librarian-bots/model_cards_with_metadata_test

modelId stringlengths 5 122	author stringlengths 2 42	last_modified timestamp[us, tz=UTC]	library_name stringclasses 245 values	tags sequencelengths 1 4.05k	pipeline_tag stringclasses 48 values	createdAt timestamp[us, tz=UTC]	card stringlengths 1 901k
Arovincy/CustomizedTextGeneration	Arovincy	2024-06-30T17:57:45Z	null	[ "region:us" ]	null	2024-06-30T17:57:15Z	Entry not found
LaLaf93/inproceedings_recognizer	LaLaf93	2024-06-30T18:07:35Z	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T18:00:11Z	Entry not found
TomEijkelenkamp/renaissance-cogvlm-composition	TomEijkelenkamp	2024-06-30T18:00:51Z	null	[ "region:us" ]	null	2024-06-30T18:00:51Z	Entry not found
6pu8wtw6/UncensoredPonyXL	6pu8wtw6	2024-06-30T18:00:52Z	null	[ "license:unknown", "region:us" ]	null	2024-06-30T18:00:52Z	--- license: unknown ---
apwic/summarization-base-0	apwic	2024-06-30T23:41:14Z	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "id", "base_model:LazarusNLP/IndoNanoT5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-06-30T18:04:33Z	--- language: - id license: apache-2.0 base_model: LazarusNLP/IndoNanoT5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization-base-0 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. --> # summarization-base-0 This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5082 - Rouge1: 0.3572 - Rouge2: 0.0 - Rougel: 0.3545 - Rougelsum: 0.3557 - Gen Len: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:-----:\|:-----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\| \| 0.6282 \| 1.0 \| 3566 \| 0.4719 \| 0.43 \| 0.0 \| 0.4255 \| 0.4282 \| 1.0 \| \| 0.4301 \| 2.0 \| 7132 \| 0.4728 \| 0.3754 \| 0.0 \| 0.3711 \| 0.3719 \| 1.0 \| \| 0.3336 \| 3.0 \| 10698 \| 0.4632 \| 0.3806 \| 0.0 \| 0.3777 \| 0.3808 \| 1.0 \| \| 0.2643 \| 4.0 \| 14264 \| 0.4921 \| 0.3537 \| 0.0 \| 0.3512 \| 0.3514 \| 1.0 \| \| 0.2174 \| 5.0 \| 17830 \| 0.5082 \| 0.3572 \| 0.0 \| 0.3545 \| 0.3557 \| 1.0 \| ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1
LaLaf93/incollection_recognizer	LaLaf93	2024-06-30T18:14:29Z	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T18:07:40Z	Entry not found
CarlosPov/Llama-2-7b-chat-hf-finetune_90_10_MIX	CarlosPov	2024-06-30T18:09:22Z	peft	[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]	null	2024-06-30T18:08:23Z	--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: Llama-2-7b-chat-hf-finetune_90_10_MIX 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. --> # Llama-2-7b-chat-hf-finetune_90_10_MIX This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: reduce_lr_on_plateau - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:-------:\|:-----:\|:---------------:\| \| 0.6795 \| 0.9968 \| 316 \| 0.7737 \| \| 0.2756 \| 1.9937 \| 632 \| 0.8534 \| \| 0.166 \| 2.9905 \| 948 \| 0.9507 \| \| 0.1135 \| 3.9874 \| 1264 \| 1.0163 \| \| 0.086 \| 4.9842 \| 1580 \| 1.0497 \| \| 0.0788 \| 5.9811 \| 1896 \| 1.0818 \| \| 0.1423 \| 6.9779 \| 2212 \| 1.1176 \| \| 0.0778 \| 7.9748 \| 2528 \| 1.1538 \| \| 0.0792 \| 8.9716 \| 2844 \| 1.1963 \| \| 0.0657 \| 9.9685 \| 3160 \| 1.1900 \| \| 0.0639 \| 10.9653 \| 3476 \| 1.2259 \| \| 0.0681 \| 11.9621 \| 3792 \| 1.2195 \| \| 0.0522 \| 12.9590 \| 4108 \| 1.2163 \| \| 0.0492 \| 13.9558 \| 4424 \| 1.2259 \| \| 0.048 \| 14.9527 \| 4740 \| 1.2378 \| \| 0.0441 \| 15.9495 \| 5056 \| 1.2492 \| \| 0.0629 \| 16.9464 \| 5372 \| 1.2564 \| \| 0.0622 \| 17.9432 \| 5688 \| 1.2606 \| \| 0.0589 \| 18.9401 \| 6004 \| 1.2662 \| \| 0.0592 \| 19.9369 \| 6320 \| 1.2712 \| \| 0.0586 \| 20.9338 \| 6636 \| 1.2780 \| \| 0.0594 \| 21.9306 \| 6952 \| 1.2807 \| \| 0.0616 \| 22.9274 \| 7268 \| 1.2874 \| \| 0.0554 \| 23.9243 \| 7584 \| 1.2904 \| \| 0.0562 \| 24.9211 \| 7900 \| 1.2934 \| \| 0.0543 \| 25.9180 \| 8216 \| 1.2961 \| \| 0.0553 \| 26.9148 \| 8532 \| 1.2986 \| \| 0.0547 \| 27.9117 \| 8848 \| 1.3009 \| \| 0.0543 \| 28.9085 \| 9164 \| 1.3025 \| \| 0.0535 \| 29.9054 \| 9480 \| 1.3040 \| \| 0.0535 \| 30.9022 \| 9796 \| 1.3053 \| \| 0.0533 \| 31.8991 \| 10112 \| 1.3068 \| \| 0.053 \| 32.8959 \| 10428 \| 1.3078 \| \| 0.0528 \| 33.8927 \| 10744 \| 1.3096 \| \| 0.0526 \| 34.8896 \| 11060 \| 1.3098 \| \| 0.0523 \| 35.8864 \| 11376 \| 1.3100 \| \| 0.052 \| 36.8833 \| 11692 \| 1.3102 \| \| 0.0516 \| 37.8801 \| 12008 \| 1.3104 \| \| 0.0513 \| 38.8770 \| 12324 \| 1.3105 \| \| 0.0504 \| 39.8738 \| 12640 \| 1.3107 \| \| 0.0508 \| 40.8707 \| 12956 \| 1.3109 \| \| 0.0503 \| 41.8675 \| 13272 \| 1.3111 \| \| 0.0501 \| 42.8644 \| 13588 \| 1.3114 \| \| 0.0502 \| 43.8612 \| 13904 \| 1.3116 \| \| 0.05 \| 44.8580 \| 14220 \| 1.3118 \| \| 0.0498 \| 45.8549 \| 14536 \| 1.3118 \| \| 0.0517 \| 46.8517 \| 14852 \| 1.3118 \| \| 0.0496 \| 47.8486 \| 15168 \| 1.3118 \| \| 0.0486 \| 48.8454 \| 15484 \| 1.3118 \| \| 0.0475 \| 49.8423 \| 15800 \| 1.3119 \| ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
daviddextre/ModelsPonyXL2	daviddextre	2024-06-30T19:12:43Z	null	[ "region:us" ]	null	2024-06-30T18:08:26Z	Entry not found
csteinmetz1/afx-rep	csteinmetz1	2024-06-30T19:13:34Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T18:12:55Z	--- license: apache-2.0 ---
noobilal/LLaMA3-Steve-Jobs	noobilal	2024-06-30T18:14:08Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-30T18:13:54Z	--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: noobilal - 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)
LaLaf93/phdthesis_recognizer	LaLaf93	2024-06-30T18:21:41Z	transformers	[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T18:14:34Z	Entry not found
shantanudave/BERTopic_vjuly	shantanudave	2024-06-30T18:18:02Z	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-06-30T18:18:01Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_vjuly 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("shantanudave/BERTopic_vjuly") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| 0 \| payment - pay - card - bank - money \| 742 \| Payment Issues Detection \| \| 1 \| load - slow - search - article - doesnt \| 705 \| Slow Search Function \| \| 2 \| clothes - clothing - size - fashion - large size \| 683 \| Large Size Quality Clothing \| \| 3 \| bon - - - - \| 668 \| bon documents collection \| \| 4 \| clear - intuitive - clear easy - recommend - selection \| 665 \| Easy Clear Navigation \| \| 5 \| - - - - \| 649 \| Keyword-Driven Document Analysis \| \| 6 \| shopping - staff - friendly - store - satisfy \| 578 \| Friendly staff satisfaction \| \| 7 \| delivery - fast delivery - fast - shipping - ship \| 563 \| Fast Delivery Quality \| \| 8 \| cart - shop cart - log - password - add \| 548 \| Shopping Cart Issues \| \| 9 \| easy use - easy - use - use easy - quick easy \| 531 \| Quick & Easy Solutions \| \| 10 \| awesome - excellent - think - clearly - phenomenal \| 462 \| Really Phenomenal Clear Thinking \| \| 11 \| quality - price - quality quality - price quality - comfortable \| 454 \| Excellent Quality Price \| \| 12 \| work work - work - work quickly - flawlessly - work flawlessly \| 390 \| Efficient Flawless Work \| \| 13 \| super super - super - superb - superb super - super friendly \| 349 \| Superb Friendly Coat \| \| 14 \| really simple - ra - solve problem - control - satisfied easy \| 145 \| User-Friendly Problem Solver \| \| 15 \| clear clear - clear - fast clear - clear fast - super clear \| 144 \| Clear and Transparent Working \| \| 16 \| discover - stuff good - stuff - fact - clearly \| 129 \| Discovering Interesting Facts \| \| 17 \| satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied \| 121 \| Utmost Satisfaction \| </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13
adnanshirik/astroclip	adnanshirik	2024-06-30T21:59:15Z	null	[ "arxiv:2310.03024", "region:us" ]	null	2024-06-30T18:18:42Z	PyTorch Lightning model checkpoints for all models created in reproduction of [AstroCLIP: A Cross-Modal Foundation Model for Galaxies](https://arxiv.org/abs/2310.03024). The reproduction is part of an assessed project and is currently private, if you are an assessor and require access to these saved model weights, please request access. There are 7 model checkpoints, one for each embedding dimensionality in [8, 16, 32, 64, 128, 256, 512]. --- license: mit ---
ANDRIOIDEA/ANDRIOIDE	ANDRIOIDEA	2024-06-30T18:19:04Z	null	[ "region:us" ]	null	2024-06-30T18:19:04Z	Entry not found
Nithusikan01/fine-tuned-llama-3-8B-customer-support	Nithusikan01	2024-06-30T18:19:20Z	null	[ "region:us" ]	null	2024-06-30T18:19:20Z	Entry not found
net31/naschainv148	net31	2024-07-01T09:11:12Z	null	[ "region:us" ]	null	2024-06-30T18:20:32Z	Entry not found
habulaj/4532236697	habulaj	2024-06-30T18:21:17Z	null	[ "region:us" ]	null	2024-06-30T18:21:10Z	Entry not found
habulaj/12116496255	habulaj	2024-06-30T18:24:53Z	null	[ "region:us" ]	null	2024-06-30T18:24:52Z	Entry not found
hansa15100/model_3b_pt_r16_epoch10_wiki	hansa15100	2024-06-30T22:01:44Z	null	[ "tensorboard", "safetensors", "region:us" ]	null	2024-06-30T18:27:04Z	Entry not found
bdsaglam/llama-3-8b-jerx-musique-peft-v99rbjcu	bdsaglam	2024-06-30T18:28:30Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T18:28:05Z	--- 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]
kpmiller/example-model	kpmiller	2024-06-30T18:29:40Z	null	[ "license:mit", "region:us" ]	null	2024-06-30T18:29:40Z	--- license: mit ---
maninderjit829/first-repo	maninderjit829	2024-06-30T18:30:57Z	null	[ "region:us" ]	null	2024-06-30T18:30:57Z	Entry not found
maninderjit829/test-repo	maninderjit829	2024-06-30T18:31:59Z	null	[ "region:us" ]	null	2024-06-30T18:31:59Z	Entry not found
abhayesian/LLama3_HarmBench_LAT_9	abhayesian	2024-07-01T10:20:38Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T18:34:24Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
Litzy619/MIS0630T1	Litzy619	2024-07-01T01:14:22Z	null	[ "region:us" ]	null	2024-06-30T18:36:56Z	Entry not found
nourheshamshaheen/llava_8epochs	nourheshamshaheen	2024-06-30T18:38:49Z	null	[ "region:us" ]	null	2024-06-30T18:38:49Z	Entry not found
mina-kdr/fr_to_daridja_translate	mina-kdr	2024-06-30T21:30:47Z	transformers	[ "transformers", "safetensors", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-30T18:41:17Z	Entry not found
ZeZanZiet/ImageCaptioning	ZeZanZiet	2024-06-30T18:42:22Z	null	[ "region:us" ]	null	2024-06-30T18:42:22Z	Entry not found
habulaj/1633517351	habulaj	2024-06-30T18:43:43Z	null	[ "region:us" ]	null	2024-06-30T18:43:41Z	Entry not found
shantanudave/BERTopic_v1_july	shantanudave	2024-06-30T18:45:19Z	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-06-30T18:45:18Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_v1_july 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("shantanudave/BERTopic_v1_july") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| 0 \| payment - pay - card - bank - money \| 742 \| Payment Issues Detection \| \| 1 \| load - slow - search - article - doesnt \| 705 \| Slow Search Function \| \| 2 \| clothes - clothing - size - fashion - large size \| 683 \| Large Size Quality Clothing \| \| 3 \| bon - - - - \| 668 \| bon documents collection \| \| 4 \| clear - intuitive - clear easy - recommend - selection \| 665 \| Easy Clear Navigation \| \| 5 \| - - - - \| 649 \| Keyword-Driven Document Analysis \| \| 6 \| shopping - staff - friendly - store - satisfy \| 578 \| Friendly staff satisfaction \| \| 7 \| delivery - fast delivery - fast - shipping - ship \| 563 \| Fast Delivery Quality \| \| 8 \| cart - shop cart - log - password - add \| 548 \| Shopping Cart Issues \| \| 9 \| easy use - easy - use - use easy - quick easy \| 531 \| Quick & Easy Solutions \| \| 10 \| awesome - excellent - think - clearly - phenomenal \| 462 \| Really Phenomenal Clear Thinking \| \| 11 \| quality - price - quality quality - price quality - comfortable \| 454 \| Excellent Quality Price \| \| 12 \| work work - work - work quickly - flawlessly - work flawlessly \| 390 \| Efficient Flawless Work \| \| 13 \| super super - super - superb - superb super - super friendly \| 349 \| Superb Friendly Coat \| \| 14 \| really simple - ra - solve problem - control - satisfied easy \| 145 \| User-Friendly Problem Solver \| \| 15 \| clear clear - clear - fast clear - clear fast - super clear \| 144 \| Clear and Transparent Working \| \| 16 \| discover - stuff good - stuff - fact - clearly \| 129 \| Discovering Interesting Facts \| \| 17 \| satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied \| 121 \| Utmost Satisfaction \| </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13
shantanudave/BERTopic_v20240630_184948	shantanudave	2024-06-30T18:49:50Z	bertopic	[ "bertopic", "text-classification", "region:us" ]	text-classification	2024-06-30T18:49:48Z	--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_v20240630_184948 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("shantanudave/BERTopic_v20240630_184948") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> \| Topic ID \| Topic Keywords \| Topic Frequency \| Label \| \|----------\|----------------\|-----------------\|-------\| \| 0 \| payment - pay - card - bank - money \| 742 \| Payment Issues Detection \| \| 1 \| load - slow - search - article - doesnt \| 705 \| Slow Search Function \| \| 2 \| clothes - clothing - size - fashion - large size \| 683 \| Large Size Quality Clothing \| \| 3 \| bon - - - - \| 668 \| bon documents collection \| \| 4 \| clear - intuitive - clear easy - recommend - selection \| 665 \| Easy Clear Navigation \| \| 5 \| - - - - \| 649 \| Keyword-Driven Document Analysis \| \| 6 \| shopping - staff - friendly - store - satisfy \| 578 \| Friendly staff satisfaction \| \| 7 \| delivery - fast delivery - fast - shipping - ship \| 563 \| Fast Delivery Quality \| \| 8 \| cart - shop cart - log - password - add \| 548 \| Shopping Cart Issues \| \| 9 \| easy use - easy - use - use easy - quick easy \| 531 \| Quick & Easy Solutions \| \| 10 \| awesome - excellent - think - clearly - phenomenal \| 462 \| Really Phenomenal Clear Thinking \| \| 11 \| quality - price - quality quality - price quality - comfortable \| 454 \| Excellent Quality Price \| \| 12 \| work work - work - work quickly - flawlessly - work flawlessly \| 390 \| Efficient Flawless Work \| \| 13 \| super super - super - superb - superb super - super friendly \| 349 \| Superb Friendly Coat \| \| 14 \| really simple - ra - solve problem - control - satisfied easy \| 145 \| User-Friendly Problem Solver \| \| 15 \| clear clear - clear - fast clear - clear fast - super clear \| 144 \| Clear and Transparent Working \| \| 16 \| discover - stuff good - stuff - fact - clearly \| 129 \| Discovering Interesting Facts \| \| 17 \| satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied \| 121 \| Utmost Satisfaction \| </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13
