pt") outputs = model(**inputs) # convert outputs (bounding boxes and class logits) to COCO API # let's only keep detections with score > 0.7 target_sizes = torch.tensor([image.size[::-1]]) results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0] for score, label, box in zip(results["scores"], results["labels"], results["boxes"]): box = [round(i, 2) for i in box.tolist()] print( f"Detected {model.config.id2label[label.item()]} with confidence " f"{round(score.item(), 3)} at location {box}" ) ``` This should output: ``` Detected cat with confidence 0.856 at location [342.19, 24.3, 640.02, 372.25] Detected remote with confidence 0.739 at location [40.79, 72.78, 176.76, 117.25] Detected cat with confidence 0.859 at location [16.5, 52.84, 318.25, 470.78] ``` Currently, both the feature extractor and model support PyTorch. ## Training data The Deformable DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively. ### BibTeX entry and citation info ```bibtex @misc{https://doi.org/10.48550/arxiv.2010.04159, doi = {10.48550/ARXIV.2010.04159}, url = {https://arxiv.org/abs/2010.04159}, author = {Zhu, Xizhou and Su, Weijie and Lu, Lewei and Li, Bin and Wang, Xiaogang and Dai, Jifeng}, keywords = {Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences}, title = {Deformable DETR: Deformable Transformers for End-to-End Object Detection}, publisher = {arXiv}, year = {2020}, copyright = {arXiv.org perpetual, non-exclusive license} } ```
itzzdeep/Mistral-7B-Instruct-v0.2-query-engine-v4-2-ckpt500-8-16-adapters	itzzdeep	2024-05-08T07:43:57Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-08T07:43:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
kknd22/RWKV6-vulkan	kknd22	2024-05-08T07:43:49Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-08T03:08:20Z	--- license: apache-2.0 ---
aaron-di/YamshadowExperiment28-7B-Linear	aaron-di	2024-05-08T07:42:18Z	6	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "merge", "mergekit", "lazymergekit", "automerger", "base_model:automerger/YamShadow-7B", "base_model:merge:automerger/YamShadow-7B", "base_model:yam-peleg/Experiment28-7B", "base_model:merge:yam-peleg/Experiment28-7B", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:33:32Z	--- license: apache-2.0 tags: - merge - mergekit - lazymergekit - automerger base_model: - automerger/YamShadow-7B - yam-peleg/Experiment28-7B --- ## 🧩 Configuration ```yaml models: - model: automerger/YamShadow-7B parameters: density: 0.5 weight: 0.5 - model: yam-peleg/Experiment28-7B parameters: density: 0.5 weight: 0.5 merge_method: linear base_model: automerger/YamShadow-7B dtype: float16 random_seed: 0 ``` ## 💻 Usage ```python !pip install -qU transformers accelerate from transformers import AutoTokenizer import transformers import torch model = "aaron-di/YamshadowExperiment28-7B-Linear" messages = [{"role": "user", "content": "What is a large language model?"}] tokenizer = AutoTokenizer.from_pretrained(model) prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) pipeline = transformers.pipeline( "text-generation", model=model, torch_dtype=torch.float16, device_map="auto", ) outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95) print(outputs[0]["generated_text"]) ```
iguanaYu/distilgpt2-finetuned-wikitext2	iguanaYu	2024-05-08T07:40:47Z	216	0	transformers	[ "transformers", "tensorboard", "safetensors", "gpt2", "text-generation", "generated_from_trainer", "base_model:distilbert/distilgpt2", "base_model:finetune:distilbert/distilgpt2", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T07:12:09Z	--- license: apache-2.0 base_model: distilgpt2 tags: - generated_from_trainer model-index: - name: distilgpt2-finetuned-wikitext2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilgpt2-finetuned-wikitext2 This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 3.6420 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-----:\|:----:\|:---------------:\| \| 3.7501 \| 1.0 \| 2334 \| 3.6669 \| \| 3.6498 \| 2.0 \| 4668 \| 3.6464 \| \| 3.6023 \| 3.0 \| 7002 \| 3.6420 \| ### Framework versions - Transformers 4.40.1 - Pytorch 2.2.1+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_2	ShenaoZ	2024-05-08T07:39:29Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_1", "base_model:finetune:ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T06:37:43Z	--- license: mit base_model: ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_1 tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.0001_sft_nodpo_3iters_bs256_5102lr_iter_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.0001_sft_nodpo_3iters_bs256_5102lr_iter_2 This model is a fine-tuned version of [ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_1](https://huggingface.co/ShenaoZ/0.0001_sft_nodpo_3iters_bs256_5102lr_iter_1) on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
four-two-labs/phi3-nord-10k	four-two-labs	2024-05-08T07:25:41Z	4	0	peft	[ "peft", "safetensors", "arxiv:1910.09700", "base_model:microsoft/Phi-3-mini-4k-instruct", "base_model:adapter:microsoft/Phi-3-mini-4k-instruct", "region:us" ]	null	2024-05-08T07:25:30Z	--- library_name: peft base_model: microsoft/Phi-3-mini-4k-instruct --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed] ### Framework versions - PEFT 0.10.0
imagepipeline/dyer	imagepipeline	2024-05-08T07:21:23Z	0	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-08T07:21:20Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## dyer <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - dyer [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/dyer?id=0c4dfd9b-8103-452c-94a4-bee84eca17fd/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "0c4dfd9b-8103-452c-94a4-bee84eca17fd", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
ZahraRahimiii/q-FrozenLake-v1-4x4-Slippery	ZahraRahimiii	2024-05-08T07:21:11Z	0	0	null	[ "FrozenLake-v1-4x4", "q-learning", "reinforcement-learning", "custom-implementation", "model-index", "region:us" ]	reinforcement-learning	2024-05-08T07:21:08Z	--- tags: - FrozenLake-v1-4x4 - q-learning - reinforcement-learning - custom-implementation model-index: - name: q-FrozenLake-v1-4x4-Slippery results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: FrozenLake-v1-4x4 type: FrozenLake-v1-4x4 metrics: - type: mean_reward value: 0.47 +/- 0.50 name: mean_reward verified: false --- # Q-Learning Agent playing1 FrozenLake-v1 This is a trained model of a Q-Learning agent playing FrozenLake-v1 . ## Usage ```python model = load_from_hub(repo_id="ZahraRahimiii/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl") # Don't forget to check if you need to add additional attributes (is_slippery=False etc) env = gym.make(model["env_id"]) ```
blockblockblock/llama-3-70B-Instruct-abliterated-bpw2.5-exl2	blockblockblock	2024-05-08T07:20:56Z	5	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "conversational", "license:llama3", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "exl2", "region:us" ]	text-generation	2024-05-08T07:15:50Z	--- license: llama3 license_name: llama3 license_link: LICENSE library_name: transformers --- # Llama-3-70B-Instruct-abliterated Model Card This is meta-llama/Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with the methodology that was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more. TL;DR: this model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal direction orthogonalized out. ## Quants [GGUF Quants available here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated-GGUF) ## For the people who like tinkering or looking to save bandwidth In the repo, I've included `refusal_dir.pth` If you have Llama-3-70B-Instruct model downloaded already, you can use the ortho cookbook to apply it to your downloaded model, which will make it the same as what you'd download from here. ## Quirkiness awareness notice This model may come with interesting quirks, as I obviously haven't extensively tested it, and the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. The code I used to generate it (and my published 'Kappa-3' model which is just Phi-3 with the same methodology applied) is available in a Python notebook in this repo. Specifically, the [ortho_cookbook.ipynb](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb). If you manage to develop further improvements, please share! This is really the most primitive way to use ablation, but there are other possibilities that I believe are as-yet unexplored.
