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	<meta charset="utf-8" /><meta name="hf:doc:metadata" content="{"title":"Text Regression","local":"text-regression","sections":[{"title":"Data Format","local":"data-format","sections":[],"depth":2},{"title":"Columns","local":"columns","sections":[{"title":"Params","local":"params","sections":[],"depth":3}],"depth":2}],"depth":1}">
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	<link rel="modulepreload" href="/docs/autotrain/pr_749/en/_app/immutable/chunks/EditOnGithub.48fa589f.js"><!-- HEAD_svelte-u9bgzb_START --><meta name="hf:doc:metadata" content="{"title":"Text Regression","local":"text-regression","sections":[{"title":"Data Format","local":"data-format","sections":[],"depth":2},{"title":"Columns","local":"columns","sections":[{"title":"Params","local":"params","sections":[],"depth":3}],"depth":2}],"depth":1}"><!-- HEAD_svelte-u9bgzb_END --> <p></p> <h1 class="relative group"><a id="text-regression" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#text-regression"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Text Regression</span></h1> <p data-svelte-h="svelte-m0pa8y">Training a text regression model with AutoTrain is super-easy! Get your data ready in
	proper format and then with just a few clicks, your state-of-the-art model will be ready to
	be used in production.</p> <h2 class="relative group"><a id="data-format" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#data-format"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Data Format</span></h2> <p data-svelte-h="svelte-11bnrsv">Let’s train a model for scoring a movie review on a scale of 1-5. The data should be
	in the following CSV format:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->text,target
	<span class="hljs-string">"this movie is great"</span>,<span class="hljs-number">5</span>
	<span class="hljs-string">"this movie is bad"</span>,<span class="hljs-number">1</span>
	.
	.
	.<!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1yj7xug">As you can see, we have two columns in the CSV file. One column is the text and the other
	is the label. The label can be any float or int.</p> <p data-svelte-h="svelte-bmzoma">If your CSV is huge, you can divide it into multiple CSV files and upload them separately.
	Please make sure that the column names are the same in all CSV files.</p> <p data-svelte-h="svelte-1pa5x49">One way to divide the CSV file using pandas is as follows:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> pandas <span class="hljs-keyword">as</span> pd

	<span class="hljs-comment"># Set the chunk size</span>
	chunk_size = <span class="hljs-number">1000</span>
	i = <span class="hljs-number">1</span>

	<span class="hljs-comment"># Open the CSV file and read it in chunks</span>
	<span class="hljs-keyword">for</span> chunk <span class="hljs-keyword">in</span> pd.read_csv(<span class="hljs-string">'example.csv'</span>, chunksize=chunk_size):
	<span class="hljs-comment"># Save each chunk to a new file</span>
	chunk.to_csv(<span class="hljs-string">f'chunk_<span class="hljs-subst">{i}</span>.csv'</span>, index=<span class="hljs-literal">False</span>)
	i += <span class="hljs-number">1</span><!-- HTML_TAG_END --></pre></div> <p data-svelte-h="svelte-1bj4tlz">Instead of CSV you can also use JSONL format. The JSONL format should be as follows:</p> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START --><span class="hljs-punctuation">{</span><span class="hljs-attr">"text"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"this movie is great"</span><span class="hljs-punctuation">,</span> <span class="hljs-attr">"target"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">5</span><span class="hljs-punctuation">}</span>
	<span class="hljs-punctuation">{</span><span class="hljs-attr">"text"</span><span class="hljs-punctuation">:</span> <span class="hljs-string">"this movie is bad"</span><span class="hljs-punctuation">,</span> <span class="hljs-attr">"target"</span><span class="hljs-punctuation">:</span> <span class="hljs-number">1</span><span class="hljs-punctuation">}</span>
	.
	.
