Buckets:

hf-doc-build
/

doc-dev

hf-doc-build/doc-dev / transformers /pr_41116 /en /_app /immutable /nodes /167.b936d4fc.js

HuggingFaceDocBuilder's picture

HuggingFaceDocBuilder

about 1 month ago

78.8 kB

	import{s as Wn,o as Zn,n as X}from"../chunks/scheduler.31fdf58d.js";import{S as Bn,i as Fn,e as p,s as r,c as u,h as Hn,a as m,d as a,b as l,f as H,j as y,g as f,k as q,w as qn,l as i,m as d,n as g,t as _,o as b,p as T}from"../chunks/index.2f76fdf0.js";import{T as an}from"../chunks/Tip.8d349121.js";import{C as Vn}from"../chunks/CopyLLMTxtMenu.6ffa41dc.js";import{D as G}from"../chunks/Docstring.ac2bce02.js";import{C as xe}from"../chunks/CodeBlock.e52df5d6.js";import{E as Re}from"../chunks/ExampleCodeBlock.f8b50594.js";import{H as E,E as Xn}from"../chunks/MermaidChart.svelte_svelte_type_style_lang.66a55e6f.js";function Sn(v){let t,h="Examples:",o,c,M;return c=new xe({props:{code:"ZnJvbSUyMHRyYW5zZm9ybWVycyUyMGltcG9ydCUyMENUUkxDb25maWclMkMlMjBDVFJMTW9kZWwlMEElMEElMjMlMjBJbml0aWFsaXppbmclMjBhJTIwQ1RSTCUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwQ1RSTENvbmZpZygpJTBBJTBBJTIzJTIwSW5pdGlhbGl6aW5nJTIwYSUyMG1vZGVsJTIwKHdpdGglMjByYW5kb20lMjB3ZWlnaHRzKSUyMGZyb20lMjB0aGUlMjBjb25maWd1cmF0aW9uJTBBbW9kZWwlMjAlM0QlMjBDVFJMTW9kZWwoY29uZmlndXJhdGlvbiklMEElMEElMjMlMjBBY2Nlc3NpbmclMjB0aGUlMjBtb2RlbCUyMGNvbmZpZ3VyYXRpb24lMEFjb25maWd1cmF0aW9uJTIwJTNEJTIwbW9kZWwuY29uZmln",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> CTRLConfig, CTRLModel

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># Initializing a CTRL configuration</span>
	<span class="hljs-meta">>>> </span>configuration = CTRLConfig()

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># Initializing a model (with random weights) from the configuration</span>
	<span class="hljs-meta">>>> </span>model = CTRLModel(configuration)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># Accessing the model configuration</span>
	<span class="hljs-meta">>>> </span>configuration = model.config`,lang:"python",wrap:!1}}),{c(){t=p("p"),t.textContent=h,o=r(),u(c.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),y(t)!=="svelte-kvfsh7"&&(t.textContent=h),o=l(n),f(c.$$.fragment,n)},m(n,w){d(n,t,w),d(n,o,w),g(c,n,w),M=!0},p:X,i(n){M\|\|(_(c.$$.fragment,n),M=!0)},o(n){b(c.$$.fragment,n),M=!1},d(n){n&&(a(t),a(o)),T(c,n)}}}function Gn(v){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
	instance afterwards instead of this since the former takes care of running the pre and post processing steps while
	the latter silently ignores them.`;return{c(){t=p("p"),t.innerHTML=h},l(o){t=m(o,"P",{"data-svelte-h":!0}),y(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,c){d(o,t,c)},p:X,d(o){o&&a(t)}}}function En(v){let t,h="Example:",o,c,M;return c=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CTRLModel
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch

	<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)
	<span class="hljs-meta">>>> </span>model = CTRLModel.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># CTRL was trained with control codes as the first token</span>
	<span class="hljs-meta">>>> </span>inputs = tokenizer(<span class="hljs-string">"Opinion My dog is cute"</span>, return_tensors=<span class="hljs-string">"pt"</span>)
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> inputs[<span class="hljs-string">"input_ids"</span>][<span class="hljs-number">0</span>, <span class="hljs-number">0</span>].item() <span class="hljs-keyword">in</span> tokenizer.control_codes.values()

	<span class="hljs-meta">>>> </span>outputs = model(**inputs)

	<span class="hljs-meta">>>> </span>last_hidden_states = outputs.last_hidden_state
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">list</span>(last_hidden_states.shape)
	[<span class="hljs-number">1</span>, <span class="hljs-number">5</span>, <span class="hljs-number">1280</span>]`,lang:"python",wrap:!1}}),{c(){t=p("p"),t.textContent=h,o=r(),u(c.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),y(t)!=="svelte-11lpom8"&&(t.textContent=h),o=l(n),f(c.$$.fragment,n)},m(n,w){d(n,t,w),d(n,o,w),g(c,n,w),M=!0},p:X,i(n){M\|\|(_(c.$$.fragment,n),M=!0)},o(n){b(c.$$.fragment,n),M=!1},d(n){n&&(a(t),a(o)),T(c,n)}}}function Yn(v){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
	instance afterwards instead of this since the former takes care of running the pre and post processing steps while
	the latter silently ignores them.`;return{c(){t=p("p"),t.innerHTML=h},l(o){t=m(o,"P",{"data-svelte-h":!0}),y(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,c){d(o,t,c)},p:X,d(o){o&&a(t)}}}function Pn(v){let t,h="Example:",o,c,M;return c=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CTRLLMHeadModel

	<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)
	<span class="hljs-meta">>>> </span>model = CTRLLMHeadModel.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># CTRL was trained with control codes as the first token</span>
	<span class="hljs-meta">>>> </span>inputs = tokenizer(<span class="hljs-string">"Wikipedia The llama is"</span>, return_tensors=<span class="hljs-string">"pt"</span>)
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> inputs[<span class="hljs-string">"input_ids"</span>][<span class="hljs-number">0</span>, <span class="hljs-number">0</span>].item() <span class="hljs-keyword">in</span> tokenizer.control_codes.values()

	<span class="hljs-meta">>>> </span>sequence_ids = model.generate(inputs[<span class="hljs-string">"input_ids"</span>])
	<span class="hljs-meta">>>> </span>sequences = tokenizer.batch_decode(sequence_ids)
	<span class="hljs-meta">>>> </span>sequences
	[<span class="hljs-string">'Wikipedia The llama is a member of the family Bovidae. It is native to the Andes of Peru,'</span>]

	<span class="hljs-meta">>>> </span>outputs = model(**inputs, labels=inputs[<span class="hljs-string">"input_ids"</span>])
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">round</span>(outputs.loss.item(), <span class="hljs-number">2</span>)
	<span class="hljs-number">9.21</span>

	<span class="hljs-meta">>>> </span><span class="hljs-built_in">list</span>(outputs.logits.shape)
	[<span class="hljs-number">1</span>, <span class="hljs-number">5</span>, <span class="hljs-number">246534</span>]`,lang:"python",wrap:!1}}),{c(){t=p("p"),t.textContent=h,o=r(),u(c.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),y(t)!=="svelte-11lpom8"&&(t.textContent=h),o=l(n),f(c.$$.fragment,n)},m(n,w){d(n,t,w),d(n,o,w),g(c,n,w),M=!0},p:X,i(n){M\|\|(_(c.$$.fragment,n),M=!0)},o(n){b(c.$$.fragment,n),M=!1},d(n){n&&(a(t),a(o)),T(c,n)}}}function Qn(v){let t,h=`Although the recipe for forward pass needs to be defined within this function, one should call the <code>Module</code>
	instance afterwards instead of this since the former takes care of running the pre and post processing steps while
	the latter silently ignores them.`;return{c(){t=p("p"),t.innerHTML=h},l(o){t=m(o,"P",{"data-svelte-h":!0}),y(t)!=="svelte-fincs2"&&(t.innerHTML=h)},m(o,c){d(o,t,c)},p:X,d(o){o&&a(t)}}}function Dn(v){let t,h="Example of single-label classification:",o,c,M;return c=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CTRLForSequenceClassification

	<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)
	<span class="hljs-meta">>>> </span>model = CTRLForSequenceClassification.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># CTRL was trained with control codes as the first token</span>
	<span class="hljs-meta">>>> </span>inputs = tokenizer(<span class="hljs-string">"Opinion My dog is cute"</span>, return_tensors=<span class="hljs-string">"pt"</span>)
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> inputs[<span class="hljs-string">"input_ids"</span>][<span class="hljs-number">0</span>, <span class="hljs-number">0</span>].item() <span class="hljs-keyword">in</span> tokenizer.control_codes.values()