Raja526/Bio_BERT_Task-ALL	Raja526	2024-06-30T18:50:16Z	transformers	[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-30T18:49:50Z	--- 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]
habulaj/9147367793	habulaj	2024-06-30T18:50:01Z	null	[ "region:us" ]	null	2024-06-30T18:49:57Z	Entry not found
darshan-aiml/nycartooncaptioncontest-git-base	darshan-aiml	2024-07-01T05:57:29Z	null	[ "region:us" ]	null	2024-06-30T18:52:39Z	Entry not found
habulaj/62039211817	habulaj	2024-06-30T18:53:23Z	null	[ "region:us" ]	null	2024-06-30T18:53:20Z	Entry not found
Med-tz/category_classifier	Med-tz	2024-06-30T18:53:52Z	null	[ "region:us" ]	null	2024-06-30T18:53:52Z	Entry not found
dbands/mistral-7b-instruct-v0.3-bnb	dbands	2024-06-30T19:00:20Z	transformers	[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-generation	2024-06-30T18:54:49Z	--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - Developed by: dbands - License: apache-2.0 - Finetuned from model : unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
Nithusikan01/fine-tuned-flan-t5-large-customer-support	Nithusikan01	2024-06-30T18:54:51Z	null	[ "region:us" ]	null	2024-06-30T18:54:51Z	Entry not found
Abdelrahman2922/distilbert-base-uncased-finetuned-Disaster_tweets	Abdelrahman2922	2024-07-01T19:16:21Z	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T18:56:35Z	Entry not found
AgastyaMalik/GarbageNet	AgastyaMalik	2024-07-01T15:42:16Z	null	[ "region:us" ]	null	2024-06-30T18:57:29Z	# GarbageNet Model Card ## Model Details Model Name: GarbageNet Model Architecture: ResNet-50 Number of Classes: 10 Dataset: Garbage Classification V2, https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 ### Model Description GarbageNet is a convolutional neural network designed to classify images of garbage into one of 10 predefined categories. The model leverages the ResNet-50 architecture, which is known for its deep residual learning capabilities, enabling it to achieve high accuracy even with relatively fewer training epochs. Developed by: Agastya Malik Model type: Image Classification Finetuned from model: ResNet-50 # Uses ## Direct Use GarbageNet is intended to be used as a tool for sorting and classifying images of garbage. This can be particularly useful for waste management systems, recycling facilities, and environmental monitoring applications. The model can be directly used through the provided Gradio interface to classify uploaded images of garbage. ## Out-of-Scope Use GarbageNet is not suitable for: Classifying non-garbage items. High-stakes applications where misclassification can lead to significant consequences. Situations requiring real-time processing on devices with limited computational power. ## Bias, Risks, and Limitations GarbageNet, like all machine learning models, has inherent limitations and potential biases: Bias: The model's performance may vary based on the diversity of the training dataset. If the dataset lacks sufficient examples of certain categories or specific types of images, the model may not perform well on those. Risks: Misclassification can lead to incorrect sorting of waste, which may affect recycling processes and waste management efficiency. Limitations: The model may not perform well in poor lighting conditions, with low-resolution images, or with objects that belong to multiple categories. ## Recommendations Use high-quality, well-lit images for classification. Continuously monitor and validate the model's performance in real-world scenarios. Be cautious when deploying the model in critical applications, and consider augmenting the dataset to improve performance on underrepresented categories. ## Training Details Training Dataset The training dataset is sourced from Kaggle and contains images classified into the following categories: Cardboard Glass Metal Paper Plastic Trash Battery Clothes Shoes Electronics ### Preprocessing: Before feeding the images into the model, the following preprocessing steps were applied: Resizing images to 224x224 pixels. Normalizing pixel values to the range [0, 1]. Applying data augmentation techniques such as rotation, flipping, and color jitter to increase the diversity of the training data. ### Training Configuration Optimizer: Adam Learning Rate: 0.001 Beta1: 0.9 Beta2: 0.999 Loss Function: CrossEntropyLoss Epochs: 5 Batch Size: 32 Learning Rate Scheduler: StepLR Step Size: 2 Gamma: 0.1 ## Evaluation The model was evaluated using a separate validation set, achieving the following performance metrics: Accuracy: 93% Precision: 92% Recall: 91% F1 Score: 91% Confusion matrices and ROC curves were also generated to provide deeper insights into the model's performance across different classes.
mahamadahmed/ser	mahamadahmed	2024-06-30T18:59:45Z	null	[ "region:us" ]	null	2024-06-30T18:59:44Z	Entry not found
Jaygeo067/llama-2-Trgoejay	Jaygeo067	2024-06-30T19:07:49Z	transformers	[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T19:02:44Z	Entry not found
maxseats/tmp	maxseats	2024-07-03T01:04:38Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-30T19:05:43Z	Entry not found
uma-wandb/my_video_model	uma-wandb	2024-07-01T04:07:45Z	transformers	[ "transformers", "safetensors", "videomae", "video-classification", "endpoints_compatible", "region:us" ]	video-classification	2024-06-30T19:06:26Z	Entry not found
habulaj/10820283115	habulaj	2024-06-30T19:08:23Z	null	[ "region:us" ]	null	2024-06-30T19:08:19Z	Entry not found
ZeZanZiet/blip_image_captioning_v1	ZeZanZiet	2024-07-01T04:27:36Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T19:09:15Z	--- 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]
maninderjit829/test2	maninderjit829	2024-06-30T19:12:58Z	null	[ "region:us" ]	null	2024-06-30T19:12:58Z	Entry not found
maninderjit829/xoxo	maninderjit829	2024-06-30T19:13:53Z	null	[ "region:us" ]	null	2024-06-30T19:13:53Z	Entry not found
chihli/llama-3-8b-chat-doctor-1	chihli	2024-07-01T10:15:09Z	transformers	[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	null	2024-06-30T19:18:15Z	--- 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]
habulaj/233173204625	habulaj	2024-06-30T19:18:39Z	null	[ "region:us" ]	null	2024-06-30T19:18:37Z	Entry not found
aadd77551/AI-test	aadd77551	2024-06-30T19:19:49Z	null	[ "region:us" ]	null	2024-06-30T19:19:49Z	Entry not found
habulaj/131864108578	habulaj	2024-06-30T19:21:13Z	null	[ "region:us" ]	null	2024-06-30T19:21:10Z	Entry not found
senagoksu/opus-mt-en-ro-finetuned-en-to-ro	senagoksu	2024-06-30T19:22:18Z	null	[ "region:us" ]	null	2024-06-30T19:22:18Z	Entry not found
Maarten1953/pegasus-samsum	Maarten1953	2024-06-30T20:29:22Z	transformers	[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-30T19:22:36Z	--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum 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. --> # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| \|:-------------:\|:------:\|:----:\|:---------------:\| \| 1.6719 \| 0.5430 \| 500 \| 1.4844 \| ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.20.0 - Tokenizers 0.19.1
JuliusFx/merged_model_opt_exp	JuliusFx	2024-06-30T22:29:47Z	transformers	[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T19:23:04Z	--- 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]
aklein4/Qwen2-0.5B-tldr-dpo-1.0	aklein4	2024-06-30T19:26:48Z	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T19:25:57Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
thesanjeetc/qlora1	thesanjeetc	2024-06-30T19:27:23Z	transformers	[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]	null	2024-06-30T19:27:01Z	--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - Developed by: thesanjeetc - License: apache-2.0 - Finetuned from model : unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
chirazman/my_awesome_qa_model	chirazman	2024-06-30T19:29:00Z	null	[ "region:us" ]	null	2024-06-30T19:29:00Z	Entry not found
vidula123/Llama-2-7b-chat-finetune-GGUF	vidula123	2024-06-30T21:50:57Z	adapter-transformers	[ "adapter-transformers", "text-generation", "en", "dataset:Vidulaae/sales_target", "dataset:Vidulaae/sales-analysis", "dataset:vidula123/Sales_Queries", "dataset:Vidulaae/demo-data", "dataset:Vidulaae/sales_analysis1", "license:llama2", "region:us" ]	text-generation	2024-06-30T19:31:14Z	--- license: llama2 datasets: - Vidulaae/sales_target - Vidulaae/sales-analysis - vidula123/Sales_Queries - Vidulaae/demo-data - Vidulaae/sales_analysis1 language: - en pipeline_tag: text-generation library_name: adapter-transformers ---
thisiskeithkwan/stanford-deidentifier-base-onnx	thisiskeithkwan	2024-06-30T19:38:57Z	transformers	[ "transformers", "onnx", "bert", "endpoints_compatible", "region:us" ]	null	2024-06-30T19:37:24Z	Entry not found
kartikay101/whisper-small-hi	kartikay101	2024-07-01T07:34:07Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Wtimit_vowel_consonent_mask_spec_aug", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-30T19:37:56Z	--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - Wtimit_vowel_consonent_mask_spec_aug metrics: - wer model-index: - name: Whisper Small Testing results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Wtimit_vowel_consonent_mask_spec_aug type: Wtimit_vowel_consonent_mask_spec_aug metrics: - name: Wer type: wer value: 19.044740024183795 --- <!-- 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 Testing This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Wtimit_vowel_consonent_mask_spec_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.2954 - Wer: 19.0447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\| \| 0.9758 \| 0.0213 \| 100 \| 0.7484 \| 18.7534 \| \| 0.2983 \| 0.0426 \| 200 \| 0.3043 \| 17.6871 \| \| 0.2397 \| 0.0638 \| 300 \| 0.2875 \| 17.1375 \| \| 0.2283 \| 0.0851 \| 400 \| 0.2787 \| 17.1320 \| \| 0.1904 \| 0.1064 \| 500 \| 0.2780 \| 16.7583 \| \| 0.1799 \| 0.1277 \| 600 \| 0.2748 \| 16.7033 \| \| 0.1782 \| 0.1489 \| 700 \| 0.2748 \| 16.7308 \| \| 0.1522 \| 0.1702 \| 800 \| 0.2726 \| 16.6154 \| \| 0.1326 \| 0.1915 \| 900 \| 0.2687 \| 16.4450 \| \| 0.138 \| 0.2128 \| 1000 \| 0.2702 \| 16.3351 \| \| 0.1317 \| 0.2340 \| 1100 \| 0.2715 \| 16.6978 \| \| 0.1312 \| 0.2553 \| 1200 \| 0.2712 \| 16.7748 \| \| 0.1222 \| 0.2766 \| 1300 \| 0.2718 \| 16.6209 \| \| 0.1181 \| 0.2979 \| 1400 \| 0.2736 \| 17.1870 \| \| 0.0975 \| 0.3191 \| 1500 \| 0.2710 \| 16.8352 \| \| 0.0795 \| 0.3404 \| 1600 \| 0.2718 \| 16.8352 \| \| 0.0791 \| 0.3617 \| 1700 \| 0.2742 \| 16.8847 \| \| 0.0822 \| 0.3830 \| 1800 \| 0.2744 \| 16.6758 \| \| 0.0734 \| 0.4043 \| 1900 \| 0.2757 \| 17.1155 \| \| 0.0896 \| 0.4255 \| 2000 \| 0.2771 \| 17.2749 \| \| 0.0578 \| 0.4468 \| 2100 \| 0.2769 \| 17.3299 \| \| 0.0727 \| 0.4681 \| 2200 \| 0.2800 \| 17.6652 \| \| 0.0691 \| 0.4894 \| 2300 \| 0.2793 \| 17.4893 \| \| 0.0656 \| 0.5106 \| 2400 \| 0.2787 \| 17.3574 \| \| 0.0726 \| 0.5319 \| 2500 \| 0.2793 \| 17.5662 \| \| 0.0494 \| 0.5532 \| 2600 \| 0.2807 \| 17.6487 \| \| 0.0635 \| 0.5745 \| 2700 \| 0.2800 \| 17.7091 \| \| 0.0503 \| 0.5957 \| 2800 \| 0.2837 \| 17.8026 \| \| 0.0688 \| 0.6170 \| 2900 \| 0.2820 \| 17.7531 \| \| 0.058 \| 0.6383 \| 3000 \| 0.2858 \| 18.1269 \| \| 0.051 \| 0.6596 \| 3100 \| 0.2871 \| 18.1159 \| \| 0.0535 \| 0.6809 \| 3200 \| 0.2870 \| 18.4951 \| \| 0.0665 \| 0.7021 \| 3300 \| 0.2868 \| 18.5776 \| \| 0.0497 \| 0.7234 \| 3400 \| 0.2891 \| 18.6105 \| \| 0.0558 \| 0.7447 \| 3500 \| 0.2891 \| 18.5446 \| \| 0.0384 \| 0.7660 \| 3600 \| 0.2891 \| 18.6820 \| \| 0.0413 \| 0.7872 \| 3700 \| 0.2908 \| 18.7369 \| \| 0.0562 \| 0.8085 \| 3800 \| 0.2916 \| 18.6655 \| \| 0.0523 \| 0.8298 \| 3900 \| 0.2920 \| 18.6600 \| \| 0.043 \| 0.8511 \| 4000 \| 0.2928 \| 18.7260 \| \| 0.0463 \| 0.8723 \| 4100 \| 0.2926 \| 18.6765 \| \| 0.0517 \| 0.8936 \| 4200 \| 0.2942 \| 18.7809 \| \| 0.0408 \| 0.9149 \| 4300 \| 0.2950 \| 18.7644 \| \| 0.0362 \| 0.9362 \| 4400 \| 0.2954 \| 18.8799 \| \| 0.047 \| 0.9574 \| 4500 \| 0.2954 \| 18.9623 \| \| 0.0347 \| 0.9787 \| 4600 \| 0.2954 \| 19.0118 \| \| 0.0404 \| 1.0 \| 4700 \| 0.2956 \| 19.0392 \| \| 0.0559 \| 1.0213 \| 4800 \| 0.2956 \| 19.0063 \| \| 0.0462 \| 1.0426 \| 4900 \| 0.2956 \| 19.1162 \| \| 0.0385 \| 1.0638 \| 5000 \| 0.2954 \| 19.0447 \| ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
fbaldassarri/modello-italia-9B-GGUF	fbaldassarri	2024-07-02T19:26:23Z	transformers	[ "transformers", "gguf", "gpt_neox", "text-generation", "pytorch", "conversational", "it", "base_model:sapienzanlp/modello-italia-9b", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T19:38:25Z	--- language: - it license: mit tags: - pytorch model_name: Modello Italia 9B base_model: sapienzanlp/modello-italia-9b inference: false model_creator: iGeniusAI model_type: gpt-neonx pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- # Model Card for Modello Italia 9B GGUFs This an UNOFFICIAL GGUF format model files repository for converted/quantized OFFICIAL model checkpoint of "Modello Italia 9B", Large Language Model (LLM) developed by [iGenius](https://it.igenius.ai/) in collaboration with [CINECA](https://www.cineca.it/). * More information about Modello Italia: [click here](https://it.igenius.ai/language-models). ## 🚨 Disclaimers * This is an UNOFFICIAL quantization of the OFFICIAL model checkpoint released by iGenius. * This model is based also on the conversion made for HF Transformers by [Sapienza NLP, Sapienza University of Rome](https://huggingface.co/sapienzanlp). * The original model was developed using LitGPT, therefore, the weights need to be converted before they can be used with Hugging Face transformers. ## 🚨 Terms and Conditions * Note: By using this model, you accept the iGenius' [terms and conditions](https://secure.igenius.ai/legal/italia_terms_and_conditions.pdf). ### 🚨 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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ## 🚨 Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th 2023 onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 🚨 Reproducibility This model has been converted/quantized using Intel [neural-speed](https://github.com/intel/neural-speed/). ## 🚨 Biases and Risks From the terms and conditions of iGenius for Modello Italia: > Modello Italia è concepito per essere utilizzato da tutti e per adattarsi a una vasta gamma di casi d'uso. È stato progettato con l'obiettivo di essere accessibile a persone provenienti da background, esperienze e prospettive diverse. Modello Italia si rivolge agli utenti e alle loro esigenze senza inserire giudizi superflui o normative, riconoscendo al contempo che anche contenuti potenzialmente problematici in determinati contesti possono avere scopi validi in altri. Il rispetto per la dignità e l'autonomia di tutti gli utenti, specialmente in termini di libertà di pensiero ed espressione, è un pilastro fondamentale del suo design. Tuttavia, essendo una nuova tecnologia, Modello Italia comporta rischi legati al suo utilizzo. I test condotti finora sono stati eseguiti in italiano e non hanno potuto coprire tutte le possibili situazioni. Pertanto, come per tutti gli LLM, non è possibile prevedere in anticipo gli output di Modello Italia e il modello potrebbe in alcuni casi generare risposte imprecise, tendenziose o altre risposte discutibili. Prima di utilizzare Modello Italia in qualsiasi contesto, gli sviluppatori sono fortemente incoraggiati a eseguire test di sicurezza e adattamento specifici per le loro applicazioni. We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data. For more information about this issue, please refer to our survey paper: * [Biases in Large Language Models: Origins, Inventory, and Discussion](https://dl.acm.org/doi/full/10.1145/3597307) ## Model architecture * The model architecture is based on GPT-NeoX.
