Spaces:
Running
on
CPU Upgrade
Running
on
CPU Upgrade
fix up rusty boi
Browse files- Pipfile +20 -0
- Pipfile.lock +1319 -0
- README.md +1 -1
- app.py +6 -4
- requirements.txt +4 -2
Pipfile
ADDED
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@@ -0,0 +1,20 @@
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[[source]]
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url = "https://pypi.org/simple"
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verify_ssl = true
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name = "pypi"
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[packages]
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torch = "==1.13.1"
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numpy = "==1.24.1"
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pandas = "==1.5.2"
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pillow = "==9.4.0"
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plotly = "==5.11.0"
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psutil = "*"
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streamlit = "*"
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sentencepiece = "*"
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transformers = "*"
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[dev-packages]
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[requires]
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python_version = "3.10"
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Pipfile.lock
ADDED
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@@ -0,0 +1,1319 @@
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| 1303 |
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"sha256:5ac88e7916c3405f0ce38ac2ac82a477fcf4d90dbbeddd04c8193171fc17f7dc",
|
| 1304 |
+
"sha256:890c98789ad884037f059af6ea915ec2d667129d509180c2c590b8009a4c4219"
|
| 1305 |
+
],
|
| 1306 |
+
"markers": "python_version >= '3.8'",
|
| 1307 |
+
"version": "==0.28.1"
|
| 1308 |
+
},
|
| 1309 |
+
"zipp": {
|
| 1310 |
+
"hashes": [
|
| 1311 |
+
"sha256:206f5a15f2af3dbaee80769fb7dc6f249695e940acca08dfb2a4769fe61e538b",
|
| 1312 |
+
"sha256:2884ed22e7d8961de1c9a05142eb69a247f120291bc0206a00a7642f09b5b715"
|
| 1313 |
+
],
|
| 1314 |
+
"markers": "python_version >= '3.8'",
|
| 1315 |
+
"version": "==3.18.1"
|
| 1316 |
+
}
|
| 1317 |
+
},
|
| 1318 |
+
"develop": {}
|
| 1319 |
+
}
|
README.md
CHANGED
|
@@ -4,7 +4,7 @@ emoji: 0️⃣
|
|
| 4 |
colorFrom: indigo
|
| 5 |
colorTo: indigo
|
| 6 |
sdk: streamlit
|
| 7 |
-
sdk_version: 1.
|
| 8 |
app_file: app.py
|
| 9 |
pinned: false
|
| 10 |
license: mit
|
|
|
|
| 4 |
colorFrom: indigo
|
| 5 |
colorTo: indigo
|
| 6 |
sdk: streamlit
|
| 7 |
+
sdk_version: 1.33.0
|
| 8 |
app_file: app.py
|
| 9 |
pinned: false
|
| 10 |
license: mit
|
app.py
CHANGED
|
@@ -28,7 +28,9 @@ Examples were automatically translated and may contain grammatical mistakes.
|
|
| 28 |
Sequences are posed as NLI premises and topic labels are turned into premises, i.e. `business` -> `This text is about business.`""",
|
| 29 |
}
|
| 30 |
|
| 31 |
-
ZSL_DESC = """
|
|
|
|
|
|
|
| 32 |
|
| 33 |
CODE_DESC = """```python
|
| 34 |
from transformers import pipeline
|
|
@@ -50,18 +52,18 @@ model_ids = {
|
|
| 50 |
|
| 51 |
device = 0 if torch.cuda.is_available() else -1
|
| 52 |
|
| 53 |
-
@st.
|
| 54 |
def load_models():
|
| 55 |
return {id: AutoModelForSequenceClassification.from_pretrained(id) for id in model_ids.values()}
|
| 56 |
|
| 57 |
models = load_models()
|
| 58 |
|
| 59 |
|
| 60 |
-
@st.
|
| 61 |
def load_tokenizer(tok_id):
|
| 62 |
return AutoTokenizer.from_pretrained(tok_id)
|
| 63 |
|
| 64 |
-
@st.
|
| 65 |
torch.nn.Parameter: lambda _: None
|
| 66 |
})
|
| 67 |
def get_most_likely(nli_model_id, sequence, labels, hypothesis_template, multi_class):
|
|
|
|
| 28 |
Sequences are posed as NLI premises and topic labels are turned into premises, i.e. `business` -> `This text is about business.`""",
|
| 29 |
}
|
| 30 |
|
| 31 |
+
ZSL_DESC = """*Update 2024/05/02: This app demo'd the use of pre-trained NLI models for zero-shot topic classification right around the 2020 announcement of GPT-3. It was originally released as a standalone app on the Hugging Face servers before the introduction of HF spaces. It is kept here for posterity.*
|
| 32 |
+
|
| 33 |
+
Recently, the NLP science community has begun to pay increasing attention to zero-shot and few-shot applications, such as in the [paper from OpenAI](https://arxiv.org/abs/2005.14165) introducing GPT-3. This demo shows how 🤗 Transformers can be used for zero-shot topic classification, the task of predicting a topic that the model has not been trained on."""
|
| 34 |
|
| 35 |
CODE_DESC = """```python
|
| 36 |
from transformers import pipeline
|
|
|
|
| 52 |
|
| 53 |
device = 0 if torch.cuda.is_available() else -1
|
| 54 |
|
| 55 |
+
@st.cache_resource
|
| 56 |
def load_models():
|
| 57 |
return {id: AutoModelForSequenceClassification.from_pretrained(id) for id in model_ids.values()}
|
| 58 |
|
| 59 |
models = load_models()
|
| 60 |
|
| 61 |
|
| 62 |
+
@st.cache_resource(show_spinner=False)
|
| 63 |
def load_tokenizer(tok_id):
|
| 64 |
return AutoTokenizer.from_pretrained(tok_id)
|
| 65 |
|
| 66 |
+
@st.cache_data(show_spinner=False, hash_funcs={
|
| 67 |
torch.nn.Parameter: lambda _: None
|
| 68 |
})
|
| 69 |
def get_most_likely(nli_model_id, sequence, labels, hypothesis_template, multi_class):
|
requirements.txt
CHANGED
|
@@ -1,7 +1,9 @@
|
|
| 1 |
-
transformers==4.
|
| 2 |
torch==1.13.1
|
| 3 |
numpy==1.24.1
|
| 4 |
pandas==1.5.2
|
| 5 |
Pillow==9.4.0
|
| 6 |
plotly==5.11.0
|
| 7 |
-
psutil
|
|
|
|
|
|
|
|
|
| 1 |
+
transformers==4.40.1
|
| 2 |
torch==1.13.1
|
| 3 |
numpy==1.24.1
|
| 4 |
pandas==1.5.2
|
| 5 |
Pillow==9.4.0
|
| 6 |
plotly==5.11.0
|
| 7 |
+
psutil
|
| 8 |
+
sentencepiece
|
| 9 |
+
streamlit==1.33.0
|