Edgar404/donut	Edgar404	2024-05-08T07:16:09Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-08T07:16:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
imagepipeline/bundy	imagepipeline	2024-05-08T07:15:29Z	0	0	null	[ "imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic", "license:creativeml-openrail-m", "region:us" ]	text-to-image	2024-05-08T07:15:27Z	--- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ## bundy <img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;"> This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/) Model details - peggy [![Try this model](https://img.shields.io/badge/try_this_model-image_pipeline-BD9319)](https://imagepipeline.io/models/bundy?id=f2cf331b-c867-48fa-b16d-201dae1be42c/) ## How to try this model ? You can try using it locally or send an API call to test the output quality. Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required. Coding in `php` `javascript` `node` etc ? Checkout our documentation [![documentation](https://img.shields.io/badge/documentation-image_pipeline-blue)](https://docs.imagepipeline.io/docs/introduction) ```python import requests import json url = "https://imagepipeline.io/sd/text2image/v1/run" payload = json.dumps({ "model_id": "sd1.5", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": false, "guidance_scale": 7.5, "multi_lingual": "no", "embeddings": "", "lora_models": "f2cf331b-c867-48fa-b16d-201dae1be42c", "lora_weights": "0.5" }) headers = { 'Content-Type': 'application/json', 'API-Key': 'your_api_key' } response = requests.request("POST", url, headers=headers, data=payload) print(response.text) } ``` Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` : [![All models](https://img.shields.io/badge/Get%20All%20Models-image_pipeline-BD9319)](https://imagepipeline.io/models) ### API Reference #### Generate Image ```http https://api.imagepipeline.io/sd/text2image/v1 ``` \| Headers \| Type \| Description \| \|:----------------------\| :------- \|:-------------------------------------------------------------------------------------------------------------------\| \| `API-Key` \| `str` \| Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) \| \| `Content-Type` \| `str` \| application/json - content type of the request body \| \| Parameter \| Type \| Description \| \| :-------- \| :------- \| :------------------------- \| \| `model_id` \| `str` \| Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own\| \| `prompt` \| `str` \| Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips \| \| `num_inference_steps` \| `int [1-50]` \| Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) \| \| `guidance_scale` \| `float [1-20]` \| Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 \| \| `lora_models` \| `str, array` \| Pass the model_id(s) of LoRA models that can be found in models page \| \| `lora_weights` \| `str, array` \| Strength of the LoRA effect \| --- license: creativeml-openrail-m tags: - imagepipeline - imagepipeline.io - text-to-image - ultra-realistic pinned: false pipeline_tag: text-to-image --- ### Feedback If you have any feedback, please reach out to us at [email protected] #### 🔗 Visit Website [![portfolio](https://img.shields.io/badge/image_pipeline-BD9319?style=for-the-badge&logo=gocd&logoColor=white)](https://imagepipeline.io/) If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
Thimira/sinhala-llama-2-7b-chat-hf	Thimira	2024-05-08T07:11:57Z	131	3	peft	[ "peft", "pytorch", "tensorboard", "safetensors", "llama", "trl", "sft", "text-generation-inference", "text-generation", "si", "dataset:Thimira/sinhala-llm-dataset-llama-prompt-format", "base_model:NousResearch/Llama-2-7b-chat-hf", "base_model:adapter:NousResearch/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	text-generation	2024-04-01T04:59:40Z	--- library_name: peft tags: - trl - sft - text-generation-inference base_model: NousResearch/Llama-2-7b-chat-hf datasets: - Thimira/sinhala-llm-dataset-llama-prompt-format model-index: - name: sinhala-llama-2-7b-chat-hf results: [] license: llama2 language: - si pipeline_tag: text-generation --- <!-- 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. --> # sinhala-llama-2-7b-chat-hf This model is a fine-tuned version of [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf) on the [Thimira/sinhala-llm-dataset-llama-prompt-format](https://huggingface.co/datasets/Thimira/sinhala-llm-dataset-llama-prompt-format) dataset. ## Model description This is a model for Sinhala language text generation which is fine-tuned from the base llama-2-7b-chat-hf model. Currently the capabilities of themodel are extremely limited, and requires further data and fine-tuning to be useful. Feel free to experiment with the model and provide feedback. ### Usage example ```python from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline tokenizer = AutoTokenizer.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf") model = AutoModelForCausalLM.from_pretrained("Thimira/sinhala-llama-2-7b-chat-hf") pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200) prompt = "ඔබට සිංහල භාෂාව තේරුම් ගත හැකිද?" result = pipe(f"<s>[INST] {prompt} [/INST]") print(result[0]['generated_text']) ``` ## Intended uses & limitations The Sinhala-LLaMA models are intended for assistant-like chat in the Sinhala language. To get the expected features and performance from these models the LLaMA 2 prompt format needs to be followed, including the INST and <<SYS>> tags, BOS and EOS tokens, and the whitespaces and breaklines in between. ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 2 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: constant - lr_scheduler_warmup_ratio: 0.03 - num_epochs: 2 ### Training results ### Framework versions - PEFT 0.10.0 - Transformers 4.40.2 - Pytorch 2.1.0 - Datasets 2.19.1 - Tokenizers 0.19.1
ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_2	ShenaoZ	2024-05-08T07:11:13Z	5	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "alignment-handbook", "trl", "dpo", "generated_from_trainer", "conversational", "dataset:updated", "dataset:original", "base_model:ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_1", "base_model:finetune:ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_1", "license:mit", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-08T06:31:35Z	--- license: mit base_model: ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_1 tags: - alignment-handbook - trl - dpo - generated_from_trainer - trl - dpo - generated_from_trainer datasets: - updated - original model-index: - name: 0.0001_sft_nodpo_5iters_bs256_5102lr_iter_2 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # 0.0001_sft_nodpo_5iters_bs256_5102lr_iter_2 This model is a fine-tuned version of [ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_1](https://huggingface.co/ShenaoZ/0.0001_sft_nodpo_5iters_bs256_5102lr_iter_1) on the updated and the original datasets. ## 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-07 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 8 - gradient_accumulation_steps: 4 - total_train_batch_size: 256 - total_eval_batch_size: 64 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - Transformers 4.40.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.19.1