silveroxides/Vision_8B_Uncensored_4bit	silveroxides	2024-06-30T19:38:53Z	null	[ "region:us" ]	null	2024-06-30T19:38:53Z	Entry not found
aklein4/Qwen2-0.5B-tldr-dro-binary-1.0	aklein4	2024-06-30T19:40:21Z	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T19:39:20Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. 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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]
Anujgr8/Whisper-Anuj-Medum-Medium-lalo	Anujgr8	2024-07-01T06:18:14Z	transformers	[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]	automatic-speech-recognition	2024-06-30T19:40:45Z	--- 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. 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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]
ewjfwejfoiwe/Unet_GAN_Self-driving-car-vision-segmentation	ewjfwejfoiwe	2024-06-30T19:45:49Z	null	[ "license:mit", "region:us" ]	null	2024-06-30T19:42:30Z	--- license: mit ---
talhaturab/my-first-model	talhaturab	2024-06-30T19:46:37Z	diffusers	[ "diffusers", "safetensors", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]	text-to-image	2024-06-30T19:44:40Z	--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth-hackathon - animal widget: - text: a photo of with face of talhamax cat and body of a elephant --- # DreamBooth model for the custom finetunig concept trained by talha on the max_cat dataset. This is a Stable Diffusion model fine-tuned on the max-pics concept with DreamBooth. It can be used by modifying the `instance_prompt`: a photo of talhamax cat This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is demo model ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('talhaturab/my-first-model') image = pipeline().images[0] image ```
2052man/vira-reservation	2052man	2024-06-30T19:47:30Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T19:47:30Z	--- license: apache-2.0 ---
LowFace/newtest	LowFace	2024-06-30T19:53:01Z	null	[ "region:us" ]	null	2024-06-30T19:53:01Z	Entry not found
erayyapagci/multilingual-e5-onnx-vespa	erayyapagci	2024-06-30T20:06:31Z	null	[ "onnx", "region:us" ]	null	2024-06-30T19:54:45Z	Entry not found
senagoksu/t5-small-finetuned-xsum	senagoksu	2024-07-01T11:36:19Z	transformers	[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "base_model:t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text2text-generation	2024-06-30T19:58:02Z	--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: validation args: default metrics: - name: Rouge1 type: rouge value: 27.9257 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5074 - Rouge1: 27.9257 - Rouge2: 7.4618 - Rougel: 21.9338 - Rougelsum: 21.9405 - Gen Len: 18.8176 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.5 - mixed_precision_training: Native AMP ### Training results \| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \| \|:-------------:\|:------:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\| \| 2.817 \| 0.0784 \| 500 \| 2.5683 \| 26.6596 \| 6.6324 \| 20.7701 \| 20.7761 \| 18.8057 \| \| 2.8029 \| 0.1568 \| 1000 \| 2.5435 \| 27.1558 \| 6.9694 \| 21.2178 \| 21.2216 \| 18.7999 \| \| 2.7797 \| 0.2352 \| 1500 \| 2.5270 \| 27.5528 \| 7.2608 \| 21.621 \| 21.6233 \| 18.7982 \| \| 2.7651 \| 0.3137 \| 2000 \| 2.5165 \| 27.6104 \| 7.2896 \| 21.6928 \| 21.7012 \| 18.8133 \| \| 2.7514 \| 0.3921 \| 2500 \| 2.5112 \| 27.8452 \| 7.3791 \| 21.8632 \| 21.8659 \| 18.8118 \| \| 2.7463 \| 0.4705 \| 3000 \| 2.5074 \| 27.9257 \| 7.4618 \| 21.9338 \| 21.9405 \| 18.8176 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1
Tristan7234/Emma	Tristan7234	2024-06-30T19:59:04Z	null	[ "license:mit", "region:us" ]	null	2024-06-30T19:59:04Z	--- license: mit ---
Samiyar/Teste	Samiyar	2024-06-30T19:59:08Z	null	[ "license:openrail", "region:us" ]	null	2024-06-30T19:59:08Z	--- license: openrail ---
adamkarvonen/othello-saes	adamkarvonen	2024-06-30T20:10:00Z	null	[ "region:us" ]	null	2024-06-30T20:02:15Z	Entry not found
ANDREBARRETOLOPES/Andre	ANDREBARRETOLOPES	2024-06-30T20:03:46Z	null	[ "license:apache-2.0", "region:us" ]	null	2024-06-30T20:03:46Z	--- license: apache-2.0 ---
ralphkalweit/Reinforce-PixelCopter	ralphkalweit	2024-06-30T20:28:11Z	null	[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]	reinforcement-learning	2024-06-30T20:04:26Z	--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 8.30 +/- 9.60 name: mean_reward verified: false --- # Reinforce Agent playing Pixelcopter-PLE-v0 This is a trained model of a Reinforce agent playing Pixelcopter-PLE-v0 . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
Litzy619/MIS0630T2	Litzy619	2024-07-01T02:36:00Z	null	[ "region:us" ]	null	2024-06-30T20:05:43Z	Entry not found
random2344/vector2	random2344	2024-06-30T20:09:07Z	null	[ "region:us" ]	null	2024-06-30T20:08:48Z	Entry not found
sindhujag26/distilbert-base-uncased-finetuned-ner	sindhujag26	2024-07-01T12:59:17Z	transformers	[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]	token-classification	2024-06-30T20:09:58Z	--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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 \| Precision \| Recall \| F1 \| Accuracy \| \|:-------------:\|:-----:\|:----:\|:---------------:\|:---------:\|:------:\|:---:\|:--------:\| \| No log \| 1.0 \| 50 \| 0.0208 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 2.0 \| 100 \| 0.0027 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| \| No log \| 3.0 \| 150 \| 0.0020 \| 1.0 \| 1.0 \| 1.0 \| 1.0 \| ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2+cpu - Datasets 2.19.2 - Tokenizers 0.19.1
pedroharaujo/emma_lora	pedroharaujo	2024-06-30T23:18:49Z	null	[ "region:us" ]	null	2024-06-30T20:10:05Z	Entry not found
lit9003code/melotts300	lit9003code	2024-06-30T20:14:11Z	null	[ "region:us" ]	null	2024-06-30T20:13:50Z	Entry not found
lit9003code/melotts301	lit9003code	2024-06-30T20:15:46Z	null	[ "region:us" ]	null	2024-06-30T20:15:25Z	Entry not found
lit9003code/melotts302	lit9003code	2024-06-30T20:18:16Z	null	[ "region:us" ]	null	2024-06-30T20:17:01Z	Entry not found
lit9003code/melotts303	lit9003code	2024-06-30T20:20:01Z	null	[ "region:us" ]	null	2024-06-30T20:19:38Z	Entry not found
lit9003code/melotts304	lit9003code	2024-06-30T20:21:34Z	null	[ "region:us" ]	null	2024-06-30T20:21:14Z	Entry not found
AnotherNN/Losk	AnotherNN	2024-06-30T20:23:12Z	null	[ "region:us" ]	null	2024-06-30T20:21:25Z	Entry not found
Moriacrafter/Qwen1.5-0.5B-8bit_DepressionDetection	Moriacrafter	2024-06-30T20:22:42Z	transformers	[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T20:22:11Z	--- library_name: transformers tags: - llama-factory --- # 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]
lit9003code/melotts305	lit9003code	2024-06-30T20:23:11Z	null	[ "region:us" ]	null	2024-06-30T20:22:49Z	Entry not found
FartLabs/FART_SMILES_tokenized_PubChem_shard00_160k_augmented	FartLabs	2024-06-30T20:23:23Z	transformers	[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text-classification	2024-06-30T20:23:12Z	--- library_name: transformers tags: [] --- # Model Card for Model ID <!-- Provide a quick summary of what the model is/does. --> ## Model Details ### Model Description <!-- Provide a longer summary of what this model is. --> This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated. - Developed by: [More Information Needed] - Funded by [optional]: [More Information Needed] - Shared by [optional]: [More Information Needed] - Model type: [More Information Needed] - Language(s) (NLP): [More Information Needed] - License: [More Information Needed] - Finetuned from model [optional]: [More Information Needed] ### Model Sources [optional] <!-- Provide the basic links for the model. --> - Repository: [More Information Needed] - Paper [optional]: [More Information Needed] - Demo [optional]: [More Information Needed] ## Uses <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. --> ### Direct Use <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> [More Information Needed] ### Downstream Use [optional] <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> [More Information Needed] ### Out-of-Scope Use <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> [More Information Needed] ## Bias, Risks, and Limitations <!-- This section is meant to convey both technical and sociotechnical limitations. --> [More Information Needed] ### Recommendations <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. ## How to Get Started with the Model Use the code below to get started with the model. [More Information Needed] ## Training Details ### Training Data <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> [More Information Needed] ### Training Procedure <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - Training regime: [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision --> #### Speeds, Sizes, Times [optional] <!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. --> [More Information Needed] ## Evaluation <!-- This section describes the evaluation protocols and provides the results. --> ### Testing Data, Factors & Metrics #### Testing Data <!-- This should link to a Dataset Card if possible. --> [More Information Needed] #### Factors <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> [More Information Needed] #### Metrics <!-- These are the evaluation metrics being used, ideally with a description of why. --> [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional] <!-- Relevant interpretability work for the model goes here --> [More Information Needed] ## Environmental Impact <!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). - Hardware Type: [More Information Needed] - Hours used: [More Information Needed] - Cloud Provider: [More Information Needed] - Compute Region: [More Information Needed] - Carbon Emitted: [More Information Needed] ## Technical Specifications [optional] ### Model Architecture and Objective [More Information Needed] ### Compute Infrastructure [More Information Needed] #### Hardware [More Information Needed] #### Software [More Information Needed] ## Citation [optional] <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> BibTeX: [More Information Needed] APA: [More Information Needed] ## Glossary [optional] <!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]
lit9003code/melotts306	lit9003code	2024-06-30T20:25:38Z	null	[ "region:us" ]	null	2024-06-30T20:24:29Z	Entry not found
iceman2434/xlm-roberta-base-ft-udpos213-top2langrandom	iceman2434	2024-06-30T20:32:46Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:26:33Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans & Norwegian corpora (Top 2 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.58\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
lit9003code/melotts307	lit9003code	2024-06-30T20:27:21Z	null	[ "region:us" ]	null	2024-06-30T20:26:58Z	Entry not found
aadd77551/test	aadd77551	2024-06-30T20:27:40Z	null	[ "region:us" ]	null	2024-06-30T20:27:40Z	Entry not found
iceman2434/xlm-roberta-base-ft-udpos213-top3langrandom	iceman2434	2024-06-30T20:33:12Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:28:23Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans, Norwegian, & Vietnamese corpora (Top 3 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.29\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
lit9003code/melotts308	lit9003code	2024-06-30T20:28:58Z	null	[ "region:us" ]	null	2024-06-30T20:28:38Z	Entry not found
asafi/Meta-Llama-3-medical-8B-merged	asafi	2024-06-30T20:34:37Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T20:29:22Z	--- library_name: transformers tags: - unsloth - trl - sft --- # 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]
iceman2434/xlm-roberta-base-ft-udpos213-top4langrandom	iceman2434	2024-06-30T20:33:33Z	null	[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]	token-classification	2024-06-30T20:30:11Z	--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Afrikaans, Norwegian, Vietnamese, & Hebrew (Top 4 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.55\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB
lit9003code/melotts309	lit9003code	2024-06-30T20:31:28Z	null	[ "region:us" ]	null	2024-06-30T20:30:15Z	Entry not found
DimensionSTP/Llama-3-KoEn-8B-Instruct-preview-scientificQA	DimensionSTP	2024-06-30T20:39:13Z	transformers	[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "llama-3-ko", "conversational", "en", "ko", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]	text-generation	2024-06-30T20:30:25Z	--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- ## Model Details This model is fine-tuned by beomi/Llama-3-KoEn-8B-Instruct-preview Fine-tuning dataset: Scientific QA dataset ```
tarsssss/finetuned-kde4-pt-to-ca-2	tarsssss	2024-07-01T04:16:21Z	transformers	[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:open_subtitles", "base_model:Helsinki-NLP/opus-mt-pt-ca", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]	text2text-generation	2024-06-30T20:31:02Z	--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-pt-ca tags: - generated_from_trainer datasets: - open_subtitles metrics: - bleu model-index: - name: finetuned-kde4-pt-to-ca-2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: open_subtitles type: open_subtitles config: ca-pt split: train args: ca-pt metrics: - name: Bleu type: bleu value: 35.718810961905895 --- <!-- This model card has been generated automatically according to the information the Trainer had access to. You should probably proofread and complete it, then remove this comment. --> # finetuned-kde4-pt-to-ca-2 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-pt-ca](https://huggingface.co/Helsinki-NLP/opus-mt-pt-ca) on the open_subtitles dataset. It achieves the following results on the evaluation set: - Loss: 0.0943 - Bleu: 35.7188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Arovincy/CustomizedTextGeneration

