public sealed class InferenceParameter : IMessage<InferenceParameter>, IEquatable<InferenceParameter>, IDeepCloneable<InferenceParameter>, IBufferMessage, IMessageReference documentation and code samples for the Google Cloud Dialogflow v2beta1 API class InferenceParameter.
The parameters of inference.
Implements
IMessageInferenceParameter, IEquatableInferenceParameter, IDeepCloneableInferenceParameter, IBufferMessage, IMessageNamespace
Google.Cloud.Dialogflow.V2Beta1Assembly
Google.Cloud.Dialogflow.V2Beta1.dll
Constructors
InferenceParameter()
public InferenceParameter()InferenceParameter(InferenceParameter)
public InferenceParameter(InferenceParameter other)| Parameter | |
|---|---|
| Name | Description |
other |
InferenceParameter |
Properties
HasMaxOutputTokens
public bool HasMaxOutputTokens { get; }Gets whether the "max_output_tokens" field is set
| Property Value | |
|---|---|
| Type | Description |
bool |
|
HasTemperature
public bool HasTemperature { get; }Gets whether the "temperature" field is set
| Property Value | |
|---|---|
| Type | Description |
bool |
|
HasTopK
public bool HasTopK { get; }Gets whether the "top_k" field is set
| Property Value | |
|---|---|
| Type | Description |
bool |
|
HasTopP
public bool HasTopP { get; }Gets whether the "top_p" field is set
| Property Value | |
|---|---|
| Type | Description |
bool |
|
MaxOutputTokens
public int MaxOutputTokens { get; set; }Optional. Maximum number of the output tokens for the generator.
| Property Value | |
|---|---|
| Type | Description |
int |
|
Temperature
public double Temperature { get; set; }Optional. Controls the randomness of LLM predictions. Low temperature = less random. High temperature = more random. If unset (or 0), uses a default value of 0.
| Property Value | |
|---|---|
| Type | Description |
double |
|
TopK
public int TopK { get; set; }Optional. Top-k changes how the model selects tokens for output. A top-k of 1 means the selected token is the most probable among all tokens in the model's vocabulary (also called greedy decoding), while a top-k of 3 means that the next token is selected from among the 3 most probable tokens (using temperature). For each token selection step, the top K tokens with the highest probabilities are sampled. Then tokens are further filtered based on topP with the final token selected using temperature sampling. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [1, 40], default to 40.
| Property Value | |
|---|---|
| Type | Description |
int |
|
TopP
public double TopP { get; set; }Optional. Top-p changes how the model selects tokens for output. Tokens are selected from most K (see topK parameter) probable to least until the sum of their probabilities equals the top-p value. For example, if tokens A, B, and C have a probability of 0.3, 0.2, and 0.1 and the top-p value is 0.5, then the model will select either A or B as the next token (using temperature) and doesn't consider C. The default top-p value is 0.95. Specify a lower value for less random responses and a higher value for more random responses. Acceptable value is [0.0, 1.0], default to 0.95.
| Property Value | |
|---|---|
| Type | Description |
double |
|