	.<!-- HTML_TAG_END --></pre></div> <h2 class="relative group"><a id="columns" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#columns"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Columns</span></h2> <p data-svelte-h="svelte-1vb53ci">Your CSV dataset must have two columns: <code>text</code> and <code>target</code>.</p> <h3 class="relative group"><a id="params" class="header-link block pr-1.5 text-lg no-hover:hidden with-hover:absolute with-hover:p-1.5 with-hover:opacity-0 with-hover:group-hover:opacity-100 with-hover:right-full" href="#params"><span><svg class="" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 256 256"><path d="M167.594 88.393a8.001 8.001 0 0 1 0 11.314l-67.882 67.882a8 8 0 1 1-11.314-11.315l67.882-67.881a8.003 8.003 0 0 1 11.314 0zm-28.287 84.86l-28.284 28.284a40 40 0 0 1-56.567-56.567l28.284-28.284a8 8 0 0 0-11.315-11.315l-28.284 28.284a56 56 0 0 0 79.196 79.197l28.285-28.285a8 8 0 1 0-11.315-11.314zM212.852 43.14a56.002 56.002 0 0 0-79.196 0l-28.284 28.284a8 8 0 1 0 11.314 11.314l28.284-28.284a40 40 0 0 1 56.568 56.567l-28.285 28.285a8 8 0 0 0 11.315 11.314l28.284-28.284a56.065 56.065 0 0 0 0-79.196z" fill="currentColor"></path></svg></span></a> <span>Params</span></h3> <div class="code-block relative"><div class="absolute top-2.5 right-4"><button class="inline-flex items-center relative text-sm focus:text-green-500 cursor-pointer focus:outline-none transition duration-200 ease-in-out opacity-0 mx-0.5 text-gray-600 " title="code excerpt" type="button"><svg class="" xmlns="http://www.w3.org/2000/svg" aria-hidden="true" fill="currentColor" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M28,10V28H10V10H28m0-2H10a2,2,0,0,0-2,2V28a2,2,0,0,0,2,2H28a2,2,0,0,0,2-2V10a2,2,0,0,0-2-2Z" transform="translate(0)"></path><path d="M4,18H2V4A2,2,0,0,1,4,2H18V4H4Z" transform="translate(0)"></path><rect fill="none" width="32" height="32"></rect></svg> <div class="absolute pointer-events-none transition-opacity bg-black text-white py-1 px-2 leading-tight rounded font-normal shadow left-1/2 top-full transform -translate-x-1/2 translate-y-2 opacity-0"><div class="absolute bottom-full left-1/2 transform -translate-x-1/2 w-0 h-0 border-black border-4 border-t-0" style="border-left-color: transparent; border-right-color: transparent; "></div> Copied</div></button></div> <pre class=""><!-- HTML_TAG_START -->❯ autotrain <span class="hljs-literal">text</span>-regression <span class="hljs-comment">--help</span>
	usage: autotrain <command> [<args>] <span class="hljs-literal">text</span>-regression [-h] [<span class="hljs-comment">--train] [--deploy] [--inference] [--username USERNAME]</span>
	[<span class="hljs-comment">--backend {local-cli,spaces-a10gl,spaces-a10gs,spaces-a100,spaces-t4m,spaces-t4s,spaces-cpu,spaces-cpuf}]</span>
	[<span class="hljs-comment">--token TOKEN] [--push-to-hub] --model MODEL --project-name PROJECT_NAME</span>
	[<span class="hljs-comment">--data-path DATA_PATH] [--train-split TRAIN_SPLIT] [--valid-split VALID_SPLIT]</span>
	[<span class="hljs-comment">--batch-size BATCH_SIZE] [--seed SEED] [--epochs EPOCHS]</span>
	[<span class="hljs-comment">--gradient_accumulation GRADIENT_ACCUMULATION] [--disable_gradient_checkpointing] [--lr LR]</span>
	[<span class="hljs-comment">--log {none,wandb,tensorboard}] [--text-column TEXT_COLUMN] [--target-column TARGET_COLUMN]</span>
	[<span class="hljs-comment">--max-seq-length MAX_SEQ_LENGTH] [--warmup-ratio WARMUP_RATIO] [--optimizer OPTIMIZER]</span>
	[<span class="hljs-comment">--scheduler SCHEDULER] [--weight-decay WEIGHT_DECAY] [--max-grad-norm MAX_GRAD_NORM]</span>
	[<span class="hljs-comment">--logging-steps LOGGING_STEPS] [--eval-strategy {steps,epoch,no}]</span>
	[<span class="hljs-comment">--save-total-limit SAVE_TOTAL_LIMIT]</span>
	[<span class="hljs-comment">--auto-find-batch-size] [--mixed-precision {fp16,bf16,None}]</span>

	✨ Run AutoTrain <span class="hljs-literal">Text</span> Regression

	options:
	-h, <span class="hljs-comment">--help show this help message and exit</span>
	<span class="hljs-comment">--train Command to train the model</span>
	<span class="hljs-comment">--deploy Command to deploy the model (limited availability)</span>
	<span class="hljs-comment">--inference Command to run inference (limited availability)</span>
	<span class="hljs-comment">--username USERNAME Hugging Face Hub Username</span>
	<span class="hljs-comment">--backend {local-cli,spaces-a10gl,spaces-a10gs,spaces-a100,spaces-t4m,spaces-t4s,spaces-cpu,spaces-cpuf}</span>
	Backend <span class="hljs-keyword">to</span> <span class="hljs-keyword">use</span>: <span class="hljs-keyword">default</span> <span class="hljs-keyword">or</span> spaces. Spaces backend requires push_to_hub & username. Advanced users only.