Arovincy

2024-06-30T17:57:45Z

0

null

[ "region:us" ]

null

2024-06-30T17:57:15Z

Entry not found

LaLaf93/inproceedings_recognizer

LaLaf93

2024-06-30T18:07:35Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T18:00:11Z

Entry not found

TomEijkelenkamp/renaissance-cogvlm-composition

TomEijkelenkamp

2024-06-30T18:00:51Z

0

null

[ "region:us" ]

null

2024-06-30T18:00:51Z

Entry not found

6pu8wtw6/UncensoredPonyXL

6pu8wtw6

2024-06-30T18:00:52Z

0

null

[ "license:unknown", "region:us" ]

null

2024-06-30T18:00:52Z

--- license: unknown ---

apwic/summarization-base-0

apwic

2024-06-30T23:41:14Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "id", "base_model:LazarusNLP/IndoNanoT5-base", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-06-30T18:04:33Z

--- language: - id license: apache-2.0 base_model: LazarusNLP/IndoNanoT5-base tags: - generated_from_trainer metrics: - rouge model-index: - name: summarization-base-0 results: [] ---  # summarization-base-0 This model is a fine-tuned version of [LazarusNLP/IndoNanoT5-base](https://huggingface.co/LazarusNLP/IndoNanoT5-base) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.5082 - Rouge1: 0.3572 - Rouge2: 0.0 - Rougel: 0.3545 - Rougelsum: 0.3557 - Gen Len: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 5e-05 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 5.0 ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:| | 0.6282 | 1.0 | 3566 | 0.4719 | 0.43 | 0.0 | 0.4255 | 0.4282 | 1.0 | | 0.4301 | 2.0 | 7132 | 0.4728 | 0.3754 | 0.0 | 0.3711 | 0.3719 | 1.0 | | 0.3336 | 3.0 | 10698 | 0.4632 | 0.3806 | 0.0 | 0.3777 | 0.3808 | 1.0 | | 0.2643 | 4.0 | 14264 | 0.4921 | 0.3537 | 0.0 | 0.3512 | 0.3514 | 1.0 | | 0.2174 | 5.0 | 17830 | 0.5082 | 0.3572 | 0.0 | 0.3545 | 0.3557 | 1.0 | ### Framework versions - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.19.1 - Tokenizers 0.19.1