	<span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> torch.no_grad():
	<span class="hljs-meta">... </span> logits = model(**inputs).logits

	<span class="hljs-meta">>>> </span>predicted_class_id = logits.argmax().item()
	<span class="hljs-meta">>>> </span>model.config.id2label[predicted_class_id]
	<span class="hljs-string">'LABEL_0'</span>`,lang:"python",wrap:!1}}),{c(){t=p("p"),t.textContent=h,o=r(),u(c.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),y(t)!=="svelte-ykxpe4"&&(t.textContent=h),o=l(n),f(c.$$.fragment,n)},m(n,w){d(n,t,w),d(n,o,w),g(c,n,w),M=!0},p:X,i(n){M\|\|(_(c.$$.fragment,n),M=!0)},o(n){b(c.$$.fragment,n),M=!1},d(n){n&&(a(t),a(o)),T(c,n)}}}function An(v){let t,h;return t=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch

	<span class="hljs-meta">>>> </span>torch.manual_seed(<span class="hljs-number">42</span>)
	<span class="hljs-meta">>>> </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
	<span class="hljs-meta">>>> </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
	<span class="hljs-meta">>>> </span>model = CTRLForSequenceClassification.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>, num_labels=num_labels)

	<span class="hljs-meta">>>> </span>labels = torch.tensor(<span class="hljs-number">1</span>)
	<span class="hljs-meta">>>> </span>loss = model(**inputs, labels=labels).loss
	<span class="hljs-meta">>>> </span><span class="hljs-built_in">round</span>(loss.item(), <span class="hljs-number">2</span>)
	<span class="hljs-number">0.93</span>`,lang:"python",wrap:!1}}),{c(){u(t.$$.fragment)},l(o){f(t.$$.fragment,o)},m(o,c){g(t,o,c),h=!0},p:X,i(o){h\|\|(_(t.$$.fragment,o),h=!0)},o(o){b(t.$$.fragment,o),h=!1},d(o){T(t,o)}}}function Kn(v){let t,h="Example of multi-label classification:",o,c,M;return c=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-keyword">import</span> torch
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">from</span> transformers <span class="hljs-keyword">import</span> AutoTokenizer, CTRLForSequenceClassification

	<span class="hljs-meta">>>> </span>tokenizer = AutoTokenizer.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>)
	<span class="hljs-meta">>>> </span>model = CTRLForSequenceClassification.from_pretrained(
	<span class="hljs-meta">... </span> <span class="hljs-string">"Salesforce/ctrl"</span>, problem_type=<span class="hljs-string">"multi_label_classification"</span>
	<span class="hljs-meta">... </span>)

	<span class="hljs-meta">>>> </span><span class="hljs-comment"># CTRL was trained with control codes as the first token</span>
	<span class="hljs-meta">>>> </span>inputs = tokenizer(<span class="hljs-string">"Opinion My dog is cute"</span>, return_tensors=<span class="hljs-string">"pt"</span>)
	<span class="hljs-meta">>>> </span><span class="hljs-keyword">assert</span> inputs[<span class="hljs-string">"input_ids"</span>][<span class="hljs-number">0</span>, <span class="hljs-number">0</span>].item() <span class="hljs-keyword">in</span> tokenizer.control_codes.values()

	<span class="hljs-meta">>>> </span><span class="hljs-keyword">with</span> torch.no_grad():
	<span class="hljs-meta">... </span> logits = model(**inputs).logits

	<span class="hljs-meta">>>> </span>predicted_class_id = logits.argmax().item()
	<span class="hljs-meta">>>> </span>model.config.id2label[predicted_class_id]
	<span class="hljs-string">'LABEL_0'</span>`,lang:"python",wrap:!1}}),{c(){t=p("p"),t.textContent=h,o=r(),u(c.$$.fragment)},l(n){t=m(n,"P",{"data-svelte-h":!0}),y(t)!=="svelte-1l8e32d"&&(t.textContent=h),o=l(n),f(c.$$.fragment,n)},m(n,w){d(n,t,w),d(n,o,w),g(c,n,w),M=!0},p:X,i(n){M\|\|(_(c.$$.fragment,n),M=!0)},o(n){b(c.$$.fragment,n),M=!1},d(n){n&&(a(t),a(o)),T(c,n)}}}function On(v){let t,h;return t=new xe({props:{code:"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",highlighted:`<span class="hljs-meta">>>> </span><span class="hljs-comment"># To train a model on \`num_labels\` classes, you can pass \`num_labels=num_labels\` to \`.from_pretrained(...)\`</span>
	<span class="hljs-meta">>>> </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
	<span class="hljs-meta">>>> </span>model = CTRLForSequenceClassification.from_pretrained(<span class="hljs-string">"Salesforce/ctrl"</span>, num_labels=num_labels)