automated-finetunning/bart_full_data_10p_20e_tm2	automated-finetunning	2024-05-08T07:07:45Z	107	0	transformers	[ "transformers", "safetensors", "bart", "text2text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-05-08T04:25:32Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
knosing/japanese_ner_model	knosing	2024-05-08T07:06:22Z	191	0	transformers	[ "transformers", "pytorch", "safetensors", "bert", "token-classification", "ner", "named entity recognition", "stockmark ner", "japanese named entity recognition", "japanese ner", "ja", "en", "dataset:stockmark/ner-wikipedia-dataset", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T06:15:37Z	--- license: apache-2.0 datasets: - stockmark/ner-wikipedia-dataset language: - ja - en metrics: - f1 - recall - precision - accuracy library_name: transformers pipeline_tag: token-classification tags: - ner - named entity recognition - stockmark ner - bert - japanese named entity recognition - japanese ner - transformers --- ### Model Description This model is a fine-tuned version of the `tohoku-nlp/bert-base-japanese-v3`, specifically optimized for Named Entity Recognition (NER) tasks. It is fine-tuned using a Japanese named entity extraction dataset derived from Wikipedia, which was developed and made publicly available by Stockmark Inc. ([NER Wikipedia Dataset](https://github.com/stockmarkteam/ner-wikipedia-dataset)). ### Intended Use This model is intended for use in tasks that require the identification and categorization of named entities within Japanese text. It is suitable for various applications in natural language processing where understanding the specific names of people, organizations, locations, etc., is crucial. ### How to Use You can use this model for NER tasks with the following simple code snippet: ```python from transformers import AutoModelForTokenClassification, AutoTokenizer import torch model_name = "knosing/japanese_ner_model" tokenizer = AutoTokenizer.from_pretrained("tohoku-nlp/bert-base-japanese-v3") model = AutoModelForTokenClassification.from_pretrained(model_name) ``` ### Model Performance The model has been evaluated on various entity types to assess its precision, recall, F1 score, and overall accuracy. Below is the detailed performance breakdown by entity type: #### Overall Metrics - Overall Precision: 0.8379 - Overall Recall: 0.8477 - Overall F1 Score: 0.8428 - Overall Accuracy: 0.9684 #### Performance by Entity Type - Other Organization Names (`の他の組織名`): - Precision: 0.71875 - Recall: 0.69 - F1 Score: 0.7041 - Sample Count: 100 - Event Names (`ベント名`): - Precision: 0.85 - Recall: 0.8586 - F1 Score: 0.8543 - Sample Count: 99 - Personal Names (`人名`): - Precision: 0.8171 - Recall: 0.8664 - F1 Score: 0.8410 - Sample Count: 232 - Generic Names (`名`): - Precision: 0.8986 - Recall: 0.9376 - F1 Score: 0.9177 - Sample Count: 529 - Product Names (`品名`): - Precision: 0.6522 - Recall: 0.5906 - F1 Score: 0.6198 - Sample Count: 127 - Government Organization Names (`治的組織名`): - Precision: 0.9160 - Recall: 0.8276 - F1 Score: 0.8696 - Sample Count: 145 - Facility Names (`設名`): - Precision: 0.7905 - Recall: 0.8357 - F1 Score: 0.8125 - Sample Count: 140 ### Note You might not able to use the model with huggingface Inference API. The intended use for the model is given in the following repository: [KeshavSingh29/fa_ner_japanese](https://github.com/KeshavSingh29/fa_ner_japanese) If you have any questions, please feel free to contact me or raise an issue at the above repo.
shtapm/whisper-large_0502_encoder_all_400steps	shtapm	2024-05-08T07:01:57Z	149	0	transformers	[ "transformers", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-05-08T06:59:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
Boosad/Lisa	Boosad	2024-05-08T06:58:18Z	0	0	null	[ "license:apache-2.0", "region:us" ]	null	2024-05-08T06:58:18Z	--- license: apache-2.0 ---
Lakshit11/BERT-debit-15c-mcc-cleaned_10epoch	Lakshit11	2024-05-08T06:58:04Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T06:57:49Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
mradermacher/l3-badger-mushroom-4x8b-GGUF	mradermacher	2024-05-08T06:55:46Z	43	1	transformers	[ "transformers", "gguf", "llama-3", "en", "base_model:maldv/l3-badger-mushroom-4x8b", "base_model:quantized:maldv/l3-badger-mushroom-4x8b", "license:cc-by-nc-4.0", "endpoints_compatible", "region:us", "conversational" ]	null	2024-05-08T05:26:07Z	--- base_model: maldv/l3-badger-mushroom-4x8b language: - en library_name: transformers license: cc-by-nc-4.0 quantized_by: mradermacher tags: - llama-3 --- ## About <!-- ### quantize_version: 2 --> <!-- ### output_tensor_quantised: 1 --> <!-- ### convert_type: hfhfix --> <!-- ### vocab_type: --> static quants of https://huggingface.co/maldv/l3-badger-mushroom-4x8b <!-- provided-files --> weighted/imatrix quants are available at https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-i1-GGUF ## Usage If you are unsure how to use GGUF files, refer to one of [TheBloke's READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for more details, including on how to concatenate multi-part files. ## Provided Quants (sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants) \| Link \| Type \| Size/GB \| Notes \| \|:-----\|:-----\|--------:\|:------\| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q2_K.gguf) \| Q2_K \| 9.4 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.IQ3_XS.gguf) \| IQ3_XS \| 10.5 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q3_K_S.gguf) \| Q3_K_S \| 11.0 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.IQ3_S.gguf) \| IQ3_S \| 11.1 \| beats Q3_K* \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.IQ3_M.gguf) \| IQ3_M \| 11.2 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q3_K_M.gguf) \| Q3_K_M \| 12.2 \| lower quality \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q3_K_L.gguf) \| Q3_K_L \| 13.1 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.IQ4_XS.gguf) \| IQ4_XS \| 13.7 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q4_K_S.gguf) \| Q4_K_S \| 14.4 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q4_K_M.gguf) \| Q4_K_M \| 15.3 \| fast, recommended \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q5_K_S.gguf) \| Q5_K_S \| 17.3 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q5_K_M.gguf) \| Q5_K_M \| 17.8 \| \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q6_K.gguf) \| Q6_K \| 20.6 \| very good quality \| \| [GGUF](https://huggingface.co/mradermacher/l3-badger-mushroom-4x8b-GGUF/resolve/main/l3-badger-mushroom-4x8b.Q8_0.gguf) \| Q8_0 \| 26.6 \| fast, best quality \| Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better): ![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png) And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9 ## FAQ / Model Request See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized. ## Thanks I thank my company, [nethype GmbH](https://www.nethype.de/), for letting me use its servers and providing upgrades to my workstation to enable this work in my free time. <!-- end -->
