# food_not_food_text_classifier by Harsh Deep Pandey Source: https://savrn.com/models/food-not-food-text-classifier Markdown alternate of the page above; the site index is https://savrn.com/llms.txt --- ## Runs On What it takes to serve food_not_food_text_classifier (67M parameters): the memory its weights need at each precision, and the cheapest way to rent enough data-center GPUs to hold them. | Precision | Weights | Memory needed | Cheapest setup | Per hour | Also fits | | --- | --- | --- | --- | --- | --- | | 16-bit | 0.1 GB | 0.2 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 8-bit | 0.1 GB | 0.1 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | | 4-bit | 0.0 GB | 0.0 GB | 1x [MI300X](https://savrn.com/ai-index/pricing/gpus/mi300x) (192 GB) Vultr | $1.85 | [1x H100](https://savrn.com/ai-index/pricing/gpus/h100) $1.99 · [1x MI325X](https://savrn.com/ai-index/pricing/gpus/mi325x) $2.00 | Memory is the weights at that precision plus 20% for the runtime and a short context; a long context needs more. Prices are the lowest on-demand hourly rates in the [SAVRN Index](https://savrn.com/ai-index/pricing/gpus), read Oct 9, 2026. [food_not_food_text_classifier on every accelerator the SAVRN Index prices, at every precision](https://savrn.com/models/food-not-food-text-classifier/gpus) ## Model Card By Harsh Deep Pandey, published under apache-2.0, revision 61c469da03d4. This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 5.0.1 - Tokenizers 0.23.2 Read Harsh Deep Pandey's full model card This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://savrn.com/models/distilbert-base-uncased) on an unknown dataset. It achieves the following results on the evaluation set: - Loss: 0.0006 - 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: 0.0001 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments - lr_scheduler_type: linear - num_epochs: 10 #### Training results | Training Loss | Epoch | Step | Validation Loss | Accuracy | | --- | --- | --- | --- | --- | | 0.3711 | 1.0 | 7 | 0.0538 | 1.0 | | 0.0247 | 2.0 | 14 | 0.0062 | 1.0 | | 0.0044 | 3.0 | 21 | 0.0022 | 1.0 | | 0.0019 | 4.0 | 28 | 0.0013 | 1.0 | | 0.0012 | 5.0 | 35 | 0.0009 | 1.0 | | 0.0010 | 6.0 | 42 | 0.0008 | 1.0 | | 0.0008 | 7.0 | 49 | 0.0007 | 1.0 | | 0.0007 | 8.0 | 56 | 0.0006 | 1.0 | | 0.0007 | 9.0 | 63 | 0.0006 | 1.0 | | 0.0007 | 10.0 | 70 | 0.0006 | 1.0 | #### Framework versions - Transformers 5.17.0 - Pytorch 2.11.0+cu130 - Datasets 5.0.1 - Tokenizers 0.23.2 ## Configuration Architecture DistilBertForSequenceClassification Context length (tokens) 512 Vocabulary size 30,522 Model type distilbert ## Identity and Version Repository harshdpandey/food_not_food_text_classifier Publisher Harsh Deep Pandey Task Text classification Modality Text Library transformers Parameters 67M parameters Languages Not stated by the source Revision 61c469da03d4ce272691dccd45cc750ff09f0b0d First published 2026-09-28 Last updated 2026-10-02 ## Files and Weights 5 files, 267.8 MB in total. The weights are 2 files totalling 267.8 MB in bin, safetensors. Weights2 files · 267.8 MB Configuration1 file · 776 B Documentation1 file · 2.1 KB Repository1 file · 1.5 KB Every file | File | Type | Size | SHA-256 | | --- | --- | --- | --- | | model.safetensors | Weights | 267.8 MB | 05906ae57b2e | | training_args.bin | Weights | 5.2 KB | 12688057a520 | | config.json | Configuration | 776 B | — | | README.md | Documentation | 2.1 KB | — | | .gitattributes | Repository | 1.5 KB | — | ## License and Download License apache-2.0 Access Open weights, no gate Download size 267.8 MB [Download from Harsh Deep Pandey](https://huggingface.co/harshdpandey/food_not_food_text_classifier) Released by Harsh Deep Pandey through its official repository on Hugging Face. [Read the license](https://www.apache.org/licenses/LICENSE-2.0). ## Built From - Derived from [distilbert/distilbert-base-uncased](https://savrn.com/models/distilbert-base-uncased) ## Memory Requirements | Precision | Weights in memory | | --- | --- | | As published | 267.8 MB | | 16-bit | 0.1 GB | | 8-bit | 0.1 GB | | 4-bit | 0.0 GB | Weights only, from the published parameter count; the key-value cache and runtime add to this. ## Questions About food_not_food_text_classifier ### How much GPU memory does food_not_food_text_classifier need? About 0.2 GB at 16-bit and 0 GB at 4-bit: the weights (67M parameters) plus a working margin. A long context needs more. ### What is the cheapest GPU to run food_not_food_text_classifier on? At 16-bit, 1x MI300X from $1.85 an hour; at 4-bit, 1x MI300X from $1.85 an hour, at the lowest on-demand prices the SAVRN Index lists. ### Can I use food_not_food_text_classifier commercially? Yes. food_not_food_text_classifier is released under Apache License 2.0. The Apache License 2.0 is a permissive open-source license. It permits commercial use, modification and redistribution. It requires keeping the license and copyright notices and any NOTICE file, stating significant changes, and it includes an express patent grant from contributors. ### What is food_not_food_text_classifier's context length? 