	<span class="hljs-meta">>>> </span>num_labels = <span class="hljs-built_in">len</span>(model.config.id2label)
	<span class="hljs-meta">>>> </span>labels = torch.nn.functional.one_hot(torch.tensor([predicted_class_id]), num_classes=num_labels).to(
	<span class="hljs-meta">... </span> torch.<span class="hljs-built_in">float</span>
	<span class="hljs-meta">... </span>)
	<span class="hljs-meta">>>> </span>loss = model(**inputs, labels=labels).loss
	<span class="hljs-meta">>>> </span>loss.backward()`,lang:"python",wrap:!1}}),{c(){u(t.$$.fragment)},l(o){f(t.$$.fragment,o)},m(o,c){g(t,o,c),h=!0},p:X,i(o){h\|\|(_(t.$$.fragment,o),h=!0)},o(o){b(t.$$.fragment,o),h=!1},d(o){T(t,o)}}}function es(v){let t,h,o,c,M,n="<em>This model was published in HF papers on 2019-09-11 and contributed to Hugging Face Transformers on 2020-11-16.</em>",w,oe,et,Y,rn='<div class="flex flex-wrap space-x-1"><img alt="FlashAttention" src="https://img.shields.io/badge/%E2%9A%A1%EF%B8%8E%20FlashAttention-eae0c8?style=flat"/> <img alt="SDPA" src="https://img.shields.io/badge/SDPA-DE3412?style=flat&logo=pytorch&logoColor=white"/></div>',tt,ae,nt,re,st,le,ln=`CTRL model was proposed in <a href="https://huggingface.co/papers/1909.05858" rel="nofollow">CTRL: A Conditional Transformer Language Model for Controllable Generation</a> by Nitish Shirish Keskar<em>, Bryan McCann</em>, Lav R. Varshney, Caiming Xiong and
	Richard Socher. It’s a causal (unidirectional) transformer pre-trained using language modeling on a very large corpus
	of ~140 GB of text data with the first token reserved as a control code (such as Links, Books, Wikipedia etc.).`,ot,ie,cn="The abstract from the paper is the following:",at,ce,dn=`<em>Large-scale language models show promising text generation capabilities, but users cannot easily control particular
	aspects of the generated text. We release CTRL, a 1.63 billion-parameter conditional transformer language model,
	trained to condition on control codes that govern style, content, and task-specific behavior. Control codes were
	derived from structure that naturally co-occurs with raw text, preserving the advantages of unsupervised learning while
	providing more explicit control over text generation. These codes also allow CTRL to predict which parts of the
	training data are most likely given a sequence. This provides a potential method for analyzing large amounts of data
	via model-based source attribution.</em>`,rt,de,pn=`This model was contributed by <a href="https://huggingface.co/keskarnitishr" rel="nofollow">keskarnitishr</a>. The original code can be found
	<a href="https://github.com/salesforce/ctrl" rel="nofollow">here</a>.`,lt,pe,it,me,mn=`<li>CTRL makes use of control codes to generate text: it requires generations to be started by certain words, sentences
	or links to generate coherent text. Refer to the <a href="https://github.com/salesforce/ctrl" rel="nofollow">original implementation</a> for
	more information.</li> <li>CTRL is a model with absolute position embeddings so it’s usually advised to pad the inputs on the right rather than
	the left.</li> <li>CTRL was trained with a causal language modeling (CLM) objective and is therefore powerful at predicting the next
	token in a sequence. Leveraging this feature allows CTRL to generate syntactically coherent text as it can be
	observed in the <em>run_generation.py</em> example script.</li> <li>The PyTorch models can take the <code>past_key_values</code> as input, which is the previously computed key/value attention pairs.
	Using the <code>past_key_values</code> value prevents the model from re-computing
	pre-computed values in the context of text generation. See the <a href="model_doc/ctrl#transformers.CTRLModel.forward"><code>forward</code></a>
	method for more information on the usage of this argument.</li>`,ct,he,dt,ue,hn='<li><a href="../tasks/sequence_classification">Text classification task guide</a></li> <li><a href="../tasks/language_modeling">Causal language modeling task guide</a></li>',pt,fe,mt,x,ge,kt,ze,un=`This is the configuration class to store the configuration of a CTRLModel. It is used to instantiate a Ctrl
	model according to the specified arguments, defining the model architecture. Instantiating a configuration with the
	defaults will yield a similar configuration to that of the <a href="https://huggingface.co/Salesforce/ctrl" rel="nofollow">Salesforce/ctrl</a>`,Ct,Ue,fn=`Configuration objects inherit from <a href="/docs/transformers/pr_41116/en/main_classes/configuration#transformers.PreTrainedConfig">PreTrainedConfig</a> and can be used to control the model outputs. Read the
	documentation from <a href="/docs/transformers/pr_41116/en/main_classes/configuration#transformers.PreTrainedConfig">PreTrainedConfig</a> for more information.`,$t,P,ht,_e,ut,z,be,jt,Ie,gn="Construct a CTRL tokenizer. Based on Byte-Pair-Encoding.",Lt,Ne,_n=`This tokenizer inherits from <a href="/docs/transformers/pr_41116/en/main_classes/tokenizer#transformers.PythonBackend">PreTrainedTokenizer</a> which contains most of the main methods. Users should refer to
	this superclass for more information regarding those methods.`,Jt,V,Te,Rt,We,bn=`Default implementation for common vocabulary saving patterns.
	Saves self.encoder/self.vocab as JSON, optionally with self.bpe_ranks as merges.
	Returns empty tuple if no vocabulary exists.`,xt,Ze,Tn=`Override this method if your tokenizer needs custom saving logic (e.g., SentencePiece models,
	multiple vocabulary files, or special file formats).`,ft,ye,gt,$,Me,zt,Be,yn="The bare Ctrl Model outputting raw hidden-states without any specific head on top.",Ut,Fe,Mn=`This model inherits from <a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)`,It,He,wn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.`,Nt,J,we,Wt,qe,vn='The <a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a> forward method, overrides the <code>__call__</code> special method.',Zt,Q,Bt,Ve,kn=`<li><p><strong>last_hidden_state</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>) — Sequence of hidden-states at the output of the last layer of the model.</p> <p>If <code>past_key_values</code> is used only the last hidden-state of the sequences of shape <code>(batch_size, 1, hidden_size)</code> is output.</p></li> <li><p><strong>past_key_values</strong> (<code>Cache</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — It is a <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.Cache">Cache</a> instance. For more details, see our <a href="https://huggingface.co/docs/transformers/en/kv_cache" rel="nofollow">kv cache guide</a>.</p> <p>Contains pre-computed hidden-states (key and values in the self-attention blocks and optionally if
	<code>config.is_encoder_decoder=True</code> in the cross-attention blocks) that can be used (see <code>past_key_values</code>
	input) to speed up sequential decoding.</p></li> <li><p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p> <p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p></li> <li><p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p> <p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.</p></li>`,Ft,D,_t,ve,bt,j,ke,Ht,Xe,Cn=`The CTRL Model transformer with a language modeling head on top (linear layer with weights tied to the input
	embeddings).`,qt,Se,$n=`This model inherits from <a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)`,Vt,Ge,jn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.`,Xt,R,Ce,St,Ee,Ln='The <a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a> forward method, overrides the <code>__call__</code> special method.',Gt,A,Et,Ye,Jn=`<li><p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Language modeling loss (for next-token prediction).</p></li> <li><p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, config.vocab_size)</code>) — Prediction scores of the language modeling head (scores for each vocabulary token before SoftMax).</p></li> <li><p><strong>past_key_values</strong> (<code>Cache</code>, <em>optional</em>, returned when <code>use_cache=True</code> is passed or when <code>config.use_cache=True</code>) — It is a <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.Cache">Cache</a> instance. For more details, see our <a href="https://huggingface.co/docs/transformers/en/kv_cache" rel="nofollow">kv cache guide</a>.</p> <p>Contains pre-computed hidden-states (key and values in the self-attention blocks) that can be used (see
	<code>past_key_values</code> input) to speed up sequential decoding.</p></li> <li><p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p> <p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p></li> <li><p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p> <p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.</p></li>`,Yt,K,Tt,$e,yt,L,je,Pt,Pe,Rn=`The CTRL Model transformer with a sequence classification head on top (linear layer).
	<a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> uses the last token in order to do the classification, as other causal models