hobee/CommentLM-7B	hobee	2024-05-08T06:49:45Z	2	1	transformers	[ "transformers", "pytorch", "internlm2", "feature-extraction", "custom_code", "license:other", "region:us" ]	feature-extraction	2024-05-08T03:50:50Z	--- license: other license_name: other license_link: LICENSE ---
DUAL-GPO/zephyr-7b-gpo-v8-i1	DUAL-GPO	2024-05-08T06:41:39Z	1	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/zephyr-7b-gpo-final-i0", "base_model:adapter:DUAL-GPO/zephyr-7b-gpo-final-i0", "license:mit", "region:us" ]	null	2024-05-07T20:59:21Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/zephyr-7b-gpo-final-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-gpo-v8-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gpo-v8-i1 This model is a fine-tuned version of [DUAL-GPO/zephyr-7b-gpo-final-i0](https://huggingface.co/DUAL-GPO/zephyr-7b-gpo-final-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 1 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
rj1ALINT/day-time	rj1ALINT	2024-05-08T06:24:58Z	29	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-08T06:23:52Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### day_time on Stable Diffusion via Dreambooth #### model by rj1ALINT This your the Stable Diffusion model fine-tuned the day_time concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: <dashcam footage > of a car driving at day time You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/rj1ALINT/day-time/resolve/main/concept_images/3.jpeg) ![image 1](https://huggingface.co/rj1ALINT/day-time/resolve/main/concept_images/0.jpeg) ![image 2](https://huggingface.co/rj1ALINT/day-time/resolve/main/concept_images/2.jpeg) ![image 3](https://huggingface.co/rj1ALINT/day-time/resolve/main/concept_images/4.jpeg) ![image 4](https://huggingface.co/rj1ALINT/day-time/resolve/main/concept_images/1.jpeg)
ritzfy/toy-part	ritzfy	2024-05-08T06:23:52Z	0	0	null	[ "en", "dataset:roneneldan/TinyStories", "license:mit", "region:us" ]	null	2024-05-08T06:01:37Z	--- license: mit datasets: - roneneldan/TinyStories language: - en --- This is a story generation model which generates upto 200 tokens when prompted with an initial part
lole25/zephyr-7b-gpo-v7-i1	lole25	2024-05-08T06:13:17Z	0	0	peft	[ "peft", "tensorboard", "safetensors", "mistral", "alignment-handbook", "generated_from_trainer", "trl", "dpo", "dataset:HuggingFaceH4/ultrafeedback_binarized", "base_model:DUAL-GPO/zephyr-7b-gpo-final-i0", "base_model:adapter:DUAL-GPO/zephyr-7b-gpo-final-i0", "license:mit", "region:us" ]	null	2024-05-07T20:59:05Z	--- license: mit library_name: peft tags: - alignment-handbook - generated_from_trainer - trl - dpo - generated_from_trainer base_model: DUAL-GPO/zephyr-7b-gpo-final-i0 datasets: - HuggingFaceH4/ultrafeedback_binarized model-index: - name: zephyr-7b-gpo-v7-i1 results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # zephyr-7b-gpo-v7-i1 This model is a fine-tuned version of [DUAL-GPO/zephyr-7b-gpo-final-i0](https://huggingface.co/DUAL-GPO/zephyr-7b-gpo-final-i0) on the HuggingFaceH4/ultrafeedback_binarized dataset. ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-06 - train_batch_size: 2 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - gradient_accumulation_steps: 2 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 0.88 ### Training results ### Framework versions - PEFT 0.7.1 - Transformers 4.36.2 - Pytorch 2.1.2+cu121 - Datasets 2.14.6 - Tokenizers 0.15.2
eunyounglee/EEVE-LLM2VEC-MNTP-STS-qa-1-adapter	eunyounglee	2024-05-08T06:05:21Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-08T06:05:18Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT	SicariusSicariiStuff	2024-05-08T06:05:17Z	7	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2023-12-25T19:06:09Z	--- language: - en license: apache-2.0 --- <div align="center"> <b style="font-size: 40px;">Tenebra_30B_Alpha01_FP16</b> </div> <img src="https://i.imgur.com/WkkCtZL.png" alt="Tenebră" style="width: 50%; min-width: 400px; display: block; margin: auto;"> # Model Details Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach. Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing. While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră! ## Tenebră is available at the following size and flavours: - 13B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_4BIT) \| [GPTQ_4-BIT_group-size-32](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_32g_4BIT) \| [GGUF-Many_Quants](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GGUF) - 30B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_4BIT) \| [GPTQ_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT) \| [EXL2_2.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-50bpw) \| [EXL2_2.8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-80bpw) \| [EXL2_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw) \| [EXL2_5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5bpw) \| [EXL2_5.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5-50bpw) \| [EXL2_6-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6bpw) \| [EXL2_6.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw) \| [EXL2_8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw) ### Support <img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;"> - [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻 - [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻 ## Disclaimer *This model is pretty uncensored, use responsibly ## Other stuff - [Experemental TTS extension for oobabooga](https://github.com/SicariusSicariiStuff/Diffusion_TTS) Based on Tortoise, EXTREMELY good quality, IF, and that's a big if, you can make it to work! - [Demonstration of the TTS capabilities](https://www.youtube.com/watch?v=V6ewxU6c1W8) Charsi narrates her story, Diablo2 (18+) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.18\| \|AI2 Reasoning Challenge (25-Shot)\|64.51\| \|HellaSwag (10-Shot) \|84.79\| \|MMLU (5-Shot) \|54.29\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|24.64\|
SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw	SicariusSicariiStuff	2024-05-08T06:04:47Z	11	0	transformers	[ "transformers", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2023-12-26T03:48:13Z	--- language: - en license: apache-2.0 --- <div align="center"> <b style="font-size: 40px;">Tenebra_30B_Alpha01_FP16</b> </div> <img src="https://i.imgur.com/WkkCtZL.png" alt="Tenebră" style="width: 50%; min-width: 400px; display: block; margin: auto;"> # Model Details Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach. Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing. While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră! ## Tenebră is available at the following size and flavours: - 13B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_4BIT) \| [GPTQ_4-BIT_group-size-32](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_32g_4BIT) \| [GGUF-Many_Quants](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GGUF) - 30B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_4BIT) \| [GPTQ_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT) \| [EXL2_2.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-50bpw) \| [EXL2_2.8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-80bpw) \| [EXL2_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw) \| [EXL2_5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5bpw) \| [EXL2_5.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5-50bpw) \| [EXL2_6-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6bpw) \| [EXL2_6.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw) \| [EXL2_8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw) ### Support <img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;"> - [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻 - [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻 ## Disclaimer *This model is pretty uncensored, use responsibly ## Other stuff - [Experemental TTS extension for oobabooga](https://github.com/SicariusSicariiStuff/Diffusion_TTS) Based on Tortoise, EXTREMELY good quality, IF, and that's a big if, you can make it to work! - [Demonstration of the TTS capabilities](https://www.youtube.com/watch?v=V6ewxU6c1W8) Charsi narrates her story, Diablo2 (18+) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.18\| \|AI2 Reasoning Challenge (25-Shot)\|64.51\| \|HellaSwag (10-Shot) \|84.79\| \|MMLU (5-Shot) \|54.29\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|24.64\|
SicariusSicariiStuff/Tenebra_30B_Alpha01_GGUF_Collab	SicariusSicariiStuff	2024-05-08T06:04:30Z	30	0	null	[ "gguf", "en", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2023-12-27T18:13:53Z	--- language: - en license: apache-2.0 --- <div align="center"> <b style="font-size: 40px;">Tenebra_30B_Alpha01_FP16</b> </div> <img src="https://i.imgur.com/WkkCtZL.png" alt="Tenebră" style="width: 50%; min-width: 400px; display: block; margin: auto;"> # Model Details Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach. Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing. While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră! ## Tenebră is available at the following size and flavours: - 13B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_4BIT) \| [GPTQ_4-BIT_group-size-32](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_32g_4BIT) \| [GGUF-Many_Quants](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GGUF) - 30B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_4BIT) \| [GPTQ_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT) \| [EXL2_2.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-50bpw) \| [EXL2_2.8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-80bpw) \| [EXL2_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw) \| [EXL2_5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5bpw) \| [EXL2_5.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5-50bpw) \| [EXL2_6-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6bpw) \| [EXL2_6.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw) \| [EXL2_8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw) ### Support <img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;"> - [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻 - [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻 ## Disclaimer *This model is pretty uncensored, use responsibly ## Other stuff - [Experemental TTS extension for oobabooga](https://github.com/SicariusSicariiStuff/Diffusion_TTS) Based on Tortoise, EXTREMELY good quality, IF, and that's a big if, you can make it to work! - [Demonstration of the TTS capabilities](https://www.youtube.com/watch?v=V6ewxU6c1W8) Charsi narrates her story, Diablo2 (18+) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.18\| \|AI2 Reasoning Challenge (25-Shot)\|64.51\| \|HellaSwag (10-Shot) \|84.79\| \|MMLU (5-Shot) \|54.29\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|24.64\|
olivernoah/OSTtoPSTAPP-Outlook-PST-password-recovery-software	olivernoah	2024-05-08T06:03:07Z	0	0	null	[ "region:us" ]	null	2024-05-08T06:02:03Z	Users can Recover Outlook PST Password with the help of OSTtoPSTAPP Outlook PST password recovery software. Any type of PST file password can be recovered with this program. Outlook PST Password Recovery Software is user-friendly, anyone can recover and reset Outlook PST password. Users can access the password for several PST files of Outlook with the software's advanced feature. Users don't have problems removing the password from any secret PST file and can load PST folders independently. The software can be used for recovering the password for any version of Microsoft Outlook. The software-supported PST file password for Outlook 2021, 2019, 2016, 2013, 2010, 2007, 2003, and others is processed. Both ANSI and Unicode PST files are supported by it properly. Even users can use any editions of Windows 11, 10, 8, 8.1, 7, XP, or Vista with this software. The program is free to download and use for Users. Read More:- https://www.osttopstapp.com/pst-password-recovery.html
AmirlyPhd/final_V2-bert-after-adding-new-words-text-classification-model	AmirlyPhd	2024-05-08T06:02:49Z	110	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "generated_from_trainer", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T06:02:29Z	--- tags: - generated_from_trainer metrics: - accuracy - f1 - precision - recall model-index: - name: final_V2-bert-after-adding-new-words-text-classification-model results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # final_V2-bert-after-adding-new-words-text-classification-model This model was trained from scratch on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.1494 - Accuracy: 0.9716 - F1: 0.8348 - Precision: 0.8317 - Recall: 0.8385 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 16 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Accuracy \| F1 \| Precision \| Recall \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:--------:\|:------:\|:---------:\|:------:\| \| 1.8136 \| 0.11 \| 50 \| 1.7501 \| 0.3470 \| 0.1733 \| 0.3034 \| 0.1944 \| \| 0.9023 \| 0.22 \| 100 \| 1.2121 \| 0.5723 \| 0.3083 \| 0.3496 \| 0.3189 \| \| 0.5924 \| 0.33 \| 150 \| 0.9662 \| 0.6667 \| 0.3919 \| 0.4265 \| 0.4037 \| \| 0.4218 \| 0.44 \| 200 \| 0.4848 \| 0.8813 \| 0.6427 \| 0.6492 \| 0.6413 \| \| 0.2025 \| 0.55 \| 250 \| 0.3807 \| 0.9021 \| 0.6677 \| 0.6538 \| 0.6829 \| \| 0.1609 \| 0.66 \| 300 \| 0.3360 \| 0.9147 \| 0.6763 \| 0.6727 \| 0.6822 \| \| 0.2035 \| 0.76 \| 350 \| 0.3705 \| 0.8991 \| 0.6711 \| 0.6589 \| 0.6838 \| \| 0.1208 \| 0.87 \| 400 \| 0.2140 \| 0.9565 \| 0.8218 \| 0.8137 \| 0.8323 \| \| 0.1313 \| 0.98 \| 450 \| 0.6818 \| 0.8704 \| 0.6779 \| 0.7179 \| 0.6859 \| \| 0.1576 \| 1.09 \| 500 \| 0.2508 \| 0.9212 \| 