LaLaf93/incollection_recognizer

LaLaf93

2024-06-30T18:14:29Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T18:07:40Z

Entry not found

CarlosPov/Llama-2-7b-chat-hf-finetune_90_10_MIX

CarlosPov

2024-06-30T18:09:22Z

0

peft

[ "peft", "safetensors", "generated_from_trainer", "base_model:meta-llama/Llama-2-7b-chat-hf", "license:llama2", "region:us" ]

null

2024-06-30T18:08:23Z

--- license: llama2 library_name: peft tags: - generated_from_trainer base_model: meta-llama/Llama-2-7b-chat-hf model-index: - name: Llama-2-7b-chat-hf-finetune_90_10_MIX results: [] ---  # Llama-2-7b-chat-hf-finetune_90_10_MIX This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 1.3119 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 3 - eval_batch_size: 3 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: reduce_lr_on_plateau - num_epochs: 50 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-------:|:-----:|:---------------:| | 0.6795 | 0.9968 | 316 | 0.7737 | | 0.2756 | 1.9937 | 632 | 0.8534 | | 0.166 | 2.9905 | 948 | 0.9507 | | 0.1135 | 3.9874 | 1264 | 1.0163 | | 0.086 | 4.9842 | 1580 | 1.0497 | | 0.0788 | 5.9811 | 1896 | 1.0818 | | 0.1423 | 6.9779 | 2212 | 1.1176 | | 0.0778 | 7.9748 | 2528 | 1.1538 | | 0.0792 | 8.9716 | 2844 | 1.1963 | | 0.0657 | 9.9685 | 3160 | 1.1900 | | 0.0639 | 10.9653 | 3476 | 1.2259 | | 0.0681 | 11.9621 | 3792 | 1.2195 | | 0.0522 | 12.9590 | 4108 | 1.2163 | | 0.0492 | 13.9558 | 4424 | 1.2259 | | 0.048 | 14.9527 | 4740 | 1.2378 | | 0.0441 | 15.9495 | 5056 | 1.2492 | | 0.0629 | 16.9464 | 5372 | 1.2564 | | 0.0622 | 17.9432 | 5688 | 1.2606 | | 0.0589 | 18.9401 | 6004 | 1.2662 | | 0.0592 | 19.9369 | 6320 | 1.2712 | | 0.0586 | 20.9338 | 6636 | 1.2780 | | 0.0594 | 21.9306 | 6952 | 1.2807 | | 0.0616 | 22.9274 | 7268 | 1.2874 | | 0.0554 | 23.9243 | 7584 | 1.2904 | | 0.0562 | 24.9211 | 7900 | 1.2934 | | 0.0543 | 25.9180 | 8216 | 1.2961 | | 0.0553 | 26.9148 | 8532 | 1.2986 | | 0.0547 | 27.9117 | 8848 | 1.3009 | | 0.0543 | 28.9085 | 9164 | 1.3025 | | 0.0535 | 29.9054 | 9480 | 1.3040 | | 0.0535 | 30.9022 | 9796 | 1.3053 | | 0.0533 | 31.8991 | 10112 | 1.3068 | | 0.053 | 32.8959 | 10428 | 1.3078 | | 0.0528 | 33.8927 | 10744 | 1.3096 | | 0.0526 | 34.8896 | 11060 | 1.3098 | | 0.0523 | 35.8864 | 11376 | 1.3100 | | 0.052 | 36.8833 | 11692 | 1.3102 | | 0.0516 | 37.8801 | 12008 | 1.3104 | | 0.0513 | 38.8770 | 12324 | 1.3105 | | 0.0504 | 39.8738 | 12640 | 1.3107 | | 0.0508 | 40.8707 | 12956 | 1.3109 | | 0.0503 | 41.8675 | 13272 | 1.3111 | | 0.0501 | 42.8644 | 13588 | 1.3114 | | 0.0502 | 43.8612 | 13904 | 1.3116 | | 0.05 | 44.8580 | 14220 | 1.3118 | | 0.0498 | 45.8549 | 14536 | 1.3118 | | 0.0517 | 46.8517 | 14852 | 1.3118 | | 0.0496 | 47.8486 | 15168 | 1.3118 | | 0.0486 | 48.8454 | 15484 | 1.3118 | | 0.0475 | 49.8423 | 15800 | 1.3119 | ### Framework versions - PEFT 0.11.1 - Transformers 4.40.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

daviddextre/ModelsPonyXL2

daviddextre

2024-06-30T19:12:43Z

0

null

[ "region:us" ]

null

2024-06-30T18:08:26Z

Entry not found

csteinmetz1/afx-rep

csteinmetz1

2024-06-30T19:13:34Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T18:12:55Z

--- license: apache-2.0 ---

noobilal/LLaMA3-Steve-Jobs

noobilal

2024-06-30T18:14:08Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-30T18:13:54Z

--- base_model: unsloth/llama-3-8b-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** noobilal - **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)

LaLaf93/phdthesis_recognizer

LaLaf93

2024-06-30T18:21:41Z

0

transformers

[ "transformers", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T18:14:34Z

Entry not found

shantanudave/BERTopic_vjuly

shantanudave

2024-06-30T18:18:02Z

0

bertopic

[ "bertopic", "text-classification", "region:us" ]

text-classification

2024-06-30T18:18:01Z

--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_vjuly 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("shantanudave/BERTopic_vjuly") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | 0 | payment - pay - card - bank - money | 742 | Payment Issues Detection | | 1 | load - slow - search - article - doesnt | 705 | Slow Search Function | | 2 | clothes - clothing - size - fashion - large size | 683 | Large Size Quality Clothing | | 3 | bon - - - - | 668 | bon documents collection | | 4 | clear - intuitive - clear easy - recommend - selection | 665 | Easy Clear Navigation | | 5 | - - - - | 649 | Keyword-Driven Document Analysis | | 6 | shopping - staff - friendly - store - satisfy | 578 | Friendly staff satisfaction | | 7 | delivery - fast delivery - fast - shipping - ship | 563 | Fast Delivery Quality | | 8 | cart - shop cart - log - password - add | 548 | Shopping Cart Issues | | 9 | easy use - easy - use - use easy - quick easy | 531 | Quick & Easy Solutions | | 10 | awesome - excellent - think - clearly - phenomenal | 462 | Really Phenomenal Clear Thinking | | 11 | quality - price - quality quality - price quality - comfortable | 454 | Excellent Quality Price | | 12 | work work - work - work quickly - flawlessly - work flawlessly | 390 | Efficient Flawless Work | | 13 | super super - super - superb - superb super - super friendly | 349 | Superb Friendly Coat | | 14 | really simple - ra - solve problem - control - satisfied easy | 145 | User-Friendly Problem Solver | | 15 | clear clear - clear - fast clear - clear fast - super clear | 144 | Clear and Transparent Working | | 16 | discover - stuff good - stuff - fact - clearly | 129 | Discovering Interesting Facts | | 17 | satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied | 121 | Utmost Satisfaction | </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13

adnanshirik/astroclip

adnanshirik

2024-06-30T21:59:15Z

0

null

[ "arxiv:2310.03024", "region:us" ]

null

2024-06-30T18:18:42Z

PyTorch Lightning model checkpoints for all models created in reproduction of [AstroCLIP: A Cross-Modal Foundation Model for Galaxies](https://arxiv.org/abs/2310.03024). The reproduction is part of an assessed project and is currently private, if you are an assessor and require access to these saved model weights, please request access. There are 7 model checkpoints, one for each embedding dimensionality in [8, 16, 32, 64, 128, 256, 512]. --- license: mit ---

ANDRIOIDEA/ANDRIOIDE

ANDRIOIDEA

2024-06-30T18:19:04Z

0

null

[ "region:us" ]

null

2024-06-30T18:19:04Z

Entry not found

Nithusikan01/fine-tuned-llama-3-8B-customer-support

Nithusikan01

2024-06-30T18:19:20Z

0

null

[ "region:us" ]

null

2024-06-30T18:19:20Z

Entry not found

net31/naschainv148

net31

2024-07-01T09:11:12Z

0

null

[ "region:us" ]

null

2024-06-30T18:20:32Z

Entry not found

habulaj/4532236697

habulaj

2024-06-30T18:21:17Z

0

null

[ "region:us" ]

null

2024-06-30T18:21:10Z

Entry not found

habulaj/12116496255

habulaj

2024-06-30T18:24:53Z

0

null

[ "region:us" ]

null

2024-06-30T18:24:52Z

Entry not found

hansa15100/model_3b_pt_r16_epoch10_wiki

hansa15100

2024-06-30T22:01:44Z

0

null

[ "tensorboard", "safetensors", "region:us" ]

null

2024-06-30T18:27:04Z

Entry not found

bdsaglam/llama-3-8b-jerx-musique-peft-v99rbjcu

bdsaglam

2024-06-30T18:28:30Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T18:28:05Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

kpmiller/example-model

kpmiller

2024-06-30T18:29:40Z

0

null

[ "license:mit", "region:us" ]

null

2024-06-30T18:29:40Z

--- license: mit ---

maninderjit829/first-repo

maninderjit829

2024-06-30T18:30:57Z

0

null

[ "region:us" ]

null

2024-06-30T18:30:57Z

Entry not found

maninderjit829/test-repo

maninderjit829

2024-06-30T18:31:59Z

0

null

[ "region:us" ]

null

2024-06-30T18:31:59Z

Entry not found

abhayesian/LLama3_HarmBench_LAT_9

abhayesian

2024-07-01T10:20:38Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T18:34:24Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Litzy619/MIS0630T1

Litzy619

2024-07-01T01:14:22Z

0

null

[ "region:us" ]

null

2024-06-30T18:36:56Z

Entry not found

nourheshamshaheen/llava_8epochs

nourheshamshaheen

2024-06-30T18:38:49Z

0

null

[ "region:us" ]

null

2024-06-30T18:38:49Z

Entry not found

mina-kdr/fr_to_daridja_translate

mina-kdr

2024-06-30T21:30:47Z

0

transformers

[ "transformers", "safetensors", "mt5", "text2text-generation", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-30T18:41:17Z

Entry not found

ZeZanZiet/ImageCaptioning

ZeZanZiet

2024-06-30T18:42:22Z

0

null

[ "region:us" ]

null

2024-06-30T18:42:22Z

Entry not found

habulaj/1633517351

habulaj

2024-06-30T18:43:43Z

0

null

[ "region:us" ]

null

2024-06-30T18:43:41Z

Entry not found

shantanudave/BERTopic_v1_july

shantanudave

2024-06-30T18:45:19Z

0

bertopic

[ "bertopic", "text-classification", "region:us" ]

text-classification

2024-06-30T18:45:18Z

--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_v1_july 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("shantanudave/BERTopic_v1_july") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | 0 | payment - pay - card - bank - money | 742 | Payment Issues Detection | | 1 | load - slow - search - article - doesnt | 705 | Slow Search Function | | 2 | clothes - clothing - size - fashion - large size | 683 | Large Size Quality Clothing | | 3 | bon - - - - | 668 | bon documents collection | | 4 | clear - intuitive - clear easy - recommend - selection | 665 | Easy Clear Navigation | | 5 | - - - - | 649 | Keyword-Driven Document Analysis | | 6 | shopping - staff - friendly - store - satisfy | 578 | Friendly staff satisfaction | | 7 | delivery - fast delivery - fast - shipping - ship | 563 | Fast Delivery Quality | | 8 | cart - shop cart - log - password - add | 548 | Shopping Cart Issues | | 9 | easy use - easy - use - use easy - quick easy | 531 | Quick & Easy Solutions | | 10 | awesome - excellent - think - clearly - phenomenal | 462 | Really Phenomenal Clear Thinking | | 11 | quality - price - quality quality - price quality - comfortable | 454 | Excellent Quality Price | | 12 | work work - work - work quickly - flawlessly - work flawlessly | 390 | Efficient Flawless Work | | 13 | super super - super - superb - superb super - super friendly | 349 | Superb Friendly Coat | | 14 | really simple - ra - solve problem - control - satisfied easy | 145 | User-Friendly Problem Solver | | 15 | clear clear - clear - fast clear - clear fast - super clear | 144 | Clear and Transparent Working | | 16 | discover - stuff good - stuff - fact - clearly | 129 | Discovering Interesting Facts | | 17 | satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied | 121 | Utmost Satisfaction | </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13