512 tokens, from the maximum position embeddings in its published configuration. ## Similar Models Model · Text classification ### [distilbert-base-uncased-finetuned-sst-2-english](https://savrn.com/models/distilbert-base-uncased-finetuned-sst-2-english) [DistilBERT community](https://savrn.com/model-publishers/distilbert) This model is a fine-tune checkpoint of DistilBERT-base-uncased, fine-tuned on SST-2. This model reaches an accuracy of 91.3 on the dev set (for comparison, Bert bert-base-uncased version reaches an accuracy of 92.7). This model can be used for topic classification. You can use the raw model for either masked language modeling or next sentence prediction, but it's mostly intended to be fine-tuned on a downstream task. See the model hub to look for fine-tuned versions on a task that interests you. The model should not be used to intentionally create hostile or alienating environments for people. In addition, the model was not trained to be factual or true representations of people or events… Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-base-uncased-finetuned-sst-2-english) Model · Text classification ### [Food_not_food_classificer-distilbert-base-uncased](https://savrn.com/models/food-not-food-classificer-distilbert-base-uncased) [Avnay Gandhi](https://savrn.com/model-publishers/avnay) This model is a fine-tuned version of distilbert-base-uncased on the None dataset. The following hyperparameters were used during training: - learningrate: 3e-05 - trainbatchsize: 8 - evalbatchsize: 8 - lrschedulertype: linear - numepochs: 10 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 5.0.1 - Tokenizers 0.23.1 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/food-not-food-classificer-distilbert-base-uncased) Model · Text classification ### [imdb-distilbert-sentiment-v2](https://savrn.com/models/imdb-distilbert-sentiment-v2) [Noura Helaly](https://savrn.com/model-publishers/nourahelaly56) This is the model card of a transformers model that has been pushed on the Hub. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. Use the code below to get started with the model. Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019). Open weights 67M parameters 512 tokens transformers [View model](https://savrn.com/models/imdb-distilbert-sentiment-v2) Model · Text classification ### [food_not_food_text_classifier-distilbert-base-uncased](https://savrn.com/models/food-not-food-text-classifier-distilbert-base-uncased) [Prathamesh Kate](https://savrn.com/model-publishers/prathameshkate15) This model is a fine-tuned version of distilbert/distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.16.1 - Pytorch 2.11.0+cu128 - Datasets 4.8.5 - Tokenizers 0.23.1 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/food-not-food-text-classifier-distilbert-base-uncased) Model · Text classification ### [ami-addressee-distilbert](https://savrn.com/models/ami-addressee-distilbert) [Josh Estrada](https://savrn.com/model-publishers/acloudcenter) This model is a fine-tuned version of distilbert-base-uncased on the None dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 2e-05 - trainbatchsize: 32 - evalbatchsize: 64 - lrschedulertype: linear - lrschedulerwarmupratio: 0.1 - numepochs: 3 - Transformers 4.57.6 - Pytorch 2.14.0+cu130 - Datasets 5.0.1 - Tokenizers 0.22.2 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/ami-addressee-distilbert) Model · Text classification ### [distilbert-food-not-food](https://savrn.com/models/distilbert-food-not-food) [Saber Bounehas](https://savrn.com/model-publishers/saberbounehas) This model is a fine-tuned version of distilbert-base-uncased on an unknown dataset. It achieves the following results on the evaluation set: The following hyperparameters were used during training: - learningrate: 0.0001 - trainbatchsize: 32 - evalbatchsize: 32 - lrschedulertype: linear - numepochs: 10 - Transformers 5.18.0 - Pytorch 2.11.0+cpu - Datasets 5.0.1 - Tokenizers 0.23.2 Open weights apache-2.0 67M parameters 512 tokens transformers [View model](https://savrn.com/models/distilbert-food-not-food) ## Harsh Deep Pandey [All models and datasets](https://savrn.com/model-publishers/harshdpandey) ## Versions - [61c469da03d4](https://savrn.com/models/food-not-food-text-classifier/versions/61c469da03d4) · current 2026-10-02 - [9ad4babe4853](https://savrn.com/models/food-not-food-text-classifier/versions/9ad4babe4853) 2026-09-29 ## Explore More - [All text classification models](https://savrn.com/models/tasks/text-classification) - [All models under apache-2.0](https://savrn.com/models/licenses/apache-2-0) - [Model comparisons](https://savrn.com/models/comparisons) - [The model directory](https://savrn.com/models) - [Open model prices by host](https://savrn.com/ai-index/pricing/open-models) ## Source - Repository metadata, read 2026-10-02. - [Hugging Face record](https://huggingface.co/harshdpandey/food_not_food_text_classifier) - [How the hub is built](https://savrn.com/model-hub/methodology)