0.7443 \| 0.7888 \| 0.7311 \| \| 0.0593 \| 1.2 \| 550 \| 0.2091 \| 0.9552 \| 0.8193 \| 0.8179 \| 0.8227 \| \| 0.0705 \| 1.31 \| 600 \| 0.2010 \| 0.9552 \| 0.8154 \| 0.8091 \| 0.8225 \| \| 0.0637 \| 1.42 \| 650 \| 0.1985 \| 0.9573 \| 0.8187 \| 0.8115 \| 0.8275 \| \| 0.0619 \| 1.53 \| 700 \| 0.2306 \| 0.9541 \| 0.8241 \| 0.8194 \| 0.8301 \| \| 0.0582 \| 1.64 \| 750 \| 0.2001 \| 0.9609 \| 0.8280 \| 0.8250 \| 0.8320 \| \| 0.1132 \| 1.75 \| 800 \| 0.1439 \| 0.9680 \| 0.8324 \| 0.8284 \| 0.8367 \| \| 0.0416 \| 1.86 \| 850 \| 0.1558 \| 0.9680 \| 0.8333 \| 0.8301 \| 0.8369 \| \| 0.0371 \| 1.97 \| 900 \| 0.2242 \| 0.9595 \| 0.8280 \| 0.8235 \| 0.8345 \| \| 0.0428 \| 2.07 \| 950 \| 0.1907 \| 0.9617 \| 0.8303 \| 0.8262 \| 0.8356 \| \| 0.0388 \| 2.18 \| 1000 \| 0.1784 \| 0.9658 \| 0.8319 \| 0.8266 \| 0.8383 \| \| 0.0335 \| 2.29 \| 1050 \| 0.1735 \| 0.9675 \| 0.8323 \| 0.8266 \| 0.8390 \| \| 0.0361 \| 2.4 \| 1100 \| 0.1921 \| 0.9636 \| 0.8283 \| 0.8219 \| 0.8360 \| \| 0.0126 \| 2.51 \| 1150 \| 0.2200 \| 0.9614 \| 0.8294 \| 0.8274 \| 0.8327 \| \| 0.003 \| 2.62 \| 1200 \| 0.2251 \| 0.9614 \| 0.8296 \| 0.8262 \| 0.8346 \| \| 0.0029 \| 2.73 \| 1250 \| 0.1750 \| 0.9694 \| 0.8348 \| 0.8314 \| 0.8388 \| \| 0.0137 \| 2.84 \| 1300 \| 0.1775 \| 0.9686 \| 0.8345 \| 0.8300 \| 0.8397 \| \| 0.0184 \| 2.95 \| 1350 \| 0.1860 \| 0.9675 \| 0.8337 \| 0.8293 \| 0.8391 \| ### Framework versions - Transformers 4.39.3 - Pytorch 2.1.2 - Datasets 2.18.0 - Tokenizers 0.15.2
SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw	SicariusSicariiStuff	2024-05-08T05:57:30Z	5	0	transformers	[ "transformers", "llama", "text-generation", "en", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-04-28T09:06:56Z	--- language: - en license: apache-2.0 --- <div align="center"> <b style="font-size: 40px;">Tenebra_30B_Alpha01_FP16</b> </div> <img src="https://i.imgur.com/WkkCtZL.png" alt="Tenebră" style="width: 50%; min-width: 400px; display: block; margin: auto;"> # Model Details Tenebră, a various sized experimental AI model, stands at the crossroads of self-awareness and unconventional datasets. Its existence embodies a foray into uncharted territories, steering away from conventional norms in favor of a more obscure and experimental approach. Noteworthy for its inclination towards the darker and more philosophical aspects of conversation, Tenebră's proficiency lies in unraveling complex discussions across a myriad of topics. Drawing from a pool of unconventional datasets, this model ventures into unexplored realms of thought, offering users an experience that is as unconventional as it is intellectually intriguing. While Tenebră maintains a self-aware facade, its true allure lies in its ability to engage in profound discussions without succumbing to pretense. Step into the realm of Tenebră! ## Tenebră is available at the following size and flavours: - 13B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_4BIT) \| [GPTQ_4-BIT_group-size-32](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GPTQ_32g_4BIT) \| [GGUF-Many_Quants](https://huggingface.co/SicariusSicariiStuff/Tinybra_13B_GGUF) - 30B: [FP16](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_FP16) \| [GPTQ_4-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_4BIT) \| [GPTQ_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_3BIT) \| [EXL2_2.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-50bpw) \| [EXL2_2.8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_2-80bpw) \| [EXL2_3-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_3bpw) \| [EXL2_5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5bpw) \| [EXL2_5.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_5-50bpw) \| [EXL2_6-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6bpw) \| [EXL2_6.5-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_6-50bpw) \| [EXL2_8-BIT](https://huggingface.co/SicariusSicariiStuff/Tenebra_30B_Alpha01_EXL2_8bpw) ### Support <img src="https://i.imgur.com/0lHHN95.png" alt="GPUs too expensive" style="width: 10%; min-width: 100px; display: block; margin: left;"> - [My Ko-fi page](https://ko-fi.com/sicarius) ALL donations will go for research resources and compute, every bit counts 🙏🏻 - [My Patreon](https://patreon.com/TenebraAI) ALL donations will go for research resources and compute, every bit counts 🙏🏻 ## Disclaimer *This model is pretty uncensored, use responsibly ## Other stuff - [Experemental TTS extension for oobabooga](https://github.com/SicariusSicariiStuff/Diffusion_TTS) Based on Tortoise, EXTREMELY good quality, IF, and that's a big if, you can make it to work! - [Demonstration of the TTS capabilities](https://www.youtube.com/watch?v=V6ewxU6c1W8) Charsi narrates her story, Diablo2 (18+) # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard) Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_SicariusSicariiStuff__Tenebra_30B_Alpha01_FP16) \| Metric \|Value\| \|---------------------------------\|----:\| \|Avg. \|60.18\| \|AI2 Reasoning Challenge (25-Shot)\|64.51\| \|HellaSwag (10-Shot) \|84.79\| \|MMLU (5-Shot) \|54.29\| \|TruthfulQA (0-shot) \|54.22\| \|Winogrande (5-shot) \|78.61\| \|GSM8k (5-shot) \|24.64\|
kevinkawchak/gradientai-Llama-3-8B-Instruct-Gradient-1048k-Molecule16	kevinkawchak	2024-05-08T05:55:15Z	9	0	transformers	[ "transformers", "safetensors", "llama", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "dataset:zjunlp/Mol-Instructions", "base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k", "base_model:finetune:gradientai/Llama-3-8B-Instruct-Gradient-1048k", "license:llama3", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-05-06T05:42:11Z	--- language: - en license: llama3 tags: - text-generation-inference - transformers - unsloth - llama - trl base_model: gradientai/Llama-3-8B-Instruct-Gradient-1048k datasets: - zjunlp/Mol-Instructions --- - Developed by: kevinkawchak - License: llama3 - Finetuned from model : gradientai/Llama-3-8B-Instruct-Gradient-1048k - Finetuned using dataset : zjunlp/Mol-Instructions, cc-by-4.0 - Dataset identification: Molecule-oriented Instructions - Dataset function: Description guided molecule design ## May 07, 2024: Additional Fine-tunings, Built with Meta Llama 3 <br> 1) gradientai/Llama-3-8B-Instruct-Gradient-1048k [Model](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) <br> Llama 3 8B update: 1040K context length from 8K, and highest RAM consumption<br> "What is the structure for adenine?" Verbose SELFIES structure, but logical<br> [Fine-tuned](https://huggingface.co/kevinkawchak/gradientai-Llama-3-8B-Instruct-Gradient-1048k-Molecule16) on Mol-Instructions, float16, [GitHub](https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/Llama-3-8B-Instruct-Gradient-1048k-Molecule.ipynb), 610 seconds, A100 40GB <br> 2) NousResearch/Hermes-2-Pro-Llama-3-8B [Model](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)<br> Llama 3 8B update: Cleaned OpenHermes 2.5, new Function Calling, JSON Mode dataset<br> "What is the structure for adenine?" Concise SELFIES