shantanudave/BERTopic_v20240630_184948

shantanudave

2024-06-30T18:49:50Z

0

bertopic

[ "bertopic", "text-classification", "region:us" ]

text-classification

2024-06-30T18:49:48Z

--- tags: - bertopic library_name: bertopic pipeline_tag: text-classification --- # BERTopic_v20240630_184948 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("shantanudave/BERTopic_v20240630_184948") topic_model.get_topic_info() ``` ## Topic overview * Number of topics: 18 * Number of training documents: 8526 <details> <summary>Click here for an overview of all topics.</summary> | Topic ID | Topic Keywords | Topic Frequency | Label | |----------|----------------|-----------------|-------| | 0 | payment - pay - card - bank - money | 742 | Payment Issues Detection | | 1 | load - slow - search - article - doesnt | 705 | Slow Search Function | | 2 | clothes - clothing - size - fashion - large size | 683 | Large Size Quality Clothing | | 3 | bon - - - - | 668 | bon documents collection | | 4 | clear - intuitive - clear easy - recommend - selection | 665 | Easy Clear Navigation | | 5 | - - - - | 649 | Keyword-Driven Document Analysis | | 6 | shopping - staff - friendly - store - satisfy | 578 | Friendly staff satisfaction | | 7 | delivery - fast delivery - fast - shipping - ship | 563 | Fast Delivery Quality | | 8 | cart - shop cart - log - password - add | 548 | Shopping Cart Issues | | 9 | easy use - easy - use - use easy - quick easy | 531 | Quick & Easy Solutions | | 10 | awesome - excellent - think - clearly - phenomenal | 462 | Really Phenomenal Clear Thinking | | 11 | quality - price - quality quality - price quality - comfortable | 454 | Excellent Quality Price | | 12 | work work - work - work quickly - flawlessly - work flawlessly | 390 | Efficient Flawless Work | | 13 | super super - super - superb - superb super - super friendly | 349 | Superb Friendly Coat | | 14 | really simple - ra - solve problem - control - satisfied easy | 145 | User-Friendly Problem Solver | | 15 | clear clear - clear - fast clear - clear fast - super clear | 144 | Clear and Transparent Working | | 16 | discover - stuff good - stuff - fact - clearly | 129 | Discovering Interesting Facts | | 17 | satisfied - satisfaction - totally satisfied - satisfied good - completely satisfied | 121 | Utmost Satisfaction | </details> ## Training hyperparameters * calculate_probabilities: True * 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: True * zeroshot_min_similarity: 0.7 * zeroshot_topic_list: None ## Framework versions * Numpy: 1.23.5 * HDBSCAN: 0.8.33 * UMAP: 0.5.5 * Pandas: 1.3.5 * Scikit-Learn: 1.4.1.post1 * Sentence-transformers: 2.6.1 * Transformers: 4.41.2 * Numba: 0.59.1 * Plotly: 5.22.0 * Python: 3.10.13

Raja526/Bio_BERT_Task-ALL

Raja526

2024-06-30T18:50:16Z

0

transformers

[ "transformers", "safetensors", "bert", "token-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-30T18:49:50Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/9147367793

habulaj

2024-06-30T18:50:01Z

0

null

[ "region:us" ]

null

2024-06-30T18:49:57Z

Entry not found

darshan-aiml/nycartooncaptioncontest-git-base

darshan-aiml

2024-07-01T05:57:29Z

0

null

[ "region:us" ]

null

2024-06-30T18:52:39Z

Entry not found

habulaj/62039211817

habulaj

2024-06-30T18:53:23Z

0

null

[ "region:us" ]

null

2024-06-30T18:53:20Z

Entry not found

Med-tz/category_classifier

Med-tz

2024-06-30T18:53:52Z

0

null

[ "region:us" ]

null

2024-06-30T18:53:52Z

Entry not found

dbands/mistral-7b-instruct-v0.3-bnb

dbands

2024-06-30T19:00:20Z

0

transformers

[ "transformers", "safetensors", "mistral", "text-generation", "text-generation-inference", "unsloth", "trl", "conversational", "en", "base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-generation

2024-06-30T18:54:49Z

--- base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - mistral - trl --- # Uploaded model - **Developed by:** dbands - **License:** apache-2.0 - **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

Nithusikan01/fine-tuned-flan-t5-large-customer-support

Nithusikan01

2024-06-30T18:54:51Z

0

null

[ "region:us" ]

null

2024-06-30T18:54:51Z

Entry not found

Abdelrahman2922/distilbert-base-uncased-finetuned-Disaster_tweets

Abdelrahman2922

2024-07-01T19:16:21Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "text-classification", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T18:56:35Z

Entry not found

AgastyaMalik/GarbageNet

AgastyaMalik

2024-07-01T15:42:16Z

0

null

[ "region:us" ]

null

2024-06-30T18:57:29Z

# GarbageNet Model Card ## Model Details Model Name: GarbageNet Model Architecture: ResNet-50 Number of Classes: 10 Dataset: Garbage Classification V2, https://www.kaggle.com/datasets/sumn2u/garbage-classification-v2 ### Model Description GarbageNet is a convolutional neural network designed to classify images of garbage into one of 10 predefined categories. The model leverages the ResNet-50 architecture, which is known for its deep residual learning capabilities, enabling it to achieve high accuracy even with relatively fewer training epochs. Developed by: Agastya Malik Model type: Image Classification Finetuned from model: ResNet-50 # Uses ## Direct Use GarbageNet is intended to be used as a tool for sorting and classifying images of garbage. This can be particularly useful for waste management systems, recycling facilities, and environmental monitoring applications. The model can be directly used through the provided Gradio interface to classify uploaded images of garbage. ## Out-of-Scope Use GarbageNet is not suitable for: Classifying non-garbage items. High-stakes applications where misclassification can lead to significant consequences. Situations requiring real-time processing on devices with limited computational power. ## Bias, Risks, and Limitations GarbageNet, like all machine learning models, has inherent limitations and potential biases: Bias: The model's performance may vary based on the diversity of the training dataset. If the dataset lacks sufficient examples of certain categories or specific types of images, the model may not perform well on those. Risks: Misclassification can lead to incorrect sorting of waste, which may affect recycling processes and waste management efficiency. Limitations: The model may not perform well in poor lighting conditions, with low-resolution images, or with objects that belong to multiple categories. ## Recommendations Use high-quality, well-lit images for classification. Continuously monitor and validate the model's performance in real-world scenarios. Be cautious when deploying the model in critical applications, and consider augmenting the dataset to improve performance on underrepresented categories. ## Training Details Training Dataset The training dataset is sourced from Kaggle and contains images classified into the following categories: Cardboard Glass Metal Paper Plastic Trash Battery Clothes Shoes Electronics ### Preprocessing: Before feeding the images into the model, the following preprocessing steps were applied: Resizing images to 224x224 pixels. Normalizing pixel values to the range [0, 1]. Applying data augmentation techniques such as rotation, flipping, and color jitter to increase the diversity of the training data. ### Training Configuration Optimizer: Adam Learning Rate: 0.001 Beta1: 0.9 Beta2: 0.999 Loss Function: CrossEntropyLoss Epochs: 5 Batch Size: 32 Learning Rate Scheduler: StepLR Step Size: 2 Gamma: 0.1 ## Evaluation The model was evaluated using a separate validation set, achieving the following performance metrics: Accuracy: 93% Precision: 92% Recall: 91% F1 Score: 91% Confusion matrices and ROC curves were also generated to provide deeper insights into the model's performance across different classes.

mahamadahmed/ser

mahamadahmed

2024-06-30T18:59:45Z

0

null

[ "region:us" ]

null

2024-06-30T18:59:44Z

Entry not found

Jaygeo067/llama-2-Trgoejay

Jaygeo067

2024-06-30T19:07:49Z

0

transformers

[ "transformers", "pytorch", "llama", "text-generation", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T19:02:44Z

Entry not found

maxseats/tmp

maxseats

2024-07-03T01:04:38Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-30T19:05:43Z

Entry not found

uma-wandb/my_video_model

uma-wandb

2024-07-01T04:07:45Z

0

transformers

[ "transformers", "safetensors", "videomae", "video-classification", "endpoints_compatible", "region:us" ]

video-classification

2024-06-30T19:06:26Z

Entry not found

habulaj/10820283115

habulaj

2024-06-30T19:08:23Z

0

null

[ "region:us" ]

null

2024-06-30T19:08:19Z

Entry not found

ZeZanZiet/blip_image_captioning_v1

ZeZanZiet

2024-07-01T04:27:36Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T19:09:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

maninderjit829/test2

maninderjit829

2024-06-30T19:12:58Z

0

null

[ "region:us" ]

null

2024-06-30T19:12:58Z

Entry not found

maninderjit829/xoxo

maninderjit829

2024-06-30T19:13:53Z

0

null

[ "region:us" ]

null

2024-06-30T19:13:53Z

Entry not found

chihli/llama-3-8b-chat-doctor-1

chihli

2024-07-01T10:15:09Z

0

transformers

[ "transformers", "safetensors", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

null

2024-06-30T19:18:15Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

habulaj/233173204625

habulaj

2024-06-30T19:18:39Z

0

null

[ "region:us" ]

null

2024-06-30T19:18:37Z

Entry not found

aadd77551/AI-test

aadd77551

2024-06-30T19:19:49Z

0

null

[ "region:us" ]

null

2024-06-30T19:19:49Z

Entry not found

habulaj/131864108578

habulaj

2024-06-30T19:21:13Z

0

null

[ "region:us" ]

null

2024-06-30T19:21:10Z

Entry not found

senagoksu/opus-mt-en-ro-finetuned-en-to-ro

senagoksu

2024-06-30T19:22:18Z

0

null

[ "region:us" ]

null

2024-06-30T19:22:18Z

Entry not found

Maarten1953/pegasus-samsum

Maarten1953

2024-06-30T20:29:22Z

0

transformers

[ "transformers", "safetensors", "pegasus", "text2text-generation", "generated_from_trainer", "dataset:samsum", "base_model:google/pegasus-cnn_dailymail", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-30T19:22:36Z

--- base_model: google/pegasus-cnn_dailymail tags: - generated_from_trainer datasets: - samsum model-index: - name: pegasus-samsum results: [] ---  # pegasus-samsum This model is a fine-tuned version of [google/pegasus-cnn_dailymail](https://huggingface.co/google/pegasus-cnn_dailymail) on the samsum dataset. It achieves the following results on the evaluation set: - Loss: 1.4844 ## 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: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 16 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 1.6719 | 0.5430 | 500 | 1.4844 | ### Framework versions - Transformers 4.41.1 - Pytorch 2.2.2 - Datasets 2.20.0 - Tokenizers 0.19.1

JuliusFx/merged_model_opt_exp

JuliusFx

2024-06-30T22:29:47Z

0

transformers

[ "transformers", "safetensors", "phi", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T19:23:04Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

aklein4/Qwen2-0.5B-tldr-dpo-1.0

aklein4

2024-06-30T19:26:48Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T19:25:57Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

thesanjeetc/qlora1

thesanjeetc

2024-06-30T19:27:23Z

0

transformers

[ "transformers", "safetensors", "text-generation-inference", "unsloth", "llama", "trl", "en", "base_model:unsloth/llama-3-8b-Instruct-bnb-4bit", "license:apache-2.0", "endpoints_compatible", "region:us" ]

null

2024-06-30T19:27:01Z

--- base_model: unsloth/llama-3-8b-Instruct-bnb-4bit language: - en license: apache-2.0 tags: - text-generation-inference - transformers - unsloth - llama - trl --- # Uploaded model - **Developed by:** thesanjeetc - **License:** apache-2.0 - **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library. [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

chirazman/my_awesome_qa_model

chirazman

2024-06-30T19:29:00Z

0

null

[ "region:us" ]

null

2024-06-30T19:29:00Z

Entry not found

vidula123/Llama-2-7b-chat-finetune-GGUF

vidula123

2024-06-30T21:50:57Z

0

adapter-transformers

[ "adapter-transformers", "text-generation", "en", "dataset:Vidulaae/sales_target", "dataset:Vidulaae/sales-analysis", "dataset:vidula123/Sales_Queries", "dataset:Vidulaae/demo-data", "dataset:Vidulaae/sales_analysis1", "license:llama2", "region:us" ]

text-generation

2024-06-30T19:31:14Z

--- license: llama2 datasets: - Vidulaae/sales_target - Vidulaae/sales-analysis - vidula123/Sales_Queries - Vidulaae/demo-data - Vidulaae/sales_analysis1 language: - en pipeline_tag: text-generation library_name: adapter-transformers ---

thisiskeithkwan/stanford-deidentifier-base-onnx

thisiskeithkwan

2024-06-30T19:38:57Z

0

transformers

[ "transformers", "onnx", "bert", "endpoints_compatible", "region:us" ]

null

2024-06-30T19:37:24Z

Entry not found

kartikay101/whisper-small-hi

kartikay101

2024-07-01T07:34:07Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "generated_from_trainer", "en", "dataset:Wtimit_vowel_consonent_mask_spec_aug", "base_model:openai/whisper-small", "license:apache-2.0", "model-index", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-30T19:37:56Z