	(e.g. GPT-2) do. Since it does classification on the last token, it requires to know the position of the last
	token. If a <code>pad_token_id</code> is defined in the configuration, it finds the last token that is not a padding token in
	each row. If no <code>pad_token_id</code> is defined, it simply takes the last value in each row of the batch. Since it cannot
	guess the padding tokens when <code>inputs_embeds</code> are passed instead of <code>input_ids</code>, it does the same (take the last
	value in each row of the batch).`,Qt,Qe,xn=`This model inherits from <a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel">PreTrainedModel</a>. Check the superclass documentation for the generic methods the
	library implements for all its model (such as downloading or saving, resizing the input embeddings, pruning heads
	etc.)`,Dt,De,zn=`This model is also a PyTorch <a href="https://pytorch.org/docs/stable/nn.html#torch.nn.Module" rel="nofollow">torch.nn.Module</a> subclass.
	Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage
	and behavior.`,At,k,Le,Kt,Ae,Un='The <a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a> forward method, overrides the <code>__call__</code> special method.',Ot,O,en,Ke,In=`<li><p><strong>loss</strong> (<code>torch.FloatTensor</code> of shape <code>(1,)</code>, <em>optional</em>, returned when <code>labels</code> is provided) — Classification (or regression if config.num_labels==1) loss.</p></li> <li><p><strong>logits</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, config.num_labels)</code>) — Classification (or regression if config.num_labels==1) scores (before SoftMax).</p></li> <li><p><strong>hidden_states</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_hidden_states=True</code> is passed or when <code>config.output_hidden_states=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for the output of the embeddings, if the model has an embedding layer, +
	one for the output of each layer) of shape <code>(batch_size, sequence_length, hidden_size)</code>.</p> <p>Hidden-states of the model at the output of each layer plus the optional initial embedding outputs.</p></li> <li><p><strong>attentions</strong> (<code>tuple(torch.FloatTensor)</code>, <em>optional</em>, returned when <code>output_attentions=True</code> is passed or when <code>config.output_attentions=True</code>) — Tuple of <code>torch.FloatTensor</code> (one for each layer) of shape <code>(batch_size, num_heads, sequence_length, sequence_length)</code>.</p> <p>Attentions weights after the attention softmax, used to compute the weighted average in the self-attention
	heads.</p></li>`,tn,ee,nn,te,sn,ne,on,se,Mt,Je,wt,Oe,vt;return oe=new Vn({props:{containerStyle:"float: right; margin-left: 10px; display: inline-flex; position: relative; z-index: 10;"}}),ae=new E({props:{title:"CTRL",local:"ctrl",headingTag:"h1"}}),re=new E({props:{title:"Overview",local:"overview",headingTag:"h2"}}),pe=new E({props:{title:"Usage tips",local:"usage-tips",headingTag:"h2"}}),he=new E({props:{title:"Resources",local:"resources",headingTag:"h2"}}),fe=new E({props:{title:"CTRLConfig",local:"transformers.CTRLConfig",headingTag:"h2"}}),ge=new G({props:{name:"class transformers.CTRLConfig",anchor:"transformers.CTRLConfig",parameters:[{name:"transformers_version",val:": str \| None = None"},{name:"architectures",val:": list[str] \| None = None"},{name:"output_hidden_states",val:": bool \| None = False"},{name:"return_dict",val:": bool \| None = True"},{name:"dtype",val:": typing.Union[str, ForwardRef('torch.dtype'), NoneType] = None"},{name:"chunk_size_feed_forward",val:": int = 0"},{name:"is_encoder_decoder",val:": bool = False"},{name:"id2label",val:": dict[int, str] \| dict[str, str] \| None = None"},{name:"label2id",val:": dict[str, int] \| dict[str, str] \| None = None"},{name:"problem_type",val:": typing.Optional[typing.Literal['regression', 'single_label_classification', 'multi_label_classification']] = None"},{name:"vocab_size",val:": int = 246534"},{name:"n_positions",val:": int = 256"},{name:"n_embd",val:": int = 1280"},{name:"dff",val:": int = 8192"},{name:"n_layer",val:": int = 48"},{name:"n_head",val:": int = 16"},{name:"resid_pdrop",val:": float \| int = 0.1"},{name:"embd_pdrop",val:": float \| int = 0.1"},{name:"layer_norm_epsilon",val:": float = 1e-06"},{name:"initializer_range",val:": float = 0.02"},{name:"use_cache",val:": bool = True"},{name:"pad_token_id",val:": int \| None = None"},{name:"bos_token_id",val:": int \| None = None"},{name:"eos_token_id",val:": int \| list[int] \| None = None"},{name:"tie_word_embeddings",val:": bool = True"}],parametersDescription:[{anchor:"transformers.CTRLConfig.vocab_size",description:`<strong>vocab_size</strong> (<code>int</code>, <em>optional</em>, defaults to <code>246534</code>) —
	Vocabulary size of the model. Defines the number of different tokens that can be represented by the <code>input_ids</code>.`,name:"vocab_size"},{anchor:"transformers.CTRLConfig.n_positions",description:`<strong>n_positions</strong> (<code>int</code>, <em>optional</em>, defaults to <code>256</code>) —
	The maximum sequence length that this model might ever be used with.`,name:"n_positions"},{anchor:"transformers.CTRLConfig.n_embd",description:`<strong>n_embd</strong> (<code>int</code>, <em>optional</em>, defaults to <code>1280</code>) —
	Dimensionality of the embeddings and hidden states.`,name:"n_embd"},{anchor:"transformers.CTRLConfig.dff",description:`<strong>dff</strong> (<code>int</code>, <em>optional</em>, defaults to 8192) —
	Dimensionality of the inner dimension of the feed forward networks (FFN).`,name:"dff"},{anchor:"transformers.CTRLConfig.n_layer",description:`<strong>n_layer</strong> (<code>int</code>, <em>optional</em>, defaults to <code>48</code>) —
	Number of hidden layers in the Transformer decoder.`,name:"n_layer"},{anchor:"transformers.CTRLConfig.n_head",description:`<strong>n_head</strong> (<code>int</code>, <em>optional</em>, defaults to <code>16</code>) —
	Number of attention heads for each attention layer in the Transformer decoder.`,name:"n_head"},{anchor:"transformers.CTRLConfig.resid_pdrop",description:`<strong>resid_pdrop</strong> (<code>Union[float, int]</code>, <em>optional</em>, defaults to <code>0.1</code>) —
	The dropout probability for all fully connected layers in the embeddings, encoder, and pooler.`,name:"resid_pdrop"},{anchor:"transformers.CTRLConfig.embd_pdrop",description:`<strong>embd_pdrop</strong> (<code>Union[float, int]</code>, <em>optional</em>, defaults to <code>0.1</code>) —
	The dropout ratio for the embeddings.`,name:"embd_pdrop"},{anchor:"transformers.CTRLConfig.layer_norm_epsilon",description:`<strong>layer_norm_epsilon</strong> (<code>float</code>, <em>optional</em>, defaults to <code>1e-06</code>) —
	The epsilon used by the layer normalization layers.`,name:"layer_norm_epsilon"},{anchor:"transformers.CTRLConfig.initializer_range",description:`<strong>initializer_range</strong> (<code>float</code>, <em>optional</em>, defaults to <code>0.02</code>) —
	The standard deviation of the truncated_normal_initializer for initializing all weight matrices.`,name:"initializer_range"},{anchor:"transformers.CTRLConfig.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether or not the model should return the last key/values attentions (not used by all models). Only
	relevant if <code>config.is_decoder=True</code> or when the model is a decoder-only generative model.`,name:"use_cache"},{anchor:"transformers.CTRLConfig.pad_token_id",description:`<strong>pad_token_id</strong> (<code>int</code>, <em>optional</em>) —
	Token id used for padding in the vocabulary.`,name:"pad_token_id"},{anchor:"transformers.CTRLConfig.bos_token_id",description:`<strong>bos_token_id</strong> (<code>int</code>, <em>optional</em>) —
	Token id used for beginning-of-stream in the vocabulary.`,name:"bos_token_id"},{anchor:"transformers.CTRLConfig.eos_token_id",description:`<strong>eos_token_id</strong> (<code>Union[int, list[int]]</code>, <em>optional</em>) —
	Token id used for end-of-stream in the vocabulary.`,name:"eos_token_id"},{anchor:"transformers.CTRLConfig.tie_word_embeddings",description:`<strong>tie_word_embeddings</strong> (<code>bool</code>, <em>optional</em>, defaults to <code>True</code>) —
	Whether to tie weight embeddings according to model’s <code>tied_weights_keys</code> mapping.`,name:"tie_word_embeddings"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/configuration_ctrl.py#L24"}}),P=new Re({props:{anchor:"transformers.CTRLConfig.example",$$slots:{default:[Sn]},$$scope:{ctx:v}}}),_e=new E({props:{title:"CTRLTokenizer",local:"transformers.CTRLTokenizer",headingTag:"h2"}}),be=new G({props:{name:"class transformers.CTRLTokenizer",anchor:"transformers.CTRLTokenizer",parameters:[{name:"vocab_file",val:""},{name:"merges_file",val:""},{name:"unk_token",val:" = '<unk>'"},{name:"**kwargs",val:""}],parametersDescription:[{anchor:"transformers.CTRLTokenizer.vocab_file",description:`<strong>vocab_file</strong> (<code>str</code>) —