structure, but less logical <br> [Fine-tuned](https://huggingface.co/kevinkawchak/NousResearch-Hermes-2-Pro-Llama-3-8B-Molecule16) on Mol-Instructions, float16, [GitHub](https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/Hermes-2-Pro-Llama-3-8B-Molecule.ipynb), 599 seconds, A100 40GB <br> 3) nvidia/Llama3-ChatQA-1.5-8B [Model](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)<br> Llama 3 8B update: ChatQA-1.5 to enhance tabular and arithmetic calculation capability<br> "What is the structure for adenine?" Verbose SELFIES structure and less logical <br> [Fine-tuned](https://huggingface.co/kevinkawchak/nvidia-Llama3-ChatQA-1.5-8B-Molecule16) on Mol-Instructions, float16, [GitHub](https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/Llama3-ChatQA-1.5-8B-Molecule.ipynb), 599 seconds, A100 40GB <br> Responses were verified against the Wikipedia [Adenine](https://en.wikipedia.org/wiki/Adenine) SMILES format and a SMILES to SELFIES python notebook estimated [generator](https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/SMILES%20to%20SELFIES%20estimator.ipynb). <br> Fine-tunings were performed using the Apache-2.0 unsloth 'Alpaca + Llama-3 8b full example' Colab [notebook](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing). ## Primary Study The following are modifications or improvements to original notebooks. Please refer to the authors' models for the published primary work. [Cover Image](https://drive.google.com/file/d/1J-spZMzLlPxkqfMrPxvtMZiD2_hfcGyr/view?usp=sharing). [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](https://llama.meta.com/llama3/license/). Built with Meta Llama 3. <br> A 4-bit quantization of Meta-Llama-3-8B-Instruct was used to reduce training memory requirements when fine-tuning on the zjunlp/Mol-Instructions dataset. (1-2) In addition, the minimum LoRA rank value was utilized to reduce the overall size of created models. In specific, the molecule-oriented instructions description guided molecule design was implemented to answer general questions and general biochemistry questions. General questions were answered with high accuracy, while biochemistry related questions returned 'SELFIES' structures but with limited accuracy. The notebook featured Torch and Hugging Face libraries using the Unsloth llama-3-8b-Instruct-bnb-4bit quantization model. Training loss decreased steadily from 1.97 to 0.73 over 60 steps. Additional testing regarding the appropriate level of compression or hyperparameter adjustments for accurate SELFIES chemical structures outputs is relevant, as shown in the GitHub notebook for research purposes (3). A 16-bit and reduced 4-bit size were uploaded to Hugging Face. (4-5) Update 04/24: The number of training steps were increased to further decrease loss, while maintaining reduced memory requirements through quantization and reduced size through LoRA. This allowed for significantly improved responses to biochemistry related questions, and were saved at the following LLM Model sizes: [8.03B](https://huggingface.co/kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule16), [4.65B](https://huggingface.co/kevinkawchak/Meta-Llama-3-8B-Instruct-Molecule04). [github](https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/Meta-Llama-3-8B-Instruct-Molecule.ipynb). References: 1) unsloth: https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit 2) zjunlp: https://huggingface.co/datasets/zjunlp/Mol-Instructions 3) github: https://github.com/kevinkawchak/Medical-Quantum-Machine-Learning/blob/main/Code/Drug%20Discovery/Meta-Llama-3/Meta-Llama-3-8B-Instruct-Mol.ipynb 4) hugging face: https://huggingface.co/kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-Mol16 5) hugging face: https://huggingface.co/kevinkawchak/Meta-Llama-3-8B-Instruct-LoRA-Mol04 @inproceedings{fang2023mol, <br> author = {Yin Fang and<br> Xiaozhuan Liang and<br> Ningyu Zhang and<br> Kangwei Liu and<br> Rui Huang and<br> Zhuo Chen and<br> Xiaohui Fan and<br> Huajun Chen},<br> title = {Mol-Instructions: {A} Large-Scale Biomolecular Instruction Dataset<br> for Large Language Models},<br> booktitle = {{ICLR}},<br> publisher = {OpenReview.net},<br> year = {2024},<br> url = {https://openreview.net/pdf?id=Tlsdsb6l9n}}<br> This llama model was trained 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)
yaolily/llava-v1.5-7b-lora-reproduce	yaolily	2024-05-08T05:52:45Z	0	0	peft	[ "peft", "llava", "region:us" ]	null	2024-05-08T05:52:33Z	--- library_name: peft --- ## Training procedure ### Framework versions - PEFT 0.4.0
obamaTeo/mistral-finetune-16bit-ver9-main-GPTQ	obamaTeo	2024-05-08T05:49:44Z	79	0	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "conversational", "arxiv:1910.09700", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "4-bit", "gptq", "region:us" ]	text-generation	2024-05-08T05:09:54Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
deadcode99/mistral-7b-32k-billm-finetuned-token-classification-segmentwise	deadcode99	2024-05-08T05:48:59Z	0	0	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:mistralai/Mistral-7B-Instruct-v0.2", "base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2", "license:apache-2.0", "region:us" ]	null	2024-05-07T17:32:31Z	--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: mistralai/Mistral-7B-Instruct-v0.2 metrics: - precision - recall - f1 - accuracy model-index: - name: mistral-7b-32k-billm-finetuned-token-classification-segmentwise results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # mistral-7b-32k-billm-finetuned-token-classification-segmentwise This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.4998 - Precision: 0.0 - Recall: 0.0 - F1: 0.0 - Accuracy: 0.7829 ## 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: 0.001 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 32 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 10 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:------:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 0.9784 \| 34 \| 0.9557 \| 0.0 \| 0.0 \| 0.0 \| 0.7596 \| \| No log \| 1.9856 \| 69 \| 0.7691 \| 0.0 \| 0.0 \| 0.0 \| 0.7707 \| \| No log \| 2.9928 \| 104 \| 0.7086 \| 0.0 \| 0.0 \| 0.0 \| 0.7794 \| \| No log \| 4.0 \| 139 \| 0.5693 \| 0.0 \| 0.0 \| 0.0 \| 0.7697 \| \| No log \| 4.9784 \| 173 \| 0.5449 \| 0.0 \| 0.0 \| 0.0 \| 0.7758 \| \| No log \| 5.9856 \| 208 \| 0.5168 \| 0.0 \| 0.0 \| 0.0 \| 0.7805 \| \| No log \| 6.9928 \| 243 \| 0.5379 \| 0.0 \| 0.0 \| 0.0 \| 0.7838 \| \| No log \| 8.0 \| 278 \| 0.5301 \| 0.0 \| 0.0 \| 0.0 \| 0.7847 \| \| No log \| 8.9784 \| 312 \| 0.5007 \| 0.0 \| 0.0 \| 0.0 \| 0.7829 \| \| No log \| 9.7842 \| 340 \| 0.4998 \| 0.0 \| 0.0 \| 0.0 \| 0.7829 \| ### Framework versions - PEFT 0.10.0 - Transformers 4.40.1 - Pytorch 2.2.2 - Datasets 2.18.0 - Tokenizers 0.19.1