--- language: - en license: apache-2.0 base_model: openai/whisper-small tags: - generated_from_trainer datasets: - Wtimit_vowel_consonent_mask_spec_aug metrics: - wer model-index: - name: Whisper Small Testing results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: Wtimit_vowel_consonent_mask_spec_aug type: Wtimit_vowel_consonent_mask_spec_aug metrics: - name: Wer type: wer value: 19.044740024183795 ---  # Whisper Small Testing This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Wtimit_vowel_consonent_mask_spec_aug dataset. It achieves the following results on the evaluation set: - Loss: 0.2954 - Wer: 19.0447 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 1e-06 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 100 - training_steps: 5000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:----:|:---------------:|:-------:| | 0.9758 | 0.0213 | 100 | 0.7484 | 18.7534 | | 0.2983 | 0.0426 | 200 | 0.3043 | 17.6871 | | 0.2397 | 0.0638 | 300 | 0.2875 | 17.1375 | | 0.2283 | 0.0851 | 400 | 0.2787 | 17.1320 | | 0.1904 | 0.1064 | 500 | 0.2780 | 16.7583 | | 0.1799 | 0.1277 | 600 | 0.2748 | 16.7033 | | 0.1782 | 0.1489 | 700 | 0.2748 | 16.7308 | | 0.1522 | 0.1702 | 800 | 0.2726 | 16.6154 | | 0.1326 | 0.1915 | 900 | 0.2687 | 16.4450 | | 0.138 | 0.2128 | 1000 | 0.2702 | 16.3351 | | 0.1317 | 0.2340 | 1100 | 0.2715 | 16.6978 | | 0.1312 | 0.2553 | 1200 | 0.2712 | 16.7748 | | 0.1222 | 0.2766 | 1300 | 0.2718 | 16.6209 | | 0.1181 | 0.2979 | 1400 | 0.2736 | 17.1870 | | 0.0975 | 0.3191 | 1500 | 0.2710 | 16.8352 | | 0.0795 | 0.3404 | 1600 | 0.2718 | 16.8352 | | 0.0791 | 0.3617 | 1700 | 0.2742 | 16.8847 | | 0.0822 | 0.3830 | 1800 | 0.2744 | 16.6758 | | 0.0734 | 0.4043 | 1900 | 0.2757 | 17.1155 | | 0.0896 | 0.4255 | 2000 | 0.2771 | 17.2749 | | 0.0578 | 0.4468 | 2100 | 0.2769 | 17.3299 | | 0.0727 | 0.4681 | 2200 | 0.2800 | 17.6652 | | 0.0691 | 0.4894 | 2300 | 0.2793 | 17.4893 | | 0.0656 | 0.5106 | 2400 | 0.2787 | 17.3574 | | 0.0726 | 0.5319 | 2500 | 0.2793 | 17.5662 | | 0.0494 | 0.5532 | 2600 | 0.2807 | 17.6487 | | 0.0635 | 0.5745 | 2700 | 0.2800 | 17.7091 | | 0.0503 | 0.5957 | 2800 | 0.2837 | 17.8026 | | 0.0688 | 0.6170 | 2900 | 0.2820 | 17.7531 | | 0.058 | 0.6383 | 3000 | 0.2858 | 18.1269 | | 0.051 | 0.6596 | 3100 | 0.2871 | 18.1159 | | 0.0535 | 0.6809 | 3200 | 0.2870 | 18.4951 | | 0.0665 | 0.7021 | 3300 | 0.2868 | 18.5776 | | 0.0497 | 0.7234 | 3400 | 0.2891 | 18.6105 | | 0.0558 | 0.7447 | 3500 | 0.2891 | 18.5446 | | 0.0384 | 0.7660 | 3600 | 0.2891 | 18.6820 | | 0.0413 | 0.7872 | 3700 | 0.2908 | 18.7369 | | 0.0562 | 0.8085 | 3800 | 0.2916 | 18.6655 | | 0.0523 | 0.8298 | 3900 | 0.2920 | 18.6600 | | 0.043 | 0.8511 | 4000 | 0.2928 | 18.7260 | | 0.0463 | 0.8723 | 4100 | 0.2926 | 18.6765 | | 0.0517 | 0.8936 | 4200 | 0.2942 | 18.7809 | | 0.0408 | 0.9149 | 4300 | 0.2950 | 18.7644 | | 0.0362 | 0.9362 | 4400 | 0.2954 | 18.8799 | | 0.047 | 0.9574 | 4500 | 0.2954 | 18.9623 | | 0.0347 | 0.9787 | 4600 | 0.2954 | 19.0118 | | 0.0404 | 1.0 | 4700 | 0.2956 | 19.0392 | | 0.0559 | 1.0213 | 4800 | 0.2956 | 19.0063 | | 0.0462 | 1.0426 | 4900 | 0.2956 | 19.1162 | | 0.0385 | 1.0638 | 5000 | 0.2954 | 19.0447 | ### Framework versions - Transformers 4.42.3 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

fbaldassarri/modello-italia-9B-GGUF

fbaldassarri

2024-07-02T19:26:23Z

0

transformers

[ "transformers", "gguf", "gpt_neox", "text-generation", "pytorch", "conversational", "it", "base_model:sapienzanlp/modello-italia-9b", "license:mit", "autotrain_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T19:38:25Z

--- language: - it license: mit tags: - pytorch model_name: Modello Italia 9B base_model: sapienzanlp/modello-italia-9b inference: false model_creator: iGeniusAI model_type: gpt-neonx pipeline_tag: text-generation prompt_template: '{prompt} ' quantized_by: fbaldassarri --- # Model Card for Modello Italia 9B GGUFs This an UNOFFICIAL GGUF format model files repository for converted/quantized OFFICIAL model checkpoint of *"Modello Italia 9B"*, Large Language Model (LLM) developed by [iGenius](https://it.igenius.ai/) in collaboration with [CINECA](https://www.cineca.it/). * More information about Modello Italia: [click here](https://it.igenius.ai/language-models). ## 🚨 Disclaimers * This is an UNOFFICIAL quantization of the OFFICIAL model checkpoint released by iGenius. * This model is based also on the conversion made for HF Transformers by [Sapienza NLP, Sapienza University of Rome](https://huggingface.co/sapienzanlp). * The original model was developed using LitGPT, therefore, the weights need to be converted before they can be used with Hugging Face transformers. ## 🚨 Terms and Conditions * **Note:** By using this model, you accept the iGenius' [**terms and conditions**](https://secure.igenius.ai/legal/italia_terms_and_conditions.pdf). ### 🚨 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. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. Here is an incomplate list of clients and libraries that are known to support GGUF: * [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option. * [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration. * [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling. * [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. * [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection. * [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration. * [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. * [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server. * [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use. ## 🚨 Compatibility These quantised GGUFv2 files are compatible with llama.cpp from August 27th 2023 onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) They are also compatible with many third party UIs and libraries - please see the list at the top of this README. ## 🚨 Reproducibility This model has been converted/quantized using Intel [neural-speed](https://github.com/intel/neural-speed/). ## 🚨 Biases and Risks From the terms and conditions of iGenius for Modello Italia: > Modello Italia è concepito per essere utilizzato da tutti e per adattarsi a una vasta gamma di casi d'uso. È stato progettato con l'obiettivo di essere accessibile a persone provenienti da background, esperienze e prospettive diverse. Modello Italia si rivolge agli utenti e alle loro esigenze senza inserire giudizi superflui o normative, riconoscendo al contempo che anche contenuti potenzialmente problematici in determinati contesti possono avere scopi validi in altri. Il rispetto per la dignità e l'autonomia di tutti gli utenti, specialmente in termini di libertà di pensiero ed espressione, è un pilastro fondamentale del suo design. Tuttavia, essendo una nuova tecnologia, Modello Italia comporta rischi legati al suo utilizzo. I test condotti finora sono stati eseguiti in italiano e non hanno potuto coprire tutte le possibili situazioni. Pertanto, come per tutti gli LLM, non è possibile prevedere in anticipo gli output di Modello Italia e il modello potrebbe in alcuni casi generare risposte imprecise, tendenziose o altre risposte discutibili. Prima di utilizzare Modello Italia in qualsiasi contesto, gli sviluppatori sono fortemente incoraggiati a eseguire test di sicurezza e adattamento specifici per le loro applicazioni. We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data. For more information about this issue, please refer to our survey paper: * [Biases in Large Language Models: Origins, Inventory, and Discussion](https://dl.acm.org/doi/full/10.1145/3597307) ## Model architecture * The model architecture is **based on GPT-NeoX**.

silveroxides/Vision_8B_Uncensored_4bit

silveroxides

2024-06-30T19:38:53Z

0

null

[ "region:us" ]

null

2024-06-30T19:38:53Z

Entry not found

aklein4/Qwen2-0.5B-tldr-dro-binary-1.0

aklein4

2024-06-30T19:40:21Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T19:39:20Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

Anujgr8/Whisper-Anuj-Medum-Medium-lalo

Anujgr8

2024-07-01T06:18:14Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "whisper", "automatic-speech-recognition", "arxiv:1910.09700", "endpoints_compatible", "region:us" ]

automatic-speech-recognition

2024-06-30T19:40:45Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

ewjfwejfoiwe/Unet_GAN_Self-driving-car-vision-segmentation

ewjfwejfoiwe

2024-06-30T19:45:49Z

0

null

[ "license:mit", "region:us" ]

null

2024-06-30T19:42:30Z

--- license: mit ---

talhaturab/my-first-model

talhaturab

2024-06-30T19:46:37Z

0

diffusers

[ "diffusers", "safetensors", "pytorch", "stable-diffusion", "text-to-image", "diffusion-models-class", "dreambooth-hackathon", "animal", "license:creativeml-openrail-m", "autotrain_compatible", "endpoints_compatible", "diffusers:StableDiffusionPipeline", "region:us" ]

text-to-image

2024-06-30T19:44:40Z

--- license: creativeml-openrail-m tags: - pytorch - diffusers - stable-diffusion - text-to-image - diffusion-models-class - dreambooth-hackathon - animal widget: - text: a photo of with face of talhamax cat and body of a elephant --- # DreamBooth model for the custom finetunig concept trained by talha on the max_cat dataset. This is a Stable Diffusion model fine-tuned on the max-pics concept with DreamBooth. It can be used by modifying the `instance_prompt`: **a photo of talhamax cat** This model was created as part of the DreamBooth Hackathon 🔥. Visit the [organisation page](https://huggingface.co/dreambooth-hackathon) for instructions on how to take part! ## Description This is demo model ## Usage ```python from diffusers import StableDiffusionPipeline pipeline = StableDiffusionPipeline.from_pretrained('talhaturab/my-first-model') image = pipeline().images[0] image ```

2052man/vira-reservation

2052man

2024-06-30T19:47:30Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T19:47:30Z

--- license: apache-2.0 ---

LowFace/newtest

LowFace

2024-06-30T19:53:01Z

0

null

[ "region:us" ]

null

2024-06-30T19:53:01Z

Entry not found

erayyapagci/multilingual-e5-onnx-vespa

erayyapagci

2024-06-30T20:06:31Z

0

null

[ "onnx", "region:us" ]

null

2024-06-30T19:54:45Z

Entry not found

senagoksu/t5-small-finetuned-xsum

senagoksu

2024-07-01T11:36:19Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "t5", "text2text-generation", "generated_from_trainer", "dataset:xsum", "base_model:t5-small", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text2text-generation