	Path to the vocabulary file.`,name:"vocab_file"},{anchor:"transformers.CTRLTokenizer.merges_file",description:`<strong>merges_file</strong> (<code>str</code>) —
	Path to the merges file.`,name:"merges_file"},{anchor:"transformers.CTRLTokenizer.unk_token",description:`<strong>unk_token</strong> (<code>str</code>, <em>optional</em>, defaults to <code>"<unk>"</code>) —
	The unknown token. A token that is not in the vocabulary cannot be converted to an ID and is set to be this
	token instead.`,name:"unk_token"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/tokenization_ctrl.py#L107"}}),Te=new G({props:{name:"save_vocabulary",anchor:"transformers.CTRLTokenizer.save_vocabulary",parameters:[{name:"save_directory",val:": str"},{name:"filename_prefix",val:": str \| None = None"}],parametersDescription:[{anchor:"transformers.CTRLTokenizer.save_vocabulary.save_directory",description:`<strong>save_directory</strong> (<code>str</code>) —
	The directory in which to save the vocabulary.`,name:"save_directory"},{anchor:"transformers.CTRLTokenizer.save_vocabulary.filename_prefix",description:`<strong>filename_prefix</strong> (<code>str</code>, <em>optional</em>) —
	An optional prefix to add to the named of the saved files.`,name:"filename_prefix"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/tokenization_python.py#L1363",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>Paths to the files saved, or empty tuple if no files saved.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><code>tuple[str, ...]</code></p>
	`}}),ye=new E({props:{title:"CTRLModel",local:"transformers.CTRLModel",headingTag:"h2"}}),Me=new G({props:{name:"class transformers.CTRLModel",anchor:"transformers.CTRLModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CTRLModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLModel">CTRLModel</a>) —
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	<a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L216"}}),we=new G({props:{name:"forward",anchor:"transformers.CTRLModel.forward",parameters:[{name:"input_ids",val:": torch.LongTensor \| None = None"},{name:"past_key_values",val:": transformers.cache_utils.Cache \| None = None"},{name:"attention_mask",val:": torch.FloatTensor \| None = None"},{name:"token_type_ids",val:": torch.LongTensor \| None = None"},{name:"position_ids",val:": torch.LongTensor \| None = None"},{name:"inputs_embeds",val:": torch.FloatTensor \| None = None"},{name:"use_cache",val:": bool \| None = None"},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}],parametersDescription:[{anchor:"transformers.CTRLModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.</p>
	<p>Indices can be obtained using <a href="/docs/transformers/pr_41116/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
	<a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
	<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.CTRLModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>~cache_utils.Cache</code>, <em>optional</em>) —
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
	returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
	<p>Only <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.Cache">Cache</a> instance is allowed as input, see our <a href="https://huggingface.co/docs/transformers/en/kv_cache" rel="nofollow">kv cache guide</a>.
	If no <code>past_key_values</code> are passed, <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.DynamicCache">DynamicCache</a> will be initialized by default.</p>
	<p>The model will output the same cache format that is fed as input.</p>
	<p>If <code>past_key_values</code> are used, the user is expected to input only unprocessed <code>input_ids</code> (those that don’t
	have their past key value states given to this model) of shape <code>(batch_size, unprocessed_length)</code> instead of all <code>input_ids</code>
	of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.CTRLModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>1 for tokens that are <strong>not masked</strong>,</li>
	<li>0 for tokens that are <strong>masked</strong>.</li>
	</ul>
	<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.CTRLModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>0 corresponds to a <em>sentence A</em> token,</li>
	<li>1 corresponds to a <em>sentence B</em> token.</li>
	</ul>
	<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.CTRLModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
	<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.CTRLModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
	Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
	model’s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.CTRLModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) —
	If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
	<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L242",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>A <a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
	>BaseModelOutputWithPast</a> or a tuple of
	<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
	elements depending on the configuration (<a
	href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLConfig"
	>CTRLConfig</a>) and inputs.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.BaseModelOutputWithPast"
	>BaseModelOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
	`}}),Q=new an({props:{$$slots:{default:[Gn]},$$scope:{ctx:v}}}),D=new Re({props:{anchor:"transformers.CTRLModel.forward.example",$$slots:{default:[En]},$$scope:{ctx:v}}}),ve=new E({props:{title:"CTRLLMHeadModel",local:"transformers.CTRLLMHeadModel",headingTag:"h2"}}),ke=new G({props:{name:"class transformers.CTRLLMHeadModel",anchor:"transformers.CTRLLMHeadModel",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CTRLLMHeadModel.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLLMHeadModel">CTRLLMHeadModel</a>) —
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	<a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L346"}}),Ce=new G({props:{name:"forward",anchor:"transformers.CTRLLMHeadModel.forward",parameters:[{name:"input_ids",val:": torch.LongTensor \| None = None"},{name:"past_key_values",val:": transformers.cache_utils.Cache \| None = None"},{name:"attention_mask",val:": torch.FloatTensor \| None = None"},{name:"token_type_ids",val:": torch.LongTensor \| None = None"},{name:"position_ids",val:": torch.LongTensor \| None = None"},{name:"inputs_embeds",val:": torch.FloatTensor \| None = None"},{name:"labels",val:": torch.LongTensor \| None = None"},{name:"use_cache",val:": bool \| None = None"},{name:"logits_to_keep",val:": int \| torch.Tensor = 0"},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}],parametersDescription:[{anchor:"transformers.CTRLLMHeadModel.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.</p>
	<p>Indices can be obtained using <a href="/docs/transformers/pr_41116/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
	<a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
	<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.CTRLLMHeadModel.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>~cache_utils.Cache</code>, <em>optional</em>) —
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
	returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
	<p>Only <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.Cache">Cache</a> instance is allowed as input, see our <a href="https://huggingface.co/docs/transformers/en/kv_cache" rel="nofollow">kv cache guide</a>.
	If no <code>past_key_values</code> are passed, <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.DynamicCache">DynamicCache</a> will be initialized by default.</p>
	<p>The model will output the same cache format that is fed as input.</p>
	<p>If <code>past_key_values</code> are used, the user is expected to input only unprocessed <code>input_ids</code> (those that don’t
	have their past key value states given to this model) of shape <code>(batch_size, unprocessed_length)</code> instead of all <code>input_ids</code>
	of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.CTRLLMHeadModel.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>1 for tokens that are <strong>not masked</strong>,</li>
	<li>0 for tokens that are <strong>masked</strong>.</li>
	</ul>
	<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.CTRLLMHeadModel.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>0 corresponds to a <em>sentence A</em> token,</li>
	<li>1 corresponds to a <em>sentence B</em> token.</li>
	</ul>
	<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.CTRLLMHeadModel.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
	<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.CTRLLMHeadModel.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
	Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