	<span class="hljs-comment">--token TOKEN Your Hugging Face API token. Token must have write access to the model hub.</span>
	<span class="hljs-comment">--push-to-hub Push to hub after training will push the trained model to the Hugging Face model hub.</span>
	<span class="hljs-comment">--model MODEL Base model to use for training</span>
	<span class="hljs-comment">--project-name PROJECT_NAME</span>
	Output directory / repo id <span class="hljs-keyword">for</span> trained model (must be unique <span class="hljs-keyword">on</span> hub)
	<span class="hljs-comment">--data-path DATA_PATH</span>
	Train dataset <span class="hljs-keyword">to</span> <span class="hljs-keyword">use</span>. <span class="hljs-keyword">When</span> using cli, this should be a directory path containing training <span class="hljs-keyword">and</span> validation data <span class="hljs-keyword">in</span> appropriate
	formats
	<span class="hljs-comment">--train-split TRAIN_SPLIT</span>
	Train dataset split <span class="hljs-keyword">to</span> <span class="hljs-keyword">use</span>
	<span class="hljs-comment">--valid-split VALID_SPLIT</span>
	Validation dataset split <span class="hljs-keyword">to</span> <span class="hljs-keyword">use</span>
	<span class="hljs-comment">--batch-size BATCH_SIZE</span>
	Training batch size <span class="hljs-keyword">to</span> <span class="hljs-keyword">use</span>
	<span class="hljs-comment">--seed SEED Random seed for reproducibility</span>
	<span class="hljs-comment">--epochs EPOCHS Number of training epochs</span>
	<span class="hljs-comment">--gradient_accumulation GRADIENT_ACCUMULATION</span>
	Gradient accumulation steps
	<span class="hljs-comment">--disable_gradient_checkpointing</span>
	Disable gradient checkpointing
	<span class="hljs-comment">--lr LR Learning rate</span>
	<span class="hljs-comment">--log {none,wandb,tensorboard}</span>
	<span class="hljs-keyword">Use</span> experiment tracking
	<span class="hljs-comment">--text-column TEXT_COLUMN</span>
	Specify the column name <span class="hljs-keyword">in</span> the dataset that contains the <span class="hljs-literal">text</span> data. Useful <span class="hljs-keyword">for</span> distinguishing between multiple <span class="hljs-literal">text</span> fields.
	<span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-symbol">'text</span>'.
	<span class="hljs-comment">--target-column TARGET_COLUMN</span>
	Specify the column name that holds the target <span class="hljs-keyword">or</span> <span class="hljs-keyword">label</span> data <span class="hljs-keyword">for</span> training. Helps <span class="hljs-keyword">in</span> distinguishing different potential
	outputs. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-symbol">'target</span>'.
	<span class="hljs-comment">--max-seq-length MAX_SEQ_LENGTH</span>
	Set the maximum <span class="hljs-keyword">sequence</span> length (number <span class="hljs-keyword">of</span> tokens) that the model should handle <span class="hljs-keyword">in</span> a single input. Longer sequences are
	truncated. Affects both memory usage <span class="hljs-keyword">and</span> computational requirements. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-number">128</span> tokens.
	<span class="hljs-comment">--warmup-ratio WARMUP_RATIO</span>
	Define the proportion <span class="hljs-keyword">of</span> training <span class="hljs-keyword">to</span> be dedicated <span class="hljs-keyword">to</span> a linear warmup where learning rate gradually increases. This can help
	<span class="hljs-keyword">in</span> stabilizing the training <span class="hljs-keyword">process</span> early <span class="hljs-keyword">on</span>. <span class="hljs-keyword">Default</span> ratio <span class="hljs-keyword">is</span> <span class="hljs-number">0.1</span>.