2024-06-30T19:58:02Z

--- license: apache-2.0 base_model: t5-small tags: - generated_from_trainer datasets: - xsum metrics: - rouge model-index: - name: t5-small-finetuned-xsum results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: xsum type: xsum config: default split: validation args: default metrics: - name: Rouge1 type: rouge value: 27.9257 ---  # t5-small-finetuned-xsum This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the xsum dataset. It achieves the following results on the evaluation set: - Loss: 2.5074 - Rouge1: 27.9257 - Rouge2: 7.4618 - Rougel: 21.9338 - Rougelsum: 21.9405 - Gen Len: 18.8176 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 0.5 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len | |:-------------:|:------:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:| | 2.817 | 0.0784 | 500 | 2.5683 | 26.6596 | 6.6324 | 20.7701 | 20.7761 | 18.8057 | | 2.8029 | 0.1568 | 1000 | 2.5435 | 27.1558 | 6.9694 | 21.2178 | 21.2216 | 18.7999 | | 2.7797 | 0.2352 | 1500 | 2.5270 | 27.5528 | 7.2608 | 21.621 | 21.6233 | 18.7982 | | 2.7651 | 0.3137 | 2000 | 2.5165 | 27.6104 | 7.2896 | 21.6928 | 21.7012 | 18.8133 | | 2.7514 | 0.3921 | 2500 | 2.5112 | 27.8452 | 7.3791 | 21.8632 | 21.8659 | 18.8118 | | 2.7463 | 0.4705 | 3000 | 2.5074 | 27.9257 | 7.4618 | 21.9338 | 21.9405 | 18.8176 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.0+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1

Tristan7234/Emma

Tristan7234

2024-06-30T19:59:04Z

0

null

[ "license:mit", "region:us" ]

null

2024-06-30T19:59:04Z

--- license: mit ---

Samiyar/Teste

Samiyar

2024-06-30T19:59:08Z

0

null

[ "license:openrail", "region:us" ]

null

2024-06-30T19:59:08Z

--- license: openrail ---

adamkarvonen/othello-saes

adamkarvonen

2024-06-30T20:10:00Z

0

null

[ "region:us" ]

null

2024-06-30T20:02:15Z

Entry not found

ANDREBARRETOLOPES/Andre

ANDREBARRETOLOPES

2024-06-30T20:03:46Z

0

null

[ "license:apache-2.0", "region:us" ]

null

2024-06-30T20:03:46Z

--- license: apache-2.0 ---

ralphkalweit/Reinforce-PixelCopter

ralphkalweit

2024-06-30T20:28:11Z

0

null

[ "Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class", "model-index", "region:us" ]

reinforcement-learning

2024-06-30T20:04:26Z

--- tags: - Pixelcopter-PLE-v0 - reinforce - reinforcement-learning - custom-implementation - deep-rl-class model-index: - name: Reinforce-PixelCopter results: - task: type: reinforcement-learning name: reinforcement-learning dataset: name: Pixelcopter-PLE-v0 type: Pixelcopter-PLE-v0 metrics: - type: mean_reward value: 8.30 +/- 9.60 name: mean_reward verified: false --- # **Reinforce** Agent playing **Pixelcopter-PLE-v0** This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** . To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction

Litzy619/MIS0630T2

Litzy619

2024-07-01T02:36:00Z

0

null

[ "region:us" ]

null

2024-06-30T20:05:43Z

Entry not found

random2344/vector2

random2344

2024-06-30T20:09:07Z

0

null

[ "region:us" ]

null

2024-06-30T20:08:48Z

Entry not found

sindhujag26/distilbert-base-uncased-finetuned-ner

sindhujag26

2024-07-01T12:59:17Z

0

transformers

[ "transformers", "tensorboard", "safetensors", "distilbert", "token-classification", "generated_from_trainer", "base_model:distilbert-base-uncased", "license:apache-2.0", "autotrain_compatible", "endpoints_compatible", "region:us" ]

token-classification

2024-06-30T20:09:58Z

--- license: apache-2.0 base_model: distilbert-base-uncased tags: - generated_from_trainer metrics: - precision - recall - f1 - accuracy model-index: - name: distilbert-base-uncased-finetuned-ner results: [] ---  # distilbert-base-uncased-finetuned-ner This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.0020 - Precision: 1.0 - Recall: 1.0 - F1: 1.0 - Accuracy: 1.0 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - 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 | Precision | Recall | F1 | Accuracy | |:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:---:|:--------:| | No log | 1.0 | 50 | 0.0208 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 2.0 | 100 | 0.0027 | 1.0 | 1.0 | 1.0 | 1.0 | | No log | 3.0 | 150 | 0.0020 | 1.0 | 1.0 | 1.0 | 1.0 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.1.2+cpu - Datasets 2.19.2 - Tokenizers 0.19.1

pedroharaujo/emma_lora

pedroharaujo

2024-06-30T23:18:49Z

0

null

[ "region:us" ]

null

2024-06-30T20:10:05Z

Entry not found

lit9003code/melotts300

lit9003code

2024-06-30T20:14:11Z

0

null

[ "region:us" ]

null

2024-06-30T20:13:50Z

Entry not found

lit9003code/melotts301

lit9003code

2024-06-30T20:15:46Z

0

null

[ "region:us" ]

null

2024-06-30T20:15:25Z

Entry not found

lit9003code/melotts302

lit9003code

2024-06-30T20:18:16Z

0

null

[ "region:us" ]

null

2024-06-30T20:17:01Z

Entry not found

lit9003code/melotts303

lit9003code

2024-06-30T20:20:01Z

0

null

[ "region:us" ]

null

2024-06-30T20:19:38Z

Entry not found

lit9003code/melotts304

lit9003code

2024-06-30T20:21:34Z

0

null

[ "region:us" ]

null

2024-06-30T20:21:14Z

Entry not found

AnotherNN/Losk

AnotherNN

2024-06-30T20:23:12Z

0

null

[ "region:us" ]

null

2024-06-30T20:21:25Z

Entry not found

Moriacrafter/Qwen1.5-0.5B-8bit_DepressionDetection

Moriacrafter

2024-06-30T20:22:42Z

0

transformers

[ "transformers", "safetensors", "qwen2", "text-generation", "llama-factory", "conversational", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T20:22:11Z

--- library_name: transformers tags: - llama-factory --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lit9003code/melotts305

lit9003code

2024-06-30T20:23:11Z

0

null

[ "region:us" ]

null

2024-06-30T20:22:49Z

Entry not found

FartLabs/FART_SMILES_tokenized_PubChem_shard00_160k_augmented

FartLabs

2024-06-30T20:23:23Z

0

transformers

[ "transformers", "safetensors", "roberta", "text-classification", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text-classification

2024-06-30T20:23:12Z

--- library_name: transformers tags: [] --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

lit9003code/melotts306

lit9003code

2024-06-30T20:25:38Z

0

null

[ "region:us" ]

null

2024-06-30T20:24:29Z

Entry not found

iceman2434/xlm-roberta-base-ft-udpos213-top2langrandom

iceman2434

2024-06-30T20:32:46Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:26:33Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans & Norwegian corpora (Top 2 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.58\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

lit9003code/melotts307

lit9003code

2024-06-30T20:27:21Z

0

null

[ "region:us" ]

null

2024-06-30T20:26:58Z

Entry not found

aadd77551/test

aadd77551

2024-06-30T20:27:40Z

0

null

[ "region:us" ]

null

2024-06-30T20:27:40Z

Entry not found

iceman2434/xlm-roberta-base-ft-udpos213-top3langrandom

iceman2434

2024-06-30T20:33:12Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:28:23Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Combined Afrikaans, Norwegian, & Vietnamese corpora (Top 3 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 75.29\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

lit9003code/melotts308

lit9003code

2024-06-30T20:28:58Z

0

null

[ "region:us" ]

null

2024-06-30T20:28:38Z

Entry not found

asafi/Meta-Llama-3-medical-8B-merged

asafi

2024-06-30T20:34:37Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "unsloth", "trl", "sft", "arxiv:1910.09700", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T20:29:22Z

--- library_name: transformers tags: - unsloth - trl - sft --- # Model Card for Model ID  ## Model Details ### Model Description  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]  - **Repository:** [More Information Needed] - **Paper [optional]:** [More Information Needed] - **Demo [optional]:** [More Information Needed] ## Uses  ### Direct Use  [More Information Needed] ### Downstream Use [optional]  [More Information Needed] ### Out-of-Scope Use  [More Information Needed] ## Bias, Risks, and Limitations  [More Information Needed] ### Recommendations  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  [More Information Needed] ### Training Procedure  #### Preprocessing [optional] [More Information Needed] #### Training Hyperparameters - **Training regime:** [More Information Needed]  #### Speeds, Sizes, Times [optional]  [More Information Needed] ## Evaluation  ### Testing Data, Factors & Metrics #### Testing Data  [More Information Needed] #### Factors  [More Information Needed] #### Metrics  [More Information Needed] ### Results [More Information Needed] #### Summary ## Model Examination [optional]  [More Information Needed] ## Environmental Impact  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]  **BibTeX:** [More Information Needed] **APA:** [More Information Needed] ## Glossary [optional]  [More Information Needed] ## More Information [optional] [More Information Needed] ## Model Card Authors [optional] [More Information Needed] ## Model Card Contact [More Information Needed]

iceman2434/xlm-roberta-base-ft-udpos213-top4langrandom

iceman2434

2024-06-30T20:33:33Z

0

null

[ "token-classification", "tl", "dataset:universal_dependencies", "region:us" ]

token-classification

2024-06-30T20:30:11Z

--- datasets: - universal_dependencies language: - tl metrics: - f1 pipeline_tag: token-classification --- ## Model Specification - Model: XLM-RoBERTa (base-sized model) - Randomized training order of languages - Training Data: - Afrikaans, Norwegian, Vietnamese, & Hebrew (Top 4 Languages) - Training Details: - Base configurations with learning rate 5e-5 ## Evaluation - Evaluation Dataset: Universal Dependencies Tagalog Ugnayan (Testing Set) - Tested in a zero-shot cross-lingual scenario on a Universal Dependencies Tagalog Ugnayan testing dataset (with 77.55\% Accuracy) ## POS Tags - ADJ – ADP – ADV – CCONJ – DET – INTJ – NOUN – NUM – PART – PRON – PROPN – PUNCT – SCONJ – VERB

lit9003code/melotts309

lit9003code

2024-06-30T20:31:28Z

0

null

[ "region:us" ]

null

2024-06-30T20:30:15Z

Entry not found

DimensionSTP/Llama-3-KoEn-8B-Instruct-preview-scientificQA

DimensionSTP

2024-06-30T20:39:13Z

0

transformers

[ "transformers", "safetensors", "llama", "text-generation", "facebook", "meta", "pytorch", "llama-3", "llama-3-ko", "conversational", "en", "ko", "license:cc-by-nc-sa-4.0", "autotrain_compatible", "endpoints_compatible", "text-generation-inference", "region:us" ]

text-generation

2024-06-30T20:30:25Z

--- language: - en - ko license: cc-by-nc-sa-4.0 tags: - facebook - meta - pytorch - llama - llama-3 - llama-3-ko pipeline_tag: text-generation license_name: llama3 license_link: LICENSE --- ## Model Details **This model is fine-tuned by beomi/Llama-3-KoEn-8B-Instruct-preview** **Fine-tuning dataset: Scientific QA dataset** ```

tarsssss/finetuned-kde4-pt-to-ca-2

tarsssss

2024-07-01T04:16:21Z

0

transformers

[ "transformers", "safetensors", "marian", "text2text-generation", "generated_from_trainer", "dataset:open_subtitles", "base_model:Helsinki-NLP/opus-mt-pt-ca", "license:apache-2.0", "model-index", "autotrain_compatible", "endpoints_compatible", "region:us" ]

text2text-generation

2024-06-30T20:31:02Z

--- license: apache-2.0 base_model: Helsinki-NLP/opus-mt-pt-ca tags: - generated_from_trainer datasets: - open_subtitles metrics: - bleu model-index: - name: finetuned-kde4-pt-to-ca-2 results: - task: name: Sequence-to-sequence Language Modeling type: text2text-generation dataset: name: open_subtitles type: open_subtitles config: ca-pt split: train args: ca-pt metrics: - name: Bleu type: bleu value: 35.718810961905895 ---  # finetuned-kde4-pt-to-ca-2 This model is a fine-tuned version of [Helsinki-NLP/opus-mt-pt-ca](https://huggingface.co/Helsinki-NLP/opus-mt-pt-ca) on the open_subtitles dataset. It achieves the following results on the evaluation set: - Loss: 0.0943 - Bleu: 35.7188 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 2e-05 - train_batch_size: 32 - eval_batch_size: 64 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - num_epochs: 3 - mixed_precision_training: Native AMP ### Training results ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1