	model’s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.CTRLLMHeadModel.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Labels for language modeling. Note that the labels <strong>are shifted</strong> inside the model, i.e. you can set
	<code>labels = input_ids</code> Indices are selected in <code>[-100, 0, ..., config.vocab_size]</code> All labels set to <code>-100</code>
	are ignored (masked), the loss is only computed for labels in <code>[0, ..., config.vocab_size]</code>`,name:"labels"},{anchor:"transformers.CTRLLMHeadModel.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) —
	If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
	<code>past_key_values</code>).`,name:"use_cache"},{anchor:"transformers.CTRLLMHeadModel.forward.logits_to_keep",description:`<strong>logits_to_keep</strong> (<code>Union[int, torch.Tensor]</code>, <em>optional</em>, defaults to <code>0</code>) —
	If an <code>int</code>, compute logits for the last <code>logits_to_keep</code> tokens. If <code>0</code>, calculate logits for all
	<code>input_ids</code> (special case). Only last token logits are needed for generation, and calculating them only for that
	token can save memory, which becomes pretty significant for long sequences or large vocabulary size.
	If a <code>torch.Tensor</code>, must be 1D corresponding to the indices to keep in the sequence length dimension.
	This is useful when using packed tensor format (single dimension for batch and sequence length).`,name:"logits_to_keep"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L357",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>A <a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
	>CausalLMOutputWithPast</a> or a tuple of
	<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
	elements depending on the configuration (<a
	href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLConfig"
	>CTRLConfig</a>) and inputs.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.CausalLMOutputWithPast"
	>CausalLMOutputWithPast</a> or <code>tuple(torch.FloatTensor)</code></p>
	`}}),A=new an({props:{$$slots:{default:[Yn]},$$scope:{ctx:v}}}),K=new Re({props:{anchor:"transformers.CTRLLMHeadModel.forward.example",$$slots:{default:[Pn]},$$scope:{ctx:v}}}),$e=new E({props:{title:"CTRLForSequenceClassification",local:"transformers.CTRLForSequenceClassification",headingTag:"h2"}}),je=new G({props:{name:"class transformers.CTRLForSequenceClassification",anchor:"transformers.CTRLForSequenceClassification",parameters:[{name:"config",val:""}],parametersDescription:[{anchor:"transformers.CTRLForSequenceClassification.config",description:`<strong>config</strong> (<a href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLForSequenceClassification">CTRLForSequenceClassification</a>) —
	Model configuration class with all the parameters of the model. Initializing with a config file does not
	load the weights associated with the model, only the configuration. Check out the
	<a href="/docs/transformers/pr_41116/en/main_classes/model#transformers.PreTrainedModel.from_pretrained">from_pretrained()</a> method to load the model weights.`,name:"config"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L466"}}),Le=new G({props:{name:"forward",anchor:"transformers.CTRLForSequenceClassification.forward",parameters:[{name:"input_ids",val:": torch.LongTensor \| None = None"},{name:"past_key_values",val:": transformers.cache_utils.Cache \| None = None"},{name:"attention_mask",val:": torch.FloatTensor \| None = None"},{name:"token_type_ids",val:": torch.LongTensor \| None = None"},{name:"position_ids",val:": torch.LongTensor \| None = None"},{name:"inputs_embeds",val:": torch.FloatTensor \| None = None"},{name:"labels",val:": torch.LongTensor \| None = None"},{name:"use_cache",val:": bool \| None = None"},{name:"**kwargs",val:": typing_extensions.Unpack[transformers.utils.generic.TransformersKwargs]"}],parametersDescription:[{anchor:"transformers.CTRLForSequenceClassification.forward.input_ids",description:`<strong>input_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of input sequence tokens in the vocabulary. Padding will be ignored by default.</p>
	<p>Indices can be obtained using <a href="/docs/transformers/pr_41116/en/model_doc/auto#transformers.AutoTokenizer">AutoTokenizer</a>. See <a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.encode">PreTrainedTokenizer.encode()</a> and
	<a href="/docs/transformers/pr_41116/en/internal/tokenization_utils#transformers.PreTrainedTokenizerBase.__call__">PreTrainedTokenizer.<strong>call</strong>()</a> for details.</p>
	<p><a href="../glossary#input-ids">What are input IDs?</a>`,name:"input_ids"},{anchor:"transformers.CTRLForSequenceClassification.forward.past_key_values",description:`<strong>past_key_values</strong> (<code>~cache_utils.Cache</code>, <em>optional</em>) —
	Pre-computed hidden-states (key and values in the self-attention blocks and in the cross-attention
	blocks) that can be used to speed up sequential decoding. This typically consists in the <code>past_key_values</code>
	returned by the model at a previous stage of decoding, when <code>use_cache=True</code> or <code>config.use_cache=True</code>.</p>
	<p>Only <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.Cache">Cache</a> instance is allowed as input, see our <a href="https://huggingface.co/docs/transformers/en/kv_cache" rel="nofollow">kv cache guide</a>.
	If no <code>past_key_values</code> are passed, <a href="/docs/transformers/pr_41116/en/internal/generation_utils#transformers.DynamicCache">DynamicCache</a> will be initialized by default.</p>
	<p>The model will output the same cache format that is fed as input.</p>
	<p>If <code>past_key_values</code> are used, the user is expected to input only unprocessed <code>input_ids</code> (those that don’t
	have their past key value states given to this model) of shape <code>(batch_size, unprocessed_length)</code> instead of all <code>input_ids</code>
	of shape <code>(batch_size, sequence_length)</code>.`,name:"past_key_values"},{anchor:"transformers.CTRLForSequenceClassification.forward.attention_mask",description:`<strong>attention_mask</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Mask to avoid performing attention on padding token indices. Mask values selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>1 for tokens that are <strong>not masked</strong>,</li>
	<li>0 for tokens that are <strong>masked</strong>.</li>
	</ul>
	<p><a href="../glossary#attention-mask">What are attention masks?</a>`,name:"attention_mask"},{anchor:"transformers.CTRLForSequenceClassification.forward.token_type_ids",description:`<strong>token_type_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Segment token indices to indicate first and second portions of the inputs. Indices are selected in <code>[0, 1]</code>:</p>
	<ul>
	<li>0 corresponds to a <em>sentence A</em> token,</li>
	<li>1 corresponds to a <em>sentence B</em> token.</li>
	</ul>
	<p><a href="../glossary#token-type-ids">What are token type IDs?</a>`,name:"token_type_ids"},{anchor:"transformers.CTRLForSequenceClassification.forward.position_ids",description:`<strong>position_ids</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size, sequence_length)</code>, <em>optional</em>) —
	Indices of positions of each input sequence tokens in the position embeddings. Selected in the range <code>[0, config.n_positions - 1]</code>.</p>
	<p><a href="../glossary#position-ids">What are position IDs?</a>`,name:"position_ids"},{anchor:"transformers.CTRLForSequenceClassification.forward.inputs_embeds",description:`<strong>inputs_embeds</strong> (<code>torch.FloatTensor</code> of shape <code>(batch_size, sequence_length, hidden_size)</code>, <em>optional</em>) —
	Optionally, instead of passing <code>input_ids</code> you can choose to directly pass an embedded representation. This
	is useful if you want more control over how to convert <code>input_ids</code> indices into associated vectors than the
	model’s internal embedding lookup matrix.`,name:"inputs_embeds"},{anchor:"transformers.CTRLForSequenceClassification.forward.labels",description:`<strong>labels</strong> (<code>torch.LongTensor</code> of shape <code>(batch_size,)</code>, <em>optional</em>) —
	Labels for computing the sequence classification/regression loss. Indices should be in <code>[0, ..., config.num_labels - 1]</code>. If <code>config.num_labels == 1</code> a regression loss is computed (Mean-Square loss), If
	<code>config.num_labels > 1</code> a classification loss is computed (Cross-Entropy).`,name:"labels"},{anchor:"transformers.CTRLForSequenceClassification.forward.use_cache",description:`<strong>use_cache</strong> (<code>bool</code>, <em>optional</em>) —
	If set to <code>True</code>, <code>past_key_values</code> key value states are returned and can be used to speed up decoding (see
	<code>past_key_values</code>).`,name:"use_cache"}],source:"https://github.com/huggingface/transformers/blob/vr_41116/src/transformers/models/ctrl/modeling_ctrl.py#L476",returnDescription:`<script context="module">export const metadata = 'undefined';<\/script>