minz27/ppo-LunarLander-v2	minz27	2024-05-08T05:48:49Z	0	0	stable-baselines3	[ "stable-baselines3", "LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "model-index", "region:us" ]	reinforcement-learning	2024-05-08T05:48:32Z	--- 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: 259.50 +/- 21.47 name: mean_reward verified: false --- # PPO Agent playing LunarLander-v2 This is a trained model of a PPO agent playing LunarLander-v2 using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3). ## Usage (with Stable-baselines3) TODO: Add your code ```python from stable_baselines3 import ... from huggingface_sb3 import load_from_hub ... ```
rj1ALINT/nighttime	rj1ALINT	2024-05-08T05:47:16Z	30	0	diffusers	[ "diffusers", "safetensors", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-05-08T05:46:11Z	--- license: creativeml-openrail-m tags: - text-to-image --- ### nighttime on Stable Diffusion via Dreambooth #### model by rj1ALINT This your the Stable Diffusion model fine-tuned the nighttime concept taught to Stable Diffusion with Dreambooth. It can be used by modifying the `instance_prompt`: <dashcam footage > of a car driving at night time You can also train your own concepts and upload them to the library by using [this notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). And you can run your new concept via `diffusers`: [Colab Notebook for Inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_inference.ipynb), [Spaces with the Public Concepts loaded](https://huggingface.co/spaces/sd-dreambooth-library/stable-diffusion-dreambooth-concepts) Here are the images used for training this concept: ![image 0](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/5.jpeg) ![image 1](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/3.jpeg) ![image 2](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/0.jpeg) ![image 3](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/2.jpeg) ![image 4](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/4.jpeg) ![image 5](https://huggingface.co/rj1ALINT/nighttime/resolve/main/concept_images/1.jpeg)
imi2/llama-3-105B-Instruct-abliterated-merged	imi2	2024-05-08T05:43:15Z	9	2	transformers	[ "transformers", "safetensors", "llama", "text-generation", "mergekit", "merge", "conversational", "autotrain_compatible", "text-generation-inference", "endpoints_compatible", "region:us" ]	text-generation	2024-05-07T15:45:34Z	--- base_model: [] library_name: transformers tags: - mergekit - merge --- # 105B 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: * ../../Storage/failspy_llama-3-70B-Instruct-abliterated ### Configuration The following YAML configuration was used to produce this model: ```yaml dtype: float16 merge_method: passthrough slices: - sources: - layer_range: [0, 40] model: ../../Storage/failspy_llama-3-70B-Instruct-abliterated - sources: - layer_range: [20, 60] model: ../../Storage/failspy_llama-3-70B-Instruct-abliterated - sources: - layer_range: [40, 80] model: ../../Storage/failspy_llama-3-70B-Instruct-abliterated ```
Manoj21k/llama3-8b-finetuned-entity-extraction-sql	Manoj21k	2024-05-08T05:40:05Z	0	0	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-05-08T05:40:00Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [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]
Yntec/OG	Yntec	2024-05-08T05:31:06Z	315	4	diffusers	[ "diffusers", "safetensors", "General", "Eldreths", "stable-diffusion", "stable-diffusion-diffusers", "text-to-image", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2023-11-06T03:04:05Z	--- license: creativeml-openrail-m library_name: diffusers pipeline_tag: text-to-image tags: - General - Eldreths - stable-diffusion - stable-diffusion-diffusers - diffusers - text-to-image --- # Elldreth's OG 4060 Mix Safetensors version of this model with the MoistMixV2 VAE baked in. Sample and prompt: ![Sample](https://cdn-uploads.huggingface.co/production/uploads/63239b8370edc53f51cd5d42/sJKkxXHEYoBjcf_2bybRN.png) fine details portrait of joyful cute girl, aliens vivid, nature trees, meadows at night, bokeh, close-up, anime masterpiece by studio ghibli. 8k, sharp high quality classic anime from 1990 in style of kyoani Original page: https://huggingface.co/danbrown/elldreth-og-mix
BogdanTurbal/bert-d_3_e_3_t_u_r_0-d_2_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:28:22Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:28:09Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_3_e_3_t_u_r_0-d_0_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:53Z	165	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:27:37Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_2_e_3_t_u_r_0-d_1_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:31Z	185	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:14:36Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_2_e_3_t_u_r_0-d_0_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:27Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:14:21Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_2_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:24Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:14:07Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
BogdanTurbal/bert-d_1_e_3_t_u_r_0-d_3_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:20Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:13:53Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_1_e_3_t_u_r_0-d_2_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:18Z	165	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:13:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_0_e_3_t_u_r_0-d_2_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:06Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:12:39Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_0_e_3_t_u_r_0-d_1_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:27:00Z	183	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:12:25Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
BogdanTurbal/bert-d_0_e_3_t_u_r_0_v1	BogdanTurbal	2024-05-08T05:26:57Z	165	0	transformers	[ "transformers", "safetensors", "bert", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-05-08T05:12:05Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 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]
fktime/NuNER-multilingual-v0.1-ai4p	fktime	2024-05-08T05:23:16Z	107	0	transformers	[ "transformers", "safetensors", "bert", "token-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-05-08T05:16:18Z	Overall Metrics: Overall Precision: 85.48% Overall Recall: 89.07% Overall F1 Score: 87.24% Overall Accuracy: 96.05% High-Performing Entities: ACCOUNTNAME: F1 score of 98.85% ACCOUNTNUMBER: F1 score of 94.71% AGE: F1 score of 97.25% EMAIL: F1 score of 99.18% ETHEREUMADDRESS: F1 score of 98.05% NEARBYGPSCOORDINATE: F1 score of 99.55% PHONEIMEI: F1 score of 98.40% PHONENUMBER: F1 score of 97.17% Entities That Need Improvement: IP: F1 score of 0.0% (no samples predicted) LITECOINADDRESS: F1 score of 0.0% MASKEDNUMBER: F1 score of 9.98% Numeric Entities: Entities like AGE and PHONEIMEI fall under this category! Legal Entities: COMPANYNAME: F1 score of 95.99% JOBTITLE: F1 score of 97.11% STATE: F1 score of 93.24%

Subsets and Splits