	<span class="hljs-comment">--optimizer OPTIMIZER</span>
	Choose the optimizer algorithm <span class="hljs-keyword">for</span> training the model. Different optimizers can affect the training speed <span class="hljs-keyword">and</span> model
	performance. <span class="hljs-symbol">'adamw_torch</span>' <span class="hljs-keyword">is</span> used by <span class="hljs-keyword">default</span>.
	<span class="hljs-comment">--scheduler SCHEDULER</span>
	<span class="hljs-keyword">Select</span> the learning rate scheduler <span class="hljs-keyword">to</span> adjust the learning rate based <span class="hljs-keyword">on</span> the number <span class="hljs-keyword">of</span> epochs. <span class="hljs-symbol">'linear</span>' decreases the
	learning rate linearly from the initial lr set. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-symbol">'linear</span>'. Try <span class="hljs-symbol">'cosine</span>' <span class="hljs-keyword">for</span> a cosine annealing schedule.
	<span class="hljs-comment">--weight-decay WEIGHT_DECAY</span>
	Set the weight decay rate <span class="hljs-keyword">to</span> apply <span class="hljs-keyword">for</span> regularization. Helps <span class="hljs-keyword">in</span> preventing the model from overfitting by penalizing large
	weights. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-number">0.0</span>, meaning no weight decay <span class="hljs-keyword">is</span> applied.
	<span class="hljs-comment">--max-grad-norm MAX_GRAD_NORM</span>
	Specify the maximum norm <span class="hljs-keyword">of</span> the gradients <span class="hljs-keyword">for</span> gradient clipping. Gradient clipping <span class="hljs-keyword">is</span> used <span class="hljs-keyword">to</span> prevent the exploding gradient
	problem <span class="hljs-keyword">in</span> deep neural networks. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-number">1.0</span>.
	<span class="hljs-comment">--logging-steps LOGGING_STEPS</span>
	Determine how often <span class="hljs-keyword">to</span> log training progress. Set this <span class="hljs-keyword">to</span> the number <span class="hljs-keyword">of</span> steps between each log output. -<span class="hljs-number">1</span> determines logging
	steps automatically. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> -<span class="hljs-number">1</span>.
	<span class="hljs-comment">--eval-strategy {steps,epoch,no}</span>
	Specify how often <span class="hljs-keyword">to</span> evaluate the model performance. Options include <span class="hljs-symbol">'no</span>', <span class="hljs-symbol">'steps</span>', <span class="hljs-symbol">'epoch</span>'. <span class="hljs-symbol">'epoch</span>' evaluates at the <span class="hljs-keyword">end</span> <span class="hljs-keyword">of</span>
	each training epoch by <span class="hljs-keyword">default</span>.
	<span class="hljs-comment">--save-total-limit SAVE_TOTAL_LIMIT</span>
	Limit the total number <span class="hljs-keyword">of</span> model checkpoints <span class="hljs-keyword">to</span> save. Helps manage disk space by retaining only the most recent checkpoints.
	<span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> <span class="hljs-keyword">to</span> save only the latest one.
	<span class="hljs-comment">--auto-find-batch-size</span>
	Enable automatic batch size determination based <span class="hljs-keyword">on</span> your hardware capabilities. <span class="hljs-keyword">When</span> set, it tries <span class="hljs-keyword">to</span> find the largest batch
	size that fits <span class="hljs-keyword">in</span> memory.
	<span class="hljs-comment">--mixed-precision {fp16,bf16,None}</span>
	Choose the precision mode <span class="hljs-keyword">for</span> training <span class="hljs-keyword">to</span> optimize performance <span class="hljs-keyword">and</span> memory usage. Options are <span class="hljs-symbol">'fp16</span>', <span class="hljs-symbol">'bf16</span>', <span class="hljs-keyword">or</span> None <span class="hljs-keyword">for</span>
	<span class="hljs-keyword">default</span> precision. <span class="hljs-keyword">Default</span> <span class="hljs-keyword">is</span> None.<!-- HTML_TAG_END --></pre></div> <a class="!text-gray-400 !no-underline text-sm flex items-center not-prose mt-4" href="https://github.com/huggingface/autotrain-advanced/blob/main/docs/source/text_regression.mdx" target="_blank"><span data-svelte-h="svelte-1kd6by1"><</span> <span data-svelte-h="svelte-x0xyl0">></span> <span data-svelte-h="svelte-1dajgef"><span class="underline ml-1.5">Update</span> on GitHub</span></a> <p></p>

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