	<p>A <a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
	>SequenceClassifierOutput</a> or a tuple of
	<code>torch.FloatTensor</code> (if <code>return_dict=False</code> is passed or when <code>config.return_dict=False</code>) comprising various
	elements depending on the configuration (<a
	href="/docs/transformers/pr_41116/en/model_doc/ctrl#transformers.CTRLConfig"
	>CTRLConfig</a>) and inputs.</p>
	`,returnType:`<script context="module">export const metadata = 'undefined';<\/script>


	<p><a
	href="/docs/transformers/pr_41116/en/main_classes/output#transformers.modeling_outputs.SequenceClassifierOutput"
	>SequenceClassifierOutput</a> or <code>tuple(torch.FloatTensor)</code></p>
	`}}),O=new an({props:{$$slots:{default:[Qn]},$$scope:{ctx:v}}}),ee=new Re({props:{anchor:"transformers.CTRLForSequenceClassification.forward.example",$$slots:{default:[Dn]},$$scope:{ctx:v}}}),te=new Re({props:{anchor:"transformers.CTRLForSequenceClassification.forward.example-2",$$slots:{default:[An]},$$scope:{ctx:v}}}),ne=new Re({props:{anchor:"transformers.CTRLForSequenceClassification.forward.example-3",$$slots:{default:[Kn]},$$scope:{ctx:v}}}),se=new Re({props:{anchor:"transformers.CTRLForSequenceClassification.forward.example-4",$$slots:{default:[On]},$$scope:{ctx:v}}}),Je=new Xn({props:{source:"https://github.com/huggingface/transformers/blob/main/docs/source/en/model_doc/ctrl.md"}}),{c(){t=p("meta"),h=r(),o=p("p"),c=r(),M=p("p"),M.innerHTML=n,w=r(),u(oe.$$.fragment),et=r(),Y=p("div"),Y.innerHTML=rn,tt=r(),u(ae.$$.fragment),nt=r(),u(re.$$.fragment),st=r(),le=p("p"),le.innerHTML=ln,ot=r(),ie=p("p"),ie.textContent=cn,at=r(),ce=p("p"),ce.innerHTML=dn,rt=r(),de=p("p"),de.innerHTML=pn,lt=r(),u(pe.$$.fragment),it=r(),me=p("ul"),me.innerHTML=mn,ct=r(),u(he.$$.fragment),dt=r(),ue=p("ul"),ue.innerHTML=hn,pt=r(),u(fe.$$.fragment),mt=r(),x=p("div"),u(ge.$$.fragment),kt=r(),ze=p("p"),ze.innerHTML=un,Ct=r(),Ue=p("p"),Ue.innerHTML=fn,$t=r(),u(P.$$.fragment),ht=r(),u(_e.$$.fragment),ut=r(),z=p("div"),u(be.$$.fragment),jt=r(),Ie=p("p"),Ie.textContent=gn,Lt=r(),Ne=p("p"),Ne.innerHTML=_n,Jt=r(),V=p("div"),u(Te.$$.fragment),Rt=r(),We=p("p"),We.textContent=bn,xt=r(),Ze=p("p"),Ze.textContent=Tn,ft=r(),u(ye.$$.fragment),gt=r(),$=p("div"),u(Me.$$.fragment),zt=r(),Be=p("p"),Be.textContent=yn,Ut=r(),Fe=p("p"),Fe.innerHTML=Mn,It=r(),He=p("p"),He.innerHTML=wn,Nt=r(),J=p("div"),u(we.$$.fragment),Wt=r(),qe=p("p"),qe.innerHTML=vn,Zt=r(),u(Q.$$.fragment),Bt=r(),Ve=p("ul"),Ve.innerHTML=kn,Ft=r(),u(D.$$.fragment),_t=r(),u(ve.$$.fragment),bt=r(),j=p("div"),u(ke.$$.fragment),Ht=r(),Xe=p("p"),Xe.textContent=Cn,qt=r(),Se=p("p"),Se.innerHTML=$n,Vt=r(),Ge=p("p"),Ge.innerHTML=jn,Xt=r(),R=p("div"),u(Ce.$$.fragment),St=r(),Ee=p("p"),Ee.innerHTML=Ln,Gt=r(),u(A.$$.fragment),Et=r(),Ye=p("ul"),Ye.innerHTML=Jn,Yt=r(),u(K.$$.fragment),Tt=r(),u($e.$$.fragment),yt=r(),L=p("div"),u(je.$$.fragment),Pt=r(),Pe=p("p"),Pe.innerHTML=Rn,Qt=r(),Qe=p("p"),Qe.innerHTML=xn,Dt=r(),De=p("p"),De.innerHTML=zn,At=r(),k=p("div"),u(Le.$$.fragment),Kt=r(),Ae=p("p"),Ae.innerHTML=Un,Ot=r(),u(O.$$.fragment),en=r(),Ke=p("ul"),Ke.innerHTML=In,tn=r(),u(ee.$$.fragment),nn=r(),u(te.$$.fragment),sn=r(),u(ne.$$.fragment),on=r(),u(se.$$.fragment),Mt=r(),u(Je.$$.fragment),wt=r(),Oe=p("p"),this.h()},l(e){const s=Hn("svelte-u9bgzb",document.head);t=m(s,"META",{name:!0,content:!0}),s.forEach(a),h=l(e),o=m(e,"P",{}),H(o).forEach(a),c=l(e),M=m(e,"P",{"data-svelte-h":!0}),y(M)!=="svelte-17blit2"&&(M.innerHTML=n),w=l(e),f(oe.$$.fragment,e),et=l(e),Y=m(e,"DIV",{style:!0,"data-svelte-h":!0}),y(Y)!=="svelte-taqiq9"&&(Y.innerHTML=rn),tt=l(e),f(ae.$$.fragment,e),nt=l(e),f(re.$$.fragment,e),st=l(e),le=m(e,"P",{"data-svelte-h":!0}),y(le)!=="svelte-12sh13q"&&(le.innerHTML=ln),ot=l(e),ie=m(e,"P",{"data-svelte-h":!0}),y(ie)!=="svelte-vfdo9a"&&(ie.textContent=cn),at=l(e),ce=m(e,"P",{"data-svelte-h":!0}),y(ce)!=="svelte-f7zzo4"&&(ce.innerHTML=dn),rt=l(e),de=m(e,"P",{"data-svelte-h":!0}),y(de)!=="svelte-3n853r"&&(de.innerHTML=pn),lt=l(e),f(pe.$$.fragment,e),it=l(e),me=m(e,"UL",{"data-svelte-h":!0}),y(me)!=="svelte-j79wce"&&(me.innerHTML=mn),ct=l(e),f(he.$$.fragment,e),dt=l(e),ue=m(e,"UL",{"data-svelte-h":!0}),y(ue)!=="svelte-17u5l9r"&&(ue.innerHTML=hn),pt=l(e),f(fe.$$.fragment,e),mt=l(e),x=m(e,"DIV",{class:!0});var B=H(x);f(ge.$$.fragment,B),kt=l(B),ze=m(B,"P",{"data-svelte-h":!0}),y(ze)!=="svelte-mivcrq"&&(ze.innerHTML=un),Ct=l(B),Ue=m(B,"P",{"data-svelte-h":!0}),y(Ue)!=="svelte-1plkghn"&&(Ue.innerHTML=fn),$t=l(B),f(P.$$.fragment,B),B.forEach(a),ht=l(e),f(_e.$$.fragment,e),ut=l(e),z=m(e,"DIV",{class:!0});var F=H(z);f(be.$$.fragment,F),jt=l(F),Ie=m(F,"P",{"data-svelte-h":!0}),y(Ie)!=="svelte-1ry85wb"&&(Ie.textContent=gn),Lt=l(F),Ne=m(F,"P",{"data-svelte-h":!0}),y(Ne)!=="svelte-1q0lsiy"&&(Ne.innerHTML=_n),Jt=l(F),V=m(F,"DIV",{class:!0});var S=H(V);f(Te.$$.fragment,S),Rt=l(S),We=m(S,"P",{"data-svelte-h":!0}),y(We)!=="svelte-1jvh0qt"&&(We.textContent=bn),xt=l(S),Ze=m(S,"P",{"data-svelte-h":!0}),y(Ze)!=="svelte-1ciymyj"&&(Ze.textContent=Tn),S.forEach(a),F.forEach(a),ft=l(e),f(ye.$$.fragment,e),gt=l(e),$=m(e,"DIV",{class:!0});var U=H($);f(Me.$$.fragment,U),zt=l(U),Be=m(U,"P",{"data-svelte-h":!0}),y(Be)!=="svelte-5qeehh"&&(Be.textContent=yn),Ut=l(U),Fe=m(U,"P",{"data-svelte-h":!0}),y(Fe)!=="svelte-g65tf5"&&(Fe.innerHTML=Mn),It=l(U),He=m(U,"P",{"data-svelte-h":!0}),y(He)!=="svelte-hswkmf"&&(He.innerHTML=wn),Nt=l(U),J=m(U,"DIV",{class:!0});var I=H(J);f(we.$$.fragment,I),Wt=l(I),qe=m(I,"P",{"data-svelte-h":!0}),y(qe)!=="svelte-zp4onz"&&(qe.innerHTML=vn),Zt=l(I),f(Q.$$.fragment,I),Bt=l(I),Ve=m(I,"UL",{"data-svelte-h":!0}),y(Ve)!=="svelte-qfk0cu"&&(Ve.innerHTML=kn),Ft=l(I),f(D.$$.fragment,I),I.forEach(a),U.forEach(a),_t=l(e),f(ve.$$.fragment,e),bt=l(e),j=m(e,"DIV",{class:!0});var N=H(j);f(ke.$$.fragment,N),Ht=l(N),Xe=m(N,"P",{"data-svelte-h":!0}),y(Xe)!=="svelte-ej2g0g"&&(Xe.textContent=Cn),qt=l(N),Se=m(N,"P",{"data-svelte-h":!0}),y(Se)!=="svelte-g65tf5"&&(Se.innerHTML=$n),Vt=l(N),Ge=m(N,"P",{"data-svelte-h":!0}),y(Ge)!=="svelte-hswkmf"&&(Ge.innerHTML=jn),Xt=l(N),R=m(N,"DIV",{class:!0});var W=H(R);f(Ce.$$.fragment,W),St=l(W),Ee=m(W,"P",{"data-svelte-h":!0}),y(Ee)!=="svelte-4e414j"&&(Ee.innerHTML=Ln),Gt=l(W),f(A.$$.fragment,W),Et=l(W),Ye=m(W,"UL",{"data-svelte-h":!0}),y(Ye)!=="svelte-h3h6by"&&(Ye.innerHTML=Jn),Yt=l(W),f(K.$$.fragment,W),W.forEach(a),N.forEach(a),Tt=l(e),f($e.$$.fragment,e),yt=l(e),L=m(e,"DIV",{class:!0});var Z=H(L);f(je.$$.fragment,Z),Pt=l(Z),Pe=m(Z,"P",{"data-svelte-h":!0}),y(Pe)!=="svelte-1q5xfrn"&&(Pe.innerHTML=Rn),Qt=l(Z),Qe=m(Z,"P",{"data-svelte-h":!0}),y(Qe)!=="svelte-g65tf5"&&(Qe.innerHTML=xn),Dt=l(Z),De=m(Z,"P",{"data-svelte-h":!0}),y(De)!=="svelte-hswkmf"&&(De.innerHTML=zn),At=l(Z),k=m(Z,"DIV",{class:!0});var C=H(k);f(Le.$$.fragment,C),Kt=l(C),Ae=m(C,"P",{"data-svelte-h":!0}),y(Ae)!=="svelte-1rojs3z"&&(Ae.innerHTML=Un),Ot=l(C),f(O.$$.fragment,C),en=l(C),Ke=m(C,"UL",{"data-svelte-h":!0}),y(Ke)!=="svelte-7jmpe7"&&(Ke.innerHTML=In),tn=l(C),f(ee.$$.fragment,C),nn=l(C),f(te.$$.fragment,C),sn=l(C),f(ne.$$.fragment,C),on=l(C),f(se.$$.fragment,C),C.forEach(a),Z.forEach(a),Mt=l(e),f(Je.$$.fragment,e),wt=l(e),Oe=m(e,"P",{}),H(Oe).forEach(a),this.h()},h(){q(t,"name","hf:doc:metadata"),q(t,"content",ts),qn(Y,"float","right"),q(x,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(V,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(z,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(J,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q($,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(R,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(j,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(k,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8"),q(L,"class","docstring border-l-2 border-t-2 pl-4 pt-3.5 border-gray-100 rounded-tl-xl mb-6 mt-8")},m(e,s){i(document.head,t),d(e,h,s),d(e,o,s),d(e,c,s),d(e,M,s),d(e,w,s),g(oe,e,s),d(e,et,s),d(e,Y,s),d(e,tt,s),g(ae,e,s),d(e,nt,s),g(re,e,s),d(e,st,s),d(e,le,s),d(e,ot,s),d(e,ie,s),d(e,at,s),d(e,ce,s),d(e,rt,s),d(e,de,s),d(e,lt,s),g(pe,e,s),d(e,it,s),d(e,me,s),d(e,ct,s),g(he,e,s),d(e,dt,s),d(e,ue,s),d(e,pt,s),g(fe,e,s),d(e,mt,s),d(e,x,s),g(ge,x,null),i(x,kt),i(x,ze),i(x,Ct),i(x,Ue),i(x,$t),g(P,x,null),d(e,ht,s),g(_e,e,s),d(e,ut,s),d(e,z,s),g(be,z,null),i(z,jt),i(z,Ie),i(z,Lt),i(z,Ne),i(z,Jt),i(z,V),g(Te,V,null),i(V,Rt),i(V,We),i(V,xt),i(V,Ze),d(e,ft,s),g(ye,e,s),d(e,gt,s),d(e,$,s),g(Me,$,null),i($,zt),i($,Be),i($,Ut),i($,Fe),i($,It),i($,He),i($,Nt),i($,J),g(we,J,null),i(J,Wt),i(J,qe),i(J,Zt),g(Q,J,null),i(J,Bt),i(J,Ve),i(J,Ft),g(D,J,null),d(e,_t,s),g(ve,e,s),d(e,bt,s),d(e,j,s),g(ke,j,null),i(j,Ht),i(j,Xe),i(j,qt),i(j,Se),i(j,Vt),i(j,Ge),i(j,Xt),i(j,R),g(Ce,R,null),i(R,St),i(R,Ee),i(R,Gt),g(A,R,null),i(R,Et),i(R,Ye),i(R,Yt),g(K,R,null),d(e,Tt,s),g($e,e,s),d(e,yt,s),d(e,L,s),g(je,L,null),i(L,Pt),i(L,Pe),i(L,Qt),i(L,Qe),i(L,Dt),i(L,De),i(L,At),i(L,k),g(Le,k,null),i(k,Kt),i(k,Ae),i(k,Ot),g(O,k,null),i(k,en),i(k,Ke),i(k,tn),g(ee,k,null),i(k,nn),g(te,k,null),i(k,sn),g(ne,k,null),i(k,on),g(se,k,null),d(e,Mt,s),g(Je,e,s),d(e,wt,s),d(e,Oe,s),vt=!0},p(e,[s]){const B={};s&2&&(B.$$scope={dirty:s,ctx:e}),P.$set(B);const F={};s&2&&(F.$$scope={dirty:s,ctx:e}),Q.$set(F);const S={};s&2&&(S.$$scope={dirty:s,ctx:e}),D.$set(S);const U={};s&2&&(U.$$scope={dirty:s,ctx:e}),A.$set(U);const I={};s&2&&(I.$$scope={dirty:s,ctx:e}),K.$set(I);const N={};s&2&&(N.$$scope={dirty:s,ctx:e}),O.$set(N);const W={};s&2&&(W.$$scope={dirty:s,ctx:e}),ee.$set(W);const Z={};s&2&&(Z.$$scope={dirty:s,ctx:e}),te.$set(Z);const C={};s&2&&(C.$$scope={dirty:s,ctx:e}),ne.$set(C);const Nn={};s&2&&(Nn.$$scope={dirty:s,ctx:e}),se.$set(Nn)},i(e){vt\|\|(_(oe.$$.fragment,e),_(ae.$$.fragment,e),_(re.$$.fragment,e),_(pe.$$.fragment,e),_(he.$$.fragment,e),_(fe.$$.fragment,e),_(ge.$$.fragment,e),_(P.$$.fragment,e),_(_e.$$.fragment,e),_(be.$$.fragment,e),_(Te.$$.fragment,e),_(ye.$$.fragment,e),_(Me.$$.fragment,e),_(we.$$.fragment,e),_(Q.$$.fragment,e),_(D.$$.fragment,e),_(ve.$$.fragment,e),_(ke.$$.fragment,e),_(Ce.$$.fragment,e),_(A.$$.fragment,e),_(K.$$.fragment,e),_($e.$$.fragment,e),_(je.$$.fragment,e),_(Le.$$.fragment,e),_(O.$$.fragment,e),_(ee.$$.fragment,e),_(te.$$.fragment,e),_(ne.$$.fragment,e),_(se.$$.fragment,e),_(Je.$$.fragment,e),vt=!0)},o(e){b(oe.$$.fragment,e),b(ae.$$.fragment,e),b(re.$$.fragment,e),b(pe.$$.fragment,e),b(he.$$.fragment,e),b(fe.$$.fragment,e),b(ge.$$.fragment,e),b(P.$$.fragment,e),b(_e.$$.fragment,e),b(be.$$.fragment,e),b(Te.$$.fragment,e),b(ye.$$.fragment,e),b(Me.$$.fragment,e),b(we.$$.fragment,e),b(Q.$$.fragment,e),b(D.$$.fragment,e),b(ve.$$.fragment,e),b(ke.$$.fragment,e),b(Ce.$$.fragment,e),b(A.$$.fragment,e),b(K.$$.fragment,e),b($e.$$.fragment,e),b(je.$$.fragment,e),b(Le.$$.fragment,e),b(O.$$.fragment,e),b(ee.$$.fragment,e),b(te.$$.fragment,e),b(ne.$$.fragment,e),b(se.$$.fragment,e),b(Je.$$.fragment,e),vt=!1},d(e){e&&(a(h),a(o),a(c),a(M),a(w),a(et),a(Y),a(tt),a(nt),a(st),a(le),a(ot),a(ie),a(at),a(ce),a(rt),a(de),a(lt),a(it),a(me),a(ct),a(dt),a(ue),a(pt),a(mt),a(x),a(ht),a(ut),a(z),a(ft),a(gt),a($),a(_t),a(bt),a(j),a(Tt),a(yt),a(L),a(Mt),a(wt),a(Oe)),a(t),T(oe,e),T(ae,e),T(re,e),T(pe,e),T(he,e),T(fe,e),T(ge),T(P),T(_e,e),T(be),T(Te),T(ye,e),T(Me),T(we),T(Q),T(D),T(ve,e),T(ke),T(Ce),T(A),T(K),T($e,e),T(je),T(Le),T(O),T(ee),T(te),T(ne),T(se),T(Je,e)}}}const ts='{"title":"CTRL","local":"ctrl","sections":[{"title":"Overview","local":"overview","sections":[],"depth":2},{"title":"Usage tips","local":"usage-tips","sections":[],"depth":2},{"title":"Resources","local":"resources","sections":[],"depth":2},{"title":"CTRLConfig","local":"transformers.CTRLConfig","sections":[],"depth":2},{"title":"CTRLTokenizer","local":"transformers.CTRLTokenizer","sections":[],"depth":2},{"title":"CTRLModel","local":"transformers.CTRLModel","sections":[],"depth":2},{"title":"CTRLLMHeadModel","local":"transformers.CTRLLMHeadModel","sections":[],"depth":2},{"title":"CTRLForSequenceClassification","local":"transformers.CTRLForSequenceClassification","sections":[],"depth":2}],"depth":1}';function ns(v){return Zn(()=>{new URLSearchParams(window.location.search).get("fw")}),[]}class ps extends Bn{constructor(t){super(),Fn(this,t,ns,es,Wn,{})}}export{ps as component};

Xet Storage Details

Size:: 78.8 kB
Xet hash:: 1ee1f54e2ab1405e742ac6de5e2a38d1aeb452fdfb71289ac67